This study investigates the feasibility of a relevance vector machine tuned with improved
Manta-Ray foraging optimization (RVM-IMRFO) in predicting monthly pan evaporation using …

Modern machine learning (ML) techniques are making inroads in every aspect of renewable
energy for optimization and model prediction. The effective utilization of ML techniques for …

Study region Two hyper-arid regions (Atbara and Kassala stations) in Sudan. Study focus
The study aims to evaluate the potential of the D-vine Copula-based quantile regression …

Applying new approaches in the management of water resources is a vital issue, especially
in arid and semi-arid regions. The water footprint is a key index in water management …

This study examines the effectiveness of various quantile regression (QR) and machine
learning (ML) methodologies developed for analyzing the relationship between …

The world is increasingly embracing cleaner and more sustainable energy sources, with
solar energy playing a crucial role in mitigating greenhouse gas emissions and addressing …

Predicting evaporation is an essential topic in water resources management. It is critical to
plan irrigation schedules, optimize hydropower production, and accurately calculate the …

PSO is an evolutionary algorithm for solving the optimization problem. This chapter explains
the mathematical model and structure of PSO. The PSO is initialized with random positions …

This study employs precipitation data sets from historical trials on 20 CMIP6 global climate
models and four shared socioeconomic pathway scenario trials (SSP1-2.6, SSP2-4.5, SSP3 …

In order to effectively improve the accuracy of measurement data from the ultrasonic
evaporation sensor (UES), research is conducted on evaluating the measurement …