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Ahmed Elbeltagi

Researcher at Mansoura University

Publications -  127
Citations -  1614

Ahmed Elbeltagi is an academic researcher from Mansoura University. The author has contributed to research in topics: Environmental science & Computer science. The author has an hindex of 8, co-authored 40 publications receiving 213 citations. Previous affiliations of Ahmed Elbeltagi include Zhejiang University.

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Estimation of SPEI Meteorological Drought Using Machine Learning Algorithms

TL;DR: In this paper, a combination of machine learning with the Standardized Precipitation Evapotranspiration Index (SPEI) is proposed for analysis of drought within a representative case study in the Tibetan Plateau, China, for the period of 1980-2019.
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Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment

TL;DR: In this article, a deep neural network (DNN) was employed for incorporating historical data and predicting future crop evapotranspiration (ETc) values to eliminate the limitations mentioned, and analyze the long-term dynamics of ETc based on limited climate data and simple method.
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The impact of climate changes on the water footprint of wheat and maize production in the Nile Delta, Egypt.

TL;DR: The findings showed that determination-coefficient between historical-predicted crop evapotranspiration (ETc) varied from 0.92 to 0.97 for two crops, which will help in optimal planning of future water under climate change in the agricultural sector.
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Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models

TL;DR: In this paper, three machine learning models, viz. long short-term memory (LSTM), multi-linear regression (MLR), and artificial neural network (ANN), have been trained.
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Prediction of Combined Terrestrial Evapotranspiration Index (CTEI) over Large River Basin Based on Machine Learning Approaches

TL;DR: In this article, 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.