Effect of Mississippi River discharge and local hydrological variables on salinity of nearby estuaries using a machine learning algorithm
Estuarine water salinity is important to coastal ecosystems and the economy. To examine the potential effect of the Mississippi River freshwater discharge on water salinity of other estuaries, we used the machine learning Cubist algorithm to model salinity of three estuaries (Apalachicola Bay, Weeks...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhou, Jiahua [verfasserIn] Deitch, Matthew J. [verfasserIn] Grunwald, Sabine [verfasserIn] Screaton, Elizabeth J. [verfasserIn] Olabarrieta, Maitane [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Estuarine, coastal and shelf science - London : Academic Press, 1981, 263 |
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| Übergeordnetes Werk: |
volume:263 |
| DOI / URN: |
10.1016/j.ecss.2021.107628 |
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| Katalog-ID: |
ELV006986676 |
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| 100 | 1 | |a Zhou, Jiahua |e verfasserin |0 (orcid)0000-0002-9985-828X |4 aut | |
| 245 | 1 | 0 | |a Effect of Mississippi River discharge and local hydrological variables on salinity of nearby estuaries using a machine learning algorithm |
| 264 | 1 | |c 2021 | |
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| 520 | |a Estuarine water salinity is important to coastal ecosystems and the economy. To examine the potential effect of the Mississippi River freshwater discharge on water salinity of other estuaries, we used the machine learning Cubist algorithm to model salinity of three estuaries (Apalachicola Bay, Weeks Bay, and Grand Bay) of the northern Gulf of Mexico (GoM). The models were trained — both with and without the Mississippi River discharge as input — to examine its effect on the salinity of other estuaries along with other variables including wind, discharge, and water depth. By including the Mississippi discharge, the Root Mean Squared Error (RMSE) of the model training sets decreased by 0.81, 1.06 and 1.73 for the Apalachicola Bay, Weeks Bay, and Grand Bay, respectively, while the decrease of the model testing sets was 0.20, 0.64, and 0.84, respectively, using the practical salinity scale. The rank of predictor importance of the Mississippi discharge increased as the modeled estuary proximity increased to the Mississippi outlet. The results showed that the Mississippi River discharge can affect the water quality of other estuaries along the northern GoM, although the variation of the salinity explained by this flow was smaller than that explained by local river flow (except for the Grand Bay where there is no local river flow) and local wind. | ||
| 650 | 4 | |a Gulf of Mexico | |
| 650 | 4 | |a Estuary | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Salinity | |
| 650 | 4 | |a Mississippi river | |
| 700 | 1 | |a Deitch, Matthew J. |e verfasserin |4 aut | |
| 700 | 1 | |a Grunwald, Sabine |e verfasserin |4 aut | |
| 700 | 1 | |a Screaton, Elizabeth J. |e verfasserin |0 (orcid)0000-0002-5456-9046 |4 aut | |
| 700 | 1 | |a Olabarrieta, Maitane |e verfasserin |4 aut | |
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10.1016/j.ecss.2021.107628 doi (DE-627)ELV006986676 (ELSEVIER)S0272-7714(21)00477-7 DE-627 ger DE-627 rda eng 550 DE-600 38.48 bkl 38.90 bkl 42.94 bkl Zhou, Jiahua verfasserin (orcid)0000-0002-9985-828X aut Effect of Mississippi River discharge and local hydrological variables on salinity of nearby estuaries using a machine learning algorithm 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Estuarine water salinity is important to coastal ecosystems and the economy. To examine the potential effect of the Mississippi River freshwater discharge on water salinity of other estuaries, we used the machine learning Cubist algorithm to model salinity of three estuaries (Apalachicola Bay, Weeks Bay, and Grand Bay) of the northern Gulf of Mexico (GoM). The models were trained — both with and without the Mississippi River discharge as input — to examine its effect on the salinity of other estuaries along with other variables including wind, discharge, and water depth. By including the Mississippi discharge, the Root Mean Squared Error (RMSE) of the model training sets decreased by 0.81, 1.06 and 1.73 for the Apalachicola Bay, Weeks Bay, and Grand Bay, respectively, while the decrease of the model testing sets was 0.20, 0.64, and 0.84, respectively, using the practical salinity scale. The rank of predictor importance of the Mississippi discharge increased as the modeled estuary proximity increased to the Mississippi outlet. The results showed that the Mississippi River discharge can affect the water quality of other estuaries along the northern GoM, although the variation of the salinity explained by this flow was smaller than that explained by local river flow (except for the Grand Bay where there is no local river flow) and local wind. Gulf of Mexico Estuary Machine learning Salinity Mississippi river Deitch, Matthew J. verfasserin aut Grunwald, Sabine verfasserin aut Screaton, Elizabeth J. verfasserin (orcid)0000-0002-5456-9046 aut Olabarrieta, Maitane verfasserin aut Enthalten in Estuarine, coastal and shelf science London : Academic Press, 1981 263 Online-Ressource (DE-627)266016448 (DE-600)1466742-3 (DE-576)259270679 0272-7714 nnns volume:263 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.48 Marine Geologie 38.90 Ozeanologie Ozeanographie 42.94 Meeresbiologie AR 263 |
| spelling |
10.1016/j.ecss.2021.107628 doi (DE-627)ELV006986676 (ELSEVIER)S0272-7714(21)00477-7 DE-627 ger DE-627 rda eng 550 DE-600 38.48 bkl 38.90 bkl 42.94 bkl Zhou, Jiahua verfasserin (orcid)0000-0002-9985-828X aut Effect of Mississippi River discharge and local hydrological variables on salinity of nearby estuaries using a machine learning algorithm 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Estuarine water salinity is important to coastal ecosystems and the economy. To examine the potential effect of the Mississippi River freshwater discharge on water salinity of other estuaries, we used the machine learning Cubist algorithm to model salinity of three estuaries (Apalachicola Bay, Weeks Bay, and Grand Bay) of the northern Gulf of Mexico (GoM). The models were trained — both with and without the Mississippi River discharge as input — to examine its effect on the salinity of other estuaries along with other variables including wind, discharge, and water depth. By including the Mississippi discharge, the Root Mean Squared Error (RMSE) of the model training sets decreased by 0.81, 1.06 and 1.73 for the Apalachicola Bay, Weeks Bay, and Grand Bay, respectively, while the decrease of the model testing sets was 0.20, 0.64, and 0.84, respectively, using the practical salinity scale. The rank of predictor importance of the Mississippi discharge increased as the modeled estuary proximity increased to the Mississippi outlet. The results showed that the Mississippi River discharge can affect the water quality of other estuaries along the northern GoM, although the variation of the salinity explained by this flow was smaller than that explained by local river flow (except for the Grand Bay where there is no local river flow) and local wind. Gulf of Mexico Estuary Machine learning Salinity Mississippi river Deitch, Matthew J. verfasserin aut Grunwald, Sabine verfasserin aut Screaton, Elizabeth J. verfasserin (orcid)0000-0002-5456-9046 aut Olabarrieta, Maitane verfasserin aut Enthalten in Estuarine, coastal and shelf science London : Academic Press, 1981 263 Online-Ressource (DE-627)266016448 (DE-600)1466742-3 (DE-576)259270679 0272-7714 nnns volume:263 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.48 Marine Geologie 38.90 Ozeanologie Ozeanographie 42.94 Meeresbiologie AR 263 |
| allfields_unstemmed |
10.1016/j.ecss.2021.107628 doi (DE-627)ELV006986676 (ELSEVIER)S0272-7714(21)00477-7 DE-627 ger DE-627 rda eng 550 DE-600 38.48 bkl 38.90 bkl 42.94 bkl Zhou, Jiahua verfasserin (orcid)0000-0002-9985-828X aut Effect of Mississippi River discharge and local hydrological variables on salinity of nearby estuaries using a machine learning algorithm 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Estuarine water salinity is important to coastal ecosystems and the economy. To examine the potential effect of the Mississippi River freshwater discharge on water salinity of other estuaries, we used the machine learning Cubist algorithm to model salinity of three estuaries (Apalachicola Bay, Weeks Bay, and Grand Bay) of the northern Gulf of Mexico (GoM). The models were trained — both with and without the Mississippi River discharge as input — to examine its effect on the salinity of other estuaries along with other variables including wind, discharge, and water depth. By including the Mississippi discharge, the Root Mean Squared Error (RMSE) of the model training sets decreased by 0.81, 1.06 and 1.73 for the Apalachicola Bay, Weeks Bay, and Grand Bay, respectively, while the decrease of the model testing sets was 0.20, 0.64, and 0.84, respectively, using the practical salinity scale. The rank of predictor importance of the Mississippi discharge increased as the modeled estuary proximity increased to the Mississippi outlet. The results showed that the Mississippi River discharge can affect the water quality of other estuaries along the northern GoM, although the variation of the salinity explained by this flow was smaller than that explained by local river flow (except for the Grand Bay where there is no local river flow) and local wind. Gulf of Mexico Estuary Machine learning Salinity Mississippi river Deitch, Matthew J. verfasserin aut Grunwald, Sabine verfasserin aut Screaton, Elizabeth J. verfasserin (orcid)0000-0002-5456-9046 aut Olabarrieta, Maitane verfasserin aut Enthalten in Estuarine, coastal and shelf science London : Academic Press, 1981 263 Online-Ressource (DE-627)266016448 (DE-600)1466742-3 (DE-576)259270679 0272-7714 nnns volume:263 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.48 Marine Geologie 38.90 Ozeanologie Ozeanographie 42.94 Meeresbiologie AR 263 |
| allfieldsGer |
10.1016/j.ecss.2021.107628 doi (DE-627)ELV006986676 (ELSEVIER)S0272-7714(21)00477-7 DE-627 ger DE-627 rda eng 550 DE-600 38.48 bkl 38.90 bkl 42.94 bkl Zhou, Jiahua verfasserin (orcid)0000-0002-9985-828X aut Effect of Mississippi River discharge and local hydrological variables on salinity of nearby estuaries using a machine learning algorithm 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Estuarine water salinity is important to coastal ecosystems and the economy. To examine the potential effect of the Mississippi River freshwater discharge on water salinity of other estuaries, we used the machine learning Cubist algorithm to model salinity of three estuaries (Apalachicola Bay, Weeks Bay, and Grand Bay) of the northern Gulf of Mexico (GoM). The models were trained — both with and without the Mississippi River discharge as input — to examine its effect on the salinity of other estuaries along with other variables including wind, discharge, and water depth. By including the Mississippi discharge, the Root Mean Squared Error (RMSE) of the model training sets decreased by 0.81, 1.06 and 1.73 for the Apalachicola Bay, Weeks Bay, and Grand Bay, respectively, while the decrease of the model testing sets was 0.20, 0.64, and 0.84, respectively, using the practical salinity scale. The rank of predictor importance of the Mississippi discharge increased as the modeled estuary proximity increased to the Mississippi outlet. The results showed that the Mississippi River discharge can affect the water quality of other estuaries along the northern GoM, although the variation of the salinity explained by this flow was smaller than that explained by local river flow (except for the Grand Bay where there is no local river flow) and local wind. Gulf of Mexico Estuary Machine learning Salinity Mississippi river Deitch, Matthew J. verfasserin aut Grunwald, Sabine verfasserin aut Screaton, Elizabeth J. verfasserin (orcid)0000-0002-5456-9046 aut Olabarrieta, Maitane verfasserin aut Enthalten in Estuarine, coastal and shelf science London : Academic Press, 1981 263 Online-Ressource (DE-627)266016448 (DE-600)1466742-3 (DE-576)259270679 0272-7714 nnns volume:263 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.48 Marine Geologie 38.90 Ozeanologie Ozeanographie 42.94 Meeresbiologie AR 263 |
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10.1016/j.ecss.2021.107628 doi (DE-627)ELV006986676 (ELSEVIER)S0272-7714(21)00477-7 DE-627 ger DE-627 rda eng 550 DE-600 38.48 bkl 38.90 bkl 42.94 bkl Zhou, Jiahua verfasserin (orcid)0000-0002-9985-828X aut Effect of Mississippi River discharge and local hydrological variables on salinity of nearby estuaries using a machine learning algorithm 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Estuarine water salinity is important to coastal ecosystems and the economy. To examine the potential effect of the Mississippi River freshwater discharge on water salinity of other estuaries, we used the machine learning Cubist algorithm to model salinity of three estuaries (Apalachicola Bay, Weeks Bay, and Grand Bay) of the northern Gulf of Mexico (GoM). The models were trained — both with and without the Mississippi River discharge as input — to examine its effect on the salinity of other estuaries along with other variables including wind, discharge, and water depth. By including the Mississippi discharge, the Root Mean Squared Error (RMSE) of the model training sets decreased by 0.81, 1.06 and 1.73 for the Apalachicola Bay, Weeks Bay, and Grand Bay, respectively, while the decrease of the model testing sets was 0.20, 0.64, and 0.84, respectively, using the practical salinity scale. The rank of predictor importance of the Mississippi discharge increased as the modeled estuary proximity increased to the Mississippi outlet. The results showed that the Mississippi River discharge can affect the water quality of other estuaries along the northern GoM, although the variation of the salinity explained by this flow was smaller than that explained by local river flow (except for the Grand Bay where there is no local river flow) and local wind. Gulf of Mexico Estuary Machine learning Salinity Mississippi river Deitch, Matthew J. verfasserin aut Grunwald, Sabine verfasserin aut Screaton, Elizabeth J. verfasserin (orcid)0000-0002-5456-9046 aut Olabarrieta, Maitane verfasserin aut Enthalten in Estuarine, coastal and shelf science London : Academic Press, 1981 263 Online-Ressource (DE-627)266016448 (DE-600)1466742-3 (DE-576)259270679 0272-7714 nnns volume:263 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.48 Marine Geologie 38.90 Ozeanologie Ozeanographie 42.94 Meeresbiologie AR 263 |
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550 DE-600 38.48 bkl 38.90 bkl 42.94 bkl Effect of Mississippi River discharge and local hydrological variables on salinity of nearby estuaries using a machine learning algorithm Gulf of Mexico Estuary Machine learning Salinity Mississippi river |
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Effect of Mississippi River discharge and local hydrological variables on salinity of nearby estuaries using a machine learning algorithm |
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Effect of Mississippi River discharge and local hydrological variables on salinity of nearby estuaries using a machine learning algorithm |
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Zhou, Jiahua |
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Estuarine, coastal and shelf science |
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Zhou, Jiahua Deitch, Matthew J. Grunwald, Sabine Screaton, Elizabeth J. Olabarrieta, Maitane |
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10.1016/j.ecss.2021.107628 |
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effect of mississippi river discharge and local hydrological variables on salinity of nearby estuaries using a machine learning algorithm |
| title_auth |
Effect of Mississippi River discharge and local hydrological variables on salinity of nearby estuaries using a machine learning algorithm |
| abstract |
Estuarine water salinity is important to coastal ecosystems and the economy. To examine the potential effect of the Mississippi River freshwater discharge on water salinity of other estuaries, we used the machine learning Cubist algorithm to model salinity of three estuaries (Apalachicola Bay, Weeks Bay, and Grand Bay) of the northern Gulf of Mexico (GoM). The models were trained — both with and without the Mississippi River discharge as input — to examine its effect on the salinity of other estuaries along with other variables including wind, discharge, and water depth. By including the Mississippi discharge, the Root Mean Squared Error (RMSE) of the model training sets decreased by 0.81, 1.06 and 1.73 for the Apalachicola Bay, Weeks Bay, and Grand Bay, respectively, while the decrease of the model testing sets was 0.20, 0.64, and 0.84, respectively, using the practical salinity scale. The rank of predictor importance of the Mississippi discharge increased as the modeled estuary proximity increased to the Mississippi outlet. The results showed that the Mississippi River discharge can affect the water quality of other estuaries along the northern GoM, although the variation of the salinity explained by this flow was smaller than that explained by local river flow (except for the Grand Bay where there is no local river flow) and local wind. |
| abstractGer |
Estuarine water salinity is important to coastal ecosystems and the economy. To examine the potential effect of the Mississippi River freshwater discharge on water salinity of other estuaries, we used the machine learning Cubist algorithm to model salinity of three estuaries (Apalachicola Bay, Weeks Bay, and Grand Bay) of the northern Gulf of Mexico (GoM). The models were trained — both with and without the Mississippi River discharge as input — to examine its effect on the salinity of other estuaries along with other variables including wind, discharge, and water depth. By including the Mississippi discharge, the Root Mean Squared Error (RMSE) of the model training sets decreased by 0.81, 1.06 and 1.73 for the Apalachicola Bay, Weeks Bay, and Grand Bay, respectively, while the decrease of the model testing sets was 0.20, 0.64, and 0.84, respectively, using the practical salinity scale. The rank of predictor importance of the Mississippi discharge increased as the modeled estuary proximity increased to the Mississippi outlet. The results showed that the Mississippi River discharge can affect the water quality of other estuaries along the northern GoM, although the variation of the salinity explained by this flow was smaller than that explained by local river flow (except for the Grand Bay where there is no local river flow) and local wind. |
| abstract_unstemmed |
Estuarine water salinity is important to coastal ecosystems and the economy. To examine the potential effect of the Mississippi River freshwater discharge on water salinity of other estuaries, we used the machine learning Cubist algorithm to model salinity of three estuaries (Apalachicola Bay, Weeks Bay, and Grand Bay) of the northern Gulf of Mexico (GoM). The models were trained — both with and without the Mississippi River discharge as input — to examine its effect on the salinity of other estuaries along with other variables including wind, discharge, and water depth. By including the Mississippi discharge, the Root Mean Squared Error (RMSE) of the model training sets decreased by 0.81, 1.06 and 1.73 for the Apalachicola Bay, Weeks Bay, and Grand Bay, respectively, while the decrease of the model testing sets was 0.20, 0.64, and 0.84, respectively, using the practical salinity scale. The rank of predictor importance of the Mississippi discharge increased as the modeled estuary proximity increased to the Mississippi outlet. The results showed that the Mississippi River discharge can affect the water quality of other estuaries along the northern GoM, although the variation of the salinity explained by this flow was smaller than that explained by local river flow (except for the Grand Bay where there is no local river flow) and local wind. |
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