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RISDeveloping Interpretable Pan Evaporation Forecasting Models for Wafra Agricultural Basin Based on Optimized Decision Tree Ensembles
Publisher: American Society of Civil Engineers
Abstract: AbstractThe forecasting of pan evaporation has improved with the advancement of machine learning
(ML) models. However, many existing approaches suffer from limited interpretability
and uncertain transferability, limiting ...
Regulating Irrigation Water Supply in Arid Agricultural Basins Using a Computational Machine-Learning Framework for Forecasting Vapor Pressure Deficit
Publisher: American Society of Civil Engineers
Abstract: AbstractAccurate forecasts of vapor pressure deficit (VPD) are essential for irrigation water
management, especially for water-scarce nations, because they enable precise estimation
of crop water demand and support the ...
Quantifying Irrigation Water Demand through Optimized Daily Vapor Pressure Deficit Forecasting Using LSTM and Metaheuristic Algorithms
Publisher: American Society of Civil Engineers
Abstract: AbstractThis study presents a robust computational framework that integrates long short-term
memory (LSTM) neural networks with four advanced metaheuristic optimization algorithms:
Nondominated Sorting Genetic Algorithm ...
Modeling the Onset of Drought Periods Using Explainable Machine Learning Models Enhanced by Bayesian Optimization
Publisher: American Society of Civil Engineers
Abstract: AbstractThis study develops an optimized machine learning-based computational framework for
assessing drought conditions in water-scarce regions. The pattern of drought periods
is highly non-linear, especially in arid ...
Enhancing Soil Water Prediction in Arid Climates Using Multipredictor Machine-Learning Models and SHAP-Based Interpretability
Publisher: American Society of Civil Engineers
Abstract: AbstractEfficient water management in irrigated watersheds requires the timely and precise
application of water to optimize crop yields and sustain resources. Although machine-learning
(ML) models have shown promise in ...