Prediction of water quality indexes with ensemble learners: Bagging and boosting
One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are fre...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Aldrees, Ali [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
|---|
| Schlagwörter: |
|---|
| Umfang: |
18 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Direct visualisation of thrombi for diagnosis of tissue valve thrombosis - Karthikeyan, Ganesan ELSEVIER, 2018, Amsterdam |
|---|---|
| Übergeordnetes Werk: |
volume:168 ; year:2022 ; pages:344-361 ; extent:18 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.psep.2022.10.005 |
|---|
| Katalog-ID: |
ELV059574690 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV059574690 | ||
| 003 | DE-627 | ||
| 005 | 20230626053254.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 221219s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.psep.2022.10.005 |2 doi | |
| 028 | 5 | 2 | |a /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001986.pica |
| 035 | |a (DE-627)ELV059574690 | ||
| 035 | |a (ELSEVIER)S0957-5820(22)00859-X | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Aldrees, Ali |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Prediction of water quality indexes with ensemble learners: Bagging and boosting |
| 264 | 1 | |c 2022transfer abstract | |
| 300 | |a 18 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models. | ||
| 520 | |a One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models. | ||
| 650 | 7 | |a Electrical conductivity |2 Elsevier | |
| 650 | 7 | |a Total dissolved solids |2 Elsevier | |
| 700 | 1 | |a Awan, Hamad Hassan |4 oth | |
| 700 | 1 | |a Javed, Muhammad Faisal |4 oth | |
| 700 | 1 | |a Mohamed, Abdeliazim Mustafa |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Karthikeyan, Ganesan ELSEVIER |t Direct visualisation of thrombi for diagnosis of tissue valve thrombosis |d 2018 |g Amsterdam |w (DE-627)ELV000231266 |
| 773 | 1 | 8 | |g volume:168 |g year:2022 |g pages:344-361 |g extent:18 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.psep.2022.10.005 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 951 | |a AR | ||
| 952 | |d 168 |j 2022 |h 344-361 |g 18 | ||
| author_variant |
a a aa |
|---|---|
| matchkey_str |
aldreesaliawanhamadhassanjavedmuhammadfa:2022----:rdcinfaeqaiyneewtesmllanr |
| hierarchy_sort_str |
2022transfer abstract |
| publishDate |
2022 |
| allfields |
10.1016/j.psep.2022.10.005 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001986.pica (DE-627)ELV059574690 (ELSEVIER)S0957-5820(22)00859-X DE-627 ger DE-627 rakwb eng Aldrees, Ali verfasserin aut Prediction of water quality indexes with ensemble learners: Bagging and boosting 2022transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models. One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models. Electrical conductivity Elsevier Total dissolved solids Elsevier Awan, Hamad Hassan oth Javed, Muhammad Faisal oth Mohamed, Abdeliazim Mustafa oth Enthalten in Elsevier Karthikeyan, Ganesan ELSEVIER Direct visualisation of thrombi for diagnosis of tissue valve thrombosis 2018 Amsterdam (DE-627)ELV000231266 volume:168 year:2022 pages:344-361 extent:18 https://doi.org/10.1016/j.psep.2022.10.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 168 2022 344-361 18 |
| spelling |
10.1016/j.psep.2022.10.005 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001986.pica (DE-627)ELV059574690 (ELSEVIER)S0957-5820(22)00859-X DE-627 ger DE-627 rakwb eng Aldrees, Ali verfasserin aut Prediction of water quality indexes with ensemble learners: Bagging and boosting 2022transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models. One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models. Electrical conductivity Elsevier Total dissolved solids Elsevier Awan, Hamad Hassan oth Javed, Muhammad Faisal oth Mohamed, Abdeliazim Mustafa oth Enthalten in Elsevier Karthikeyan, Ganesan ELSEVIER Direct visualisation of thrombi for diagnosis of tissue valve thrombosis 2018 Amsterdam (DE-627)ELV000231266 volume:168 year:2022 pages:344-361 extent:18 https://doi.org/10.1016/j.psep.2022.10.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 168 2022 344-361 18 |
| allfields_unstemmed |
10.1016/j.psep.2022.10.005 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001986.pica (DE-627)ELV059574690 (ELSEVIER)S0957-5820(22)00859-X DE-627 ger DE-627 rakwb eng Aldrees, Ali verfasserin aut Prediction of water quality indexes with ensemble learners: Bagging and boosting 2022transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models. One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models. Electrical conductivity Elsevier Total dissolved solids Elsevier Awan, Hamad Hassan oth Javed, Muhammad Faisal oth Mohamed, Abdeliazim Mustafa oth Enthalten in Elsevier Karthikeyan, Ganesan ELSEVIER Direct visualisation of thrombi for diagnosis of tissue valve thrombosis 2018 Amsterdam (DE-627)ELV000231266 volume:168 year:2022 pages:344-361 extent:18 https://doi.org/10.1016/j.psep.2022.10.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 168 2022 344-361 18 |
| allfieldsGer |
10.1016/j.psep.2022.10.005 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001986.pica (DE-627)ELV059574690 (ELSEVIER)S0957-5820(22)00859-X DE-627 ger DE-627 rakwb eng Aldrees, Ali verfasserin aut Prediction of water quality indexes with ensemble learners: Bagging and boosting 2022transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models. One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models. Electrical conductivity Elsevier Total dissolved solids Elsevier Awan, Hamad Hassan oth Javed, Muhammad Faisal oth Mohamed, Abdeliazim Mustafa oth Enthalten in Elsevier Karthikeyan, Ganesan ELSEVIER Direct visualisation of thrombi for diagnosis of tissue valve thrombosis 2018 Amsterdam (DE-627)ELV000231266 volume:168 year:2022 pages:344-361 extent:18 https://doi.org/10.1016/j.psep.2022.10.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 168 2022 344-361 18 |
| allfieldsSound |
10.1016/j.psep.2022.10.005 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001986.pica (DE-627)ELV059574690 (ELSEVIER)S0957-5820(22)00859-X DE-627 ger DE-627 rakwb eng Aldrees, Ali verfasserin aut Prediction of water quality indexes with ensemble learners: Bagging and boosting 2022transfer abstract 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models. One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models. Electrical conductivity Elsevier Total dissolved solids Elsevier Awan, Hamad Hassan oth Javed, Muhammad Faisal oth Mohamed, Abdeliazim Mustafa oth Enthalten in Elsevier Karthikeyan, Ganesan ELSEVIER Direct visualisation of thrombi for diagnosis of tissue valve thrombosis 2018 Amsterdam (DE-627)ELV000231266 volume:168 year:2022 pages:344-361 extent:18 https://doi.org/10.1016/j.psep.2022.10.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 168 2022 344-361 18 |
| language |
English |
| source |
Enthalten in Direct visualisation of thrombi for diagnosis of tissue valve thrombosis Amsterdam volume:168 year:2022 pages:344-361 extent:18 |
| sourceStr |
Enthalten in Direct visualisation of thrombi for diagnosis of tissue valve thrombosis Amsterdam volume:168 year:2022 pages:344-361 extent:18 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Electrical conductivity Total dissolved solids |
| isfreeaccess_bool |
false |
| container_title |
Direct visualisation of thrombi for diagnosis of tissue valve thrombosis |
| authorswithroles_txt_mv |
Aldrees, Ali @@aut@@ Awan, Hamad Hassan @@oth@@ Javed, Muhammad Faisal @@oth@@ Mohamed, Abdeliazim Mustafa @@oth@@ |
| publishDateDaySort_date |
2022-01-01T00:00:00Z |
| hierarchy_top_id |
ELV000231266 |
| id |
ELV059574690 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV059574690</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626053254.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221219s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.psep.2022.10.005</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001986.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV059574690</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0957-5820(22)00859-X</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Aldrees, Ali</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Prediction of water quality indexes with ensemble learners: Bagging and boosting</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">18</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Electrical conductivity</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Total dissolved solids</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Awan, Hamad Hassan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Javed, Muhammad Faisal</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mohamed, Abdeliazim Mustafa</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Karthikeyan, Ganesan ELSEVIER</subfield><subfield code="t">Direct visualisation of thrombi for diagnosis of tissue valve thrombosis</subfield><subfield code="d">2018</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV000231266</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:168</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:344-361</subfield><subfield code="g">extent:18</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.psep.2022.10.005</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">168</subfield><subfield code="j">2022</subfield><subfield code="h">344-361</subfield><subfield code="g">18</subfield></datafield></record></collection>
|
| author |
Aldrees, Ali |
| spellingShingle |
Aldrees, Ali Elsevier Electrical conductivity Elsevier Total dissolved solids Prediction of water quality indexes with ensemble learners: Bagging and boosting |
| authorStr |
Aldrees, Ali |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV000231266 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
Prediction of water quality indexes with ensemble learners: Bagging and boosting Electrical conductivity Elsevier Total dissolved solids Elsevier |
| topic |
Elsevier Electrical conductivity Elsevier Total dissolved solids |
| topic_unstemmed |
Elsevier Electrical conductivity Elsevier Total dissolved solids |
| topic_browse |
Elsevier Electrical conductivity Elsevier Total dissolved solids |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
h h a hh hha m f j mf mfj a m m am amm |
| hierarchy_parent_title |
Direct visualisation of thrombi for diagnosis of tissue valve thrombosis |
| hierarchy_parent_id |
ELV000231266 |
| hierarchy_top_title |
Direct visualisation of thrombi for diagnosis of tissue valve thrombosis |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV000231266 |
| title |
Prediction of water quality indexes with ensemble learners: Bagging and boosting |
| ctrlnum |
(DE-627)ELV059574690 (ELSEVIER)S0957-5820(22)00859-X |
| title_full |
Prediction of water quality indexes with ensemble learners: Bagging and boosting |
| author_sort |
Aldrees, Ali |
| journal |
Direct visualisation of thrombi for diagnosis of tissue valve thrombosis |
| journalStr |
Direct visualisation of thrombi for diagnosis of tissue valve thrombosis |
| lang_code |
eng |
| isOA_bool |
false |
| recordtype |
marc |
| publishDateSort |
2022 |
| contenttype_str_mv |
zzz |
| container_start_page |
344 |
| author_browse |
Aldrees, Ali |
| container_volume |
168 |
| physical |
18 |
| format_se |
Elektronische Aufsätze |
| author-letter |
Aldrees, Ali |
| doi_str_mv |
10.1016/j.psep.2022.10.005 |
| title_sort |
prediction of water quality indexes with ensemble learners: bagging and boosting |
| title_auth |
Prediction of water quality indexes with ensemble learners: Bagging and boosting |
| abstract |
One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models. |
| abstractGer |
One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models. |
| abstract_unstemmed |
One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U |
| title_short |
Prediction of water quality indexes with ensemble learners: Bagging and boosting |
| url |
https://doi.org/10.1016/j.psep.2022.10.005 |
| remote_bool |
true |
| author2 |
Awan, Hamad Hassan Javed, Muhammad Faisal Mohamed, Abdeliazim Mustafa |
| author2Str |
Awan, Hamad Hassan Javed, Muhammad Faisal Mohamed, Abdeliazim Mustafa |
| ppnlink |
ELV000231266 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth |
| doi_str |
10.1016/j.psep.2022.10.005 |
| up_date |
2024-07-06T22:24:38.836Z |
| _version_ |
1803870198916186112 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV059574690</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626053254.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221219s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.psep.2022.10.005</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001986.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV059574690</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0957-5820(22)00859-X</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Aldrees, Ali</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Prediction of water quality indexes with ensemble learners: Bagging and boosting</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">18</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">One of the most crucial jobs to improve water resources management plans is the assessment of river water quality. A water quality index (WQI) takes multiple water quality factors into account simultaneously. Traditionally, derivations of sub-indices for WQI computations take a long time and are frequently rife with errors. The adoption of reliable and effective machine learning (ML) algorithms has become essential for predicting the WQI of such a matrix. This study predicts WQI, i.e., total dissolved solids (TDS) and electrical conductivity (EC), using ML techniques, including individual learners in conjunction with ensemble learners (bagging and boosting). Anaconda (Python) is utilized to accomplish this. Weak ensemble learners are incorporated to create a strong ensemble learner using an adaptive boosting technique, ensemble learner bagging, and random forest (RF) as a modified bagging method. The ensemble learners are employed on weak or individual learners, which include multi-layer perceptron neural networks (MLPNN), support vector machines (SVM), and decision trees (DT) using regression. The data comprises 372 data readings collected on a monthly basis and eight characteristics to forecast the results. Twenty boosting and bagging sub-models were trained on the collected data readings, and they were then optimized to produce the highest R2. Additionally, K-Fold cross-validation with R2, RMSE, and MAE is used to validate the testing data. Furthermore, a statistical model performance index is used to compare ensemble models to individual ones (e.g., MAE, RMSE, NSE, MSE, and RMLSE). The outcome revealed that using the boosting and bagging learners improves the response of individual models. RF, with an R2 of 0.958 and 0.964 (TDS and EC), and DT, with bagging having an R2 of 0.954 and 0.961 (TDS and EC), reported the fewest errors and provided the most reliable and precise performance of the models. In general, the ML ensemble model would improve the performance of models.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Electrical conductivity</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Total dissolved solids</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Awan, Hamad Hassan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Javed, Muhammad Faisal</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mohamed, Abdeliazim Mustafa</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Karthikeyan, Ganesan ELSEVIER</subfield><subfield code="t">Direct visualisation of thrombi for diagnosis of tissue valve thrombosis</subfield><subfield code="d">2018</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV000231266</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:168</subfield><subfield code="g">year:2022</subfield><subfield code="g">pages:344-361</subfield><subfield code="g">extent:18</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.psep.2022.10.005</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">168</subfield><subfield code="j">2022</subfield><subfield code="h">344-361</subfield><subfield code="g">18</subfield></datafield></record></collection>
|
| score |
7.402815 |