A hybrid of ANN and CLA to predict rainfall

Abstract Accurate prediction of rainfall is extremely important to design of hydrological system and water resources management. In this study, two different techniques, artificial neural networks (ANNs) and Learning-Cellular Automation (CLA) were used to classify rainy days. A wide range of dataset...
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Autor*in:	Mohammadpour, Reza [verfasserIn] Asaie, Zahra [verfasserIn] Shojaeian, Mohammad Reza [verfasserIn] Sadeghzadeh, Mehdi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Learning automata Cellular automata Cellular automata-learn Weather forecast Machine learning Artificial neural network

Übergeordnetes Werk:	Enthalten in: Arabian journal of geosciences - Berlin : Springer, 2008, 11(2018), 18 vom: 12. Sept.
Übergeordnetes Werk:	volume:11 ; year:2018 ; number:18 ; day:12 ; month:09

Links:	Volltext

DOI / URN:	10.1007/s12517-018-3804-z

Katalog-ID:	SPR025968963

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520			\|a Abstract Accurate prediction of rainfall is extremely important to design of hydrological system and water resources management. In this study, two different techniques, artificial neural networks (ANNs) and Learning-Cellular Automation (CLA) were used to classify rainy days. A wide range of dataset (2249 dataset) was collected from Shiraz synoptic stations including temperature, humidity, wind speed, pressure, and daily rainfall. The results indicated that the CLA with R2 = 0.796 and RMSE = 0.431 provides better classification in comparison with ANN (R2 = 0.566 and RMSE = 0.407). Then, daily rainfall is predicted using a novel hybrid method of ANN and CLA (ANN-CLA) that has the ability to automatically remove non-rainy days. To investigate the performance of proposed method, the results of ANN-CLA were compared with one of the most common neural network method, namely Feed Forward Back Propagation (FFBP) with two learning function of LM (FFBP-LM) and BFGS (FFBP-BFGS). The result indicated that hybrid of ANN-CLA successfully predicts daily rainfall (R2 = 0.881 and RMSE = 0.202) in comparison with FFBP-LM (R2 = 0.839 and RMSE = 0.222) and FFBP-BFGS (R2 = 0.698 and RMSE = 0.246). A sensitivity analysis is performed on the data to determine the effect of input parameters on the daily rainfall. This analysis indicates that the average wind speed is the main parameter for prediction of rainfall. This research highlights that the presented techniques can be successfully employed as a robust method for prediction of problems related to water resources, hydrology, and water sciences.
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700	1		\|a Sadeghzadeh, Mehdi \|e verfasserin \|4 aut
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publishDate	2018
allfields	10.1007/s12517-018-3804-z doi (DE-627)SPR025968963 (SPR)s12517-018-3804-z-e DE-627 ger DE-627 rakwb eng 550 ASE Mohammadpour, Reza verfasserin aut A hybrid of ANN and CLA to predict rainfall 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate prediction of rainfall is extremely important to design of hydrological system and water resources management. In this study, two different techniques, artificial neural networks (ANNs) and Learning-Cellular Automation (CLA) were used to classify rainy days. A wide range of dataset (2249 dataset) was collected from Shiraz synoptic stations including temperature, humidity, wind speed, pressure, and daily rainfall. The results indicated that the CLA with R2 = 0.796 and RMSE = 0.431 provides better classification in comparison with ANN (R2 = 0.566 and RMSE = 0.407). Then, daily rainfall is predicted using a novel hybrid method of ANN and CLA (ANN-CLA) that has the ability to automatically remove non-rainy days. To investigate the performance of proposed method, the results of ANN-CLA were compared with one of the most common neural network method, namely Feed Forward Back Propagation (FFBP) with two learning function of LM (FFBP-LM) and BFGS (FFBP-BFGS). The result indicated that hybrid of ANN-CLA successfully predicts daily rainfall (R2 = 0.881 and RMSE = 0.202) in comparison with FFBP-LM (R2 = 0.839 and RMSE = 0.222) and FFBP-BFGS (R2 = 0.698 and RMSE = 0.246). A sensitivity analysis is performed on the data to determine the effect of input parameters on the daily rainfall. This analysis indicates that the average wind speed is the main parameter for prediction of rainfall. This research highlights that the presented techniques can be successfully employed as a robust method for prediction of problems related to water resources, hydrology, and water sciences. Learning automata (dpeaa)DE-He213 Cellular automata (dpeaa)DE-He213 Cellular automata-learn (dpeaa)DE-He213 Weather forecast (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Asaie, Zahra verfasserin aut Shojaeian, Mohammad Reza verfasserin aut Sadeghzadeh, Mehdi verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 11(2018), 18 vom: 12. Sept. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:11 year:2018 number:18 day:12 month:09 https://dx.doi.org/10.1007/s12517-018-3804-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2018 18 12 09
spelling	10.1007/s12517-018-3804-z doi (DE-627)SPR025968963 (SPR)s12517-018-3804-z-e DE-627 ger DE-627 rakwb eng 550 ASE Mohammadpour, Reza verfasserin aut A hybrid of ANN and CLA to predict rainfall 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate prediction of rainfall is extremely important to design of hydrological system and water resources management. In this study, two different techniques, artificial neural networks (ANNs) and Learning-Cellular Automation (CLA) were used to classify rainy days. A wide range of dataset (2249 dataset) was collected from Shiraz synoptic stations including temperature, humidity, wind speed, pressure, and daily rainfall. The results indicated that the CLA with R2 = 0.796 and RMSE = 0.431 provides better classification in comparison with ANN (R2 = 0.566 and RMSE = 0.407). Then, daily rainfall is predicted using a novel hybrid method of ANN and CLA (ANN-CLA) that has the ability to automatically remove non-rainy days. To investigate the performance of proposed method, the results of ANN-CLA were compared with one of the most common neural network method, namely Feed Forward Back Propagation (FFBP) with two learning function of LM (FFBP-LM) and BFGS (FFBP-BFGS). The result indicated that hybrid of ANN-CLA successfully predicts daily rainfall (R2 = 0.881 and RMSE = 0.202) in comparison with FFBP-LM (R2 = 0.839 and RMSE = 0.222) and FFBP-BFGS (R2 = 0.698 and RMSE = 0.246). A sensitivity analysis is performed on the data to determine the effect of input parameters on the daily rainfall. This analysis indicates that the average wind speed is the main parameter for prediction of rainfall. This research highlights that the presented techniques can be successfully employed as a robust method for prediction of problems related to water resources, hydrology, and water sciences. Learning automata (dpeaa)DE-He213 Cellular automata (dpeaa)DE-He213 Cellular automata-learn (dpeaa)DE-He213 Weather forecast (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Asaie, Zahra verfasserin aut Shojaeian, Mohammad Reza verfasserin aut Sadeghzadeh, Mehdi verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 11(2018), 18 vom: 12. Sept. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:11 year:2018 number:18 day:12 month:09 https://dx.doi.org/10.1007/s12517-018-3804-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2018 18 12 09
allfields_unstemmed	10.1007/s12517-018-3804-z doi (DE-627)SPR025968963 (SPR)s12517-018-3804-z-e DE-627 ger DE-627 rakwb eng 550 ASE Mohammadpour, Reza verfasserin aut A hybrid of ANN and CLA to predict rainfall 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate prediction of rainfall is extremely important to design of hydrological system and water resources management. In this study, two different techniques, artificial neural networks (ANNs) and Learning-Cellular Automation (CLA) were used to classify rainy days. A wide range of dataset (2249 dataset) was collected from Shiraz synoptic stations including temperature, humidity, wind speed, pressure, and daily rainfall. The results indicated that the CLA with R2 = 0.796 and RMSE = 0.431 provides better classification in comparison with ANN (R2 = 0.566 and RMSE = 0.407). Then, daily rainfall is predicted using a novel hybrid method of ANN and CLA (ANN-CLA) that has the ability to automatically remove non-rainy days. To investigate the performance of proposed method, the results of ANN-CLA were compared with one of the most common neural network method, namely Feed Forward Back Propagation (FFBP) with two learning function of LM (FFBP-LM) and BFGS (FFBP-BFGS). The result indicated that hybrid of ANN-CLA successfully predicts daily rainfall (R2 = 0.881 and RMSE = 0.202) in comparison with FFBP-LM (R2 = 0.839 and RMSE = 0.222) and FFBP-BFGS (R2 = 0.698 and RMSE = 0.246). A sensitivity analysis is performed on the data to determine the effect of input parameters on the daily rainfall. This analysis indicates that the average wind speed is the main parameter for prediction of rainfall. This research highlights that the presented techniques can be successfully employed as a robust method for prediction of problems related to water resources, hydrology, and water sciences. Learning automata (dpeaa)DE-He213 Cellular automata (dpeaa)DE-He213 Cellular automata-learn (dpeaa)DE-He213 Weather forecast (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Asaie, Zahra verfasserin aut Shojaeian, Mohammad Reza verfasserin aut Sadeghzadeh, Mehdi verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 11(2018), 18 vom: 12. Sept. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:11 year:2018 number:18 day:12 month:09 https://dx.doi.org/10.1007/s12517-018-3804-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2018 18 12 09
allfieldsGer	10.1007/s12517-018-3804-z doi (DE-627)SPR025968963 (SPR)s12517-018-3804-z-e DE-627 ger DE-627 rakwb eng 550 ASE Mohammadpour, Reza verfasserin aut A hybrid of ANN and CLA to predict rainfall 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate prediction of rainfall is extremely important to design of hydrological system and water resources management. In this study, two different techniques, artificial neural networks (ANNs) and Learning-Cellular Automation (CLA) were used to classify rainy days. A wide range of dataset (2249 dataset) was collected from Shiraz synoptic stations including temperature, humidity, wind speed, pressure, and daily rainfall. The results indicated that the CLA with R2 = 0.796 and RMSE = 0.431 provides better classification in comparison with ANN (R2 = 0.566 and RMSE = 0.407). Then, daily rainfall is predicted using a novel hybrid method of ANN and CLA (ANN-CLA) that has the ability to automatically remove non-rainy days. To investigate the performance of proposed method, the results of ANN-CLA were compared with one of the most common neural network method, namely Feed Forward Back Propagation (FFBP) with two learning function of LM (FFBP-LM) and BFGS (FFBP-BFGS). The result indicated that hybrid of ANN-CLA successfully predicts daily rainfall (R2 = 0.881 and RMSE = 0.202) in comparison with FFBP-LM (R2 = 0.839 and RMSE = 0.222) and FFBP-BFGS (R2 = 0.698 and RMSE = 0.246). A sensitivity analysis is performed on the data to determine the effect of input parameters on the daily rainfall. This analysis indicates that the average wind speed is the main parameter for prediction of rainfall. This research highlights that the presented techniques can be successfully employed as a robust method for prediction of problems related to water resources, hydrology, and water sciences. Learning automata (dpeaa)DE-He213 Cellular automata (dpeaa)DE-He213 Cellular automata-learn (dpeaa)DE-He213 Weather forecast (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Asaie, Zahra verfasserin aut Shojaeian, Mohammad Reza verfasserin aut Sadeghzadeh, Mehdi verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 11(2018), 18 vom: 12. Sept. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:11 year:2018 number:18 day:12 month:09 https://dx.doi.org/10.1007/s12517-018-3804-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2018 18 12 09
allfieldsSound	10.1007/s12517-018-3804-z doi (DE-627)SPR025968963 (SPR)s12517-018-3804-z-e DE-627 ger DE-627 rakwb eng 550 ASE Mohammadpour, Reza verfasserin aut A hybrid of ANN and CLA to predict rainfall 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Accurate prediction of rainfall is extremely important to design of hydrological system and water resources management. In this study, two different techniques, artificial neural networks (ANNs) and Learning-Cellular Automation (CLA) were used to classify rainy days. A wide range of dataset (2249 dataset) was collected from Shiraz synoptic stations including temperature, humidity, wind speed, pressure, and daily rainfall. The results indicated that the CLA with R2 = 0.796 and RMSE = 0.431 provides better classification in comparison with ANN (R2 = 0.566 and RMSE = 0.407). Then, daily rainfall is predicted using a novel hybrid method of ANN and CLA (ANN-CLA) that has the ability to automatically remove non-rainy days. To investigate the performance of proposed method, the results of ANN-CLA were compared with one of the most common neural network method, namely Feed Forward Back Propagation (FFBP) with two learning function of LM (FFBP-LM) and BFGS (FFBP-BFGS). The result indicated that hybrid of ANN-CLA successfully predicts daily rainfall (R2 = 0.881 and RMSE = 0.202) in comparison with FFBP-LM (R2 = 0.839 and RMSE = 0.222) and FFBP-BFGS (R2 = 0.698 and RMSE = 0.246). A sensitivity analysis is performed on the data to determine the effect of input parameters on the daily rainfall. This analysis indicates that the average wind speed is the main parameter for prediction of rainfall. This research highlights that the presented techniques can be successfully employed as a robust method for prediction of problems related to water resources, hydrology, and water sciences. Learning automata (dpeaa)DE-He213 Cellular automata (dpeaa)DE-He213 Cellular automata-learn (dpeaa)DE-He213 Weather forecast (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Asaie, Zahra verfasserin aut Shojaeian, Mohammad Reza verfasserin aut Sadeghzadeh, Mehdi verfasserin aut Enthalten in Arabian journal of geosciences Berlin : Springer, 2008 11(2018), 18 vom: 12. Sept. (DE-627)572421877 (DE-600)2438771-X 1866-7538 nnns volume:11 year:2018 number:18 day:12 month:09 https://dx.doi.org/10.1007/s12517-018-3804-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2018 18 12 09
language	English
source	Enthalten in Arabian journal of geosciences 11(2018), 18 vom: 12. Sept. volume:11 year:2018 number:18 day:12 month:09
sourceStr	Enthalten in Arabian journal of geosciences 11(2018), 18 vom: 12. Sept. volume:11 year:2018 number:18 day:12 month:09
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Learning automata Cellular automata Cellular automata-learn Weather forecast Machine learning Artificial neural network
dewey-raw	550
isfreeaccess_bool	false
container_title	Arabian journal of geosciences
authorswithroles_txt_mv	Mohammadpour, Reza @@aut@@ Asaie, Zahra @@aut@@ Shojaeian, Mohammad Reza @@aut@@ Sadeghzadeh, Mehdi @@aut@@
publishDateDaySort_date	2018-09-12T00:00:00Z
hierarchy_top_id	572421877
dewey-sort	3550
id	SPR025968963
language_de	englisch
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author	Mohammadpour, Reza
spellingShingle	Mohammadpour, Reza ddc 550 misc Learning automata misc Cellular automata misc Cellular automata-learn misc Weather forecast misc Machine learning misc Artificial neural network A hybrid of ANN and CLA to predict rainfall
authorStr	Mohammadpour, Reza
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topic_title	550 ASE A hybrid of ANN and CLA to predict rainfall Learning automata (dpeaa)DE-He213 Cellular automata (dpeaa)DE-He213 Cellular automata-learn (dpeaa)DE-He213 Weather forecast (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213
topic	ddc 550 misc Learning automata misc Cellular automata misc Cellular automata-learn misc Weather forecast misc Machine learning misc Artificial neural network
topic_unstemmed	ddc 550 misc Learning automata misc Cellular automata misc Cellular automata-learn misc Weather forecast misc Machine learning misc Artificial neural network
topic_browse	ddc 550 misc Learning automata misc Cellular automata misc Cellular automata-learn misc Weather forecast misc Machine learning misc Artificial neural network
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title	A hybrid of ANN and CLA to predict rainfall
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title_full	A hybrid of ANN and CLA to predict rainfall
author_sort	Mohammadpour, Reza
journal	Arabian journal of geosciences
journalStr	Arabian journal of geosciences
lang_code	eng
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author_browse	Mohammadpour, Reza Asaie, Zahra Shojaeian, Mohammad Reza Sadeghzadeh, Mehdi
container_volume	11
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title_sort	hybrid of ann and cla to predict rainfall
title_auth	A hybrid of ANN and CLA to predict rainfall
abstract	Abstract Accurate prediction of rainfall is extremely important to design of hydrological system and water resources management. In this study, two different techniques, artificial neural networks (ANNs) and Learning-Cellular Automation (CLA) were used to classify rainy days. A wide range of dataset (2249 dataset) was collected from Shiraz synoptic stations including temperature, humidity, wind speed, pressure, and daily rainfall. The results indicated that the CLA with R2 = 0.796 and RMSE = 0.431 provides better classification in comparison with ANN (R2 = 0.566 and RMSE = 0.407). Then, daily rainfall is predicted using a novel hybrid method of ANN and CLA (ANN-CLA) that has the ability to automatically remove non-rainy days. To investigate the performance of proposed method, the results of ANN-CLA were compared with one of the most common neural network method, namely Feed Forward Back Propagation (FFBP) with two learning function of LM (FFBP-LM) and BFGS (FFBP-BFGS). The result indicated that hybrid of ANN-CLA successfully predicts daily rainfall (R2 = 0.881 and RMSE = 0.202) in comparison with FFBP-LM (R2 = 0.839 and RMSE = 0.222) and FFBP-BFGS (R2 = 0.698 and RMSE = 0.246). A sensitivity analysis is performed on the data to determine the effect of input parameters on the daily rainfall. This analysis indicates that the average wind speed is the main parameter for prediction of rainfall. This research highlights that the presented techniques can be successfully employed as a robust method for prediction of problems related to water resources, hydrology, and water sciences.
abstractGer	Abstract Accurate prediction of rainfall is extremely important to design of hydrological system and water resources management. In this study, two different techniques, artificial neural networks (ANNs) and Learning-Cellular Automation (CLA) were used to classify rainy days. A wide range of dataset (2249 dataset) was collected from Shiraz synoptic stations including temperature, humidity, wind speed, pressure, and daily rainfall. The results indicated that the CLA with R2 = 0.796 and RMSE = 0.431 provides better classification in comparison with ANN (R2 = 0.566 and RMSE = 0.407). Then, daily rainfall is predicted using a novel hybrid method of ANN and CLA (ANN-CLA) that has the ability to automatically remove non-rainy days. To investigate the performance of proposed method, the results of ANN-CLA were compared with one of the most common neural network method, namely Feed Forward Back Propagation (FFBP) with two learning function of LM (FFBP-LM) and BFGS (FFBP-BFGS). The result indicated that hybrid of ANN-CLA successfully predicts daily rainfall (R2 = 0.881 and RMSE = 0.202) in comparison with FFBP-LM (R2 = 0.839 and RMSE = 0.222) and FFBP-BFGS (R2 = 0.698 and RMSE = 0.246). A sensitivity analysis is performed on the data to determine the effect of input parameters on the daily rainfall. This analysis indicates that the average wind speed is the main parameter for prediction of rainfall. This research highlights that the presented techniques can be successfully employed as a robust method for prediction of problems related to water resources, hydrology, and water sciences.
abstract_unstemmed	Abstract Accurate prediction of rainfall is extremely important to design of hydrological system and water resources management. In this study, two different techniques, artificial neural networks (ANNs) and Learning-Cellular Automation (CLA) were used to classify rainy days. A wide range of dataset (2249 dataset) was collected from Shiraz synoptic stations including temperature, humidity, wind speed, pressure, and daily rainfall. The results indicated that the CLA with R2 = 0.796 and RMSE = 0.431 provides better classification in comparison with ANN (R2 = 0.566 and RMSE = 0.407). Then, daily rainfall is predicted using a novel hybrid method of ANN and CLA (ANN-CLA) that has the ability to automatically remove non-rainy days. To investigate the performance of proposed method, the results of ANN-CLA were compared with one of the most common neural network method, namely Feed Forward Back Propagation (FFBP) with two learning function of LM (FFBP-LM) and BFGS (FFBP-BFGS). The result indicated that hybrid of ANN-CLA successfully predicts daily rainfall (R2 = 0.881 and RMSE = 0.202) in comparison with FFBP-LM (R2 = 0.839 and RMSE = 0.222) and FFBP-BFGS (R2 = 0.698 and RMSE = 0.246). A sensitivity analysis is performed on the data to determine the effect of input parameters on the daily rainfall. This analysis indicates that the average wind speed is the main parameter for prediction of rainfall. This research highlights that the presented techniques can be successfully employed as a robust method for prediction of problems related to water resources, hydrology, and water sciences.
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title_short	A hybrid of ANN and CLA to predict rainfall
url	https://dx.doi.org/10.1007/s12517-018-3804-z
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author2	Asaie, Zahra Shojaeian, Mohammad Reza Sadeghzadeh, Mehdi
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doi_str	10.1007/s12517-018-3804-z
up_date	2024-07-03T18:03:54.741Z
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A hybrid of ANN and CLA to predict rainfall

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