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

Authors / Editors

Research Areas

No matching items found.

Publication Details

Output type: Journal article

UM6P affiliated Publication?: Yes

Author list: Elbeltagi, Ahmed; Kumari, Nikul; Dharpure, Jaydeo K.; Mokhtar, Ali; Alsafadi, Karam; Kumar, Manish; Mehdinejadiani, Behrouz; Ramezani Etedali, Hadi; Brouziyne, Youssef; Towfiqul Islam, Abu Reza Md.; Kuriqi, Alban

Publisher: MDPI

Publication year: 2021

Journal: Water (2073-4441)

Volume number: 13

Issue number: 4

ISSN: 2073-4441

eISSN: 2073-4441

Languages: English (EN-GB)

View in Web of Science | View on publisher site | View citing articles in Web of Science


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 R-2 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 R-2 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.


No matching items found.


No matching items found.

Last updated on 2021-25-11 at 23:19