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Prediction of Combined Terrestrial Evapotranspiration Index (CTEI) over Large River Basin Based on Machine Learning Approaches

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Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt
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College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
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Discipline of Civil, Surveying and Environmental Engineering, University of Newcastle, Callaghan, NSW 2308, Australia
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Centre of Excellence in Disaster Mitigation and Management, Indian Institute of Technology Roorkee, Uttarakhand 247667, India
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State of Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau, Institute of Soil and Water Conservation, Northwest Agriculture and Forestry University, Yangling 712100, China
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Department of Agricultural Engineering, Faculty of Agriculture, Cairo University, Giza 12613, Egypt
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Department of Geography and GIS, Faculty of Arts, Alexandria University, Alexandria 25435, Egypt
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Department of Soil and Water Conservation Engineering, G. B. Pant University of Agriculture and Technology, Pantnagar 263145, India
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Department of Water Science and Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj 66177-15175, Iran
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Department of Water Sciences and Engineering, Imam Khomeini International University, Qazvin 34149-16818, Iran
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International Water Research Institute, Mohammed VI Polytechnic University (UM6P), Benguerir 43150, Morocco
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Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh
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CERIS, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
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Authors to whom correspondence should be addressed.
Academic Editor: Renato Mor-bidelli
Water 2021, 13(4), 547; https://doi.org/10.3390/w13040547
Received: 15 January 2021 / Revised: 13 February 2021 / Accepted: 17 February 2021 / Published: 20 February 2021
(This article belongs to the Section Hydrology and Hydrogeology)
Drought is a fundamental physical feature of the climate pattern worldwide. Over the past few decades, a natural disaster has accelerated its occurrence, which has significantly impacted agricultural systems, economies, environments, water resources, and supplies. Therefore, it is essential to develop new techniques that enable comprehensive determination and observations of droughts over large areas with satisfactory spatial and temporal resolution. This study modeled a new drought index called the Combined Terrestrial Evapotranspiration Index (CTEI), developed in the Ganga river basin. For this, five Machine Learning (ML) techniques, derived from artificial intelligence theories, were applied: the Support Vector Machine (SVM) algorithm, decision trees, Matern 5/2 Gaussian process regression, boosted trees, and bagged trees. These techniques were driven by twelve different models generated from input combinations of satellite data and hydrometeorological parameters. The results indicated that the eighth model performed best and was superior among all the models, with the SVM algorithm resulting in an R2 value of 0.82 and the lowest errors in terms of the Root Mean Squared Error (RMSE) (0.33) and Mean Absolute Error (MAE) (0.20), followed by the Matern 5/2 Gaussian model with an R2 value of 0.75 and RMSE and MAE of 0.39 and 0.21 mm/day, respectively. Moreover, among all the five methods, the SVM and Matern 5/2 Gaussian methods were the best-performing ML algorithms in our study of CTEI predictions for the Ganga basin. View Full-Text
Keywords: droughts; GRACE; evapotranspiration; machine learning; terrestrial water storage; precipitation; Ganga river basin droughts; GRACE; evapotranspiration; machine learning; terrestrial water storage; precipitation; Ganga river basin
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MDPI and ACS Style

Elbeltagi, A.; Kumari, N.; Dharpure, J.K.; Mokhtar, A.; Alsafadi, K.; Kumar, M.; Mehdinejadiani, B.; Ramezani Etedali, H.; Brouziyne, Y.; Towfiqul Islam, A.R.M.; Kuriqi, A. Prediction of Combined Terrestrial Evapotranspiration Index (CTEI) over Large River Basin Based on Machine Learning Approaches. Water 2021, 13, 547. https://doi.org/10.3390/w13040547

AMA Style

Elbeltagi A, Kumari N, Dharpure JK, Mokhtar A, Alsafadi K, Kumar M, Mehdinejadiani B, Ramezani Etedali H, Brouziyne Y, Towfiqul Islam ARM, Kuriqi A. Prediction of Combined Terrestrial Evapotranspiration Index (CTEI) over Large River Basin Based on Machine Learning Approaches. Water. 2021; 13(4):547. https://doi.org/10.3390/w13040547

Chicago/Turabian Style

Elbeltagi, Ahmed; Kumari, Nikul; Dharpure, Jaydeo K.; Mokhtar, Ali; Alsafadi, Karam; Kumar, Manish; Mehdinejadiani, Behrouz; Ramezani Etedali, Hadi; Brouziyne, Youssef; Towfiqul Islam, Abu R.M.; Kuriqi, Alban. 2021. "Prediction of Combined Terrestrial Evapotranspiration Index (CTEI) over Large River Basin Based on Machine Learning Approaches" Water 13, no. 4: 547. https://doi.org/10.3390/w13040547

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