Enhancing streamflow prediction in a mountainous watershed using a convolutional neural network with gridded data.

In this research, we demonstrate the effectiveness of a convolutional neural network (CNN) model, integrated with the ERA5-Land dataset, for accurately simulating daily streamflow in a mountainous watershed. Our methodology harnesses image-based inputs, incorporating spatial distribution maps of key environmental variables, including temperature, snowmelt, snow cover, snow depth, volumetric soil water content, total evaporation, total precipitation, and leaf area index. The proposed CNN architecture, while drawing inspiration from classical designs, is specifically tailored for the task of streamflow prediction.

Innovative approach for predicting daily reference evapotranspiration using improved shallow and deep learning models in a coastal region: A comparative study.

Accurate and reliable estimation of Reference Evapotranspiration (ETo) is crucial for water resources management, hydrological processes, and agricultural production. The FAO-56 Penman-Monteith (FAO-56PM) approach is recommended as the standard model for ETo estimation; nevertheless, the absence of comprehensive meteorological variables at many global locations frequently restricts its implementation. This study compares shallow learning (SL) and deep learning (DL) models for estimating daily ETo against the FAO-56PM approach based on various statistic metrics and graphic tool over a coastal Red Sea region, Sudan.

An innovative approach for predicting groundwater TDS using optimized ensemble machine learning algorithms at two levels of modeling strategy.

Groundwater salinization in coastal aquifers is a major socioeconomic challenge in Oman and many other regions worldwide due to several anthropogenic activities and natural drivers. Therefore, assessing the salinization of groundwater resources is crucial to ensure the protection of water resources and sustainable management. The aim of this study is to apply a novel approach using predictive optimized ensemble trees-based (ETB) machine learning models, namely Catboost regression (CBR), Extra trees regression (ETR), and Bagging regression (BA), at two levels of modeling strategy for predicting groundwater TDS as an indicator for seawater intrusion in a coastal aquifer, Oman.

A critical review of environmental and public health impacts from the activities of evaporation ponds.

Evaporation ponds (EVPs) are among the most cost-effective, and simple wastewater treatment technologies used in many regions/countries with high solar radiation levels. However, its operational limitations, which include the overflow of wastewater, leakages via liners, and large surface area of the EVP that is exposed to atmosphere, creates a negative feedback to the environment. Therefore, the main aim of this review study of more than a hundred works published a little all over the continents is to provide a summary of various contaminations that are associated with EVPs activities through different environmental compartments.

Optimal Location of Wells for Storage and Recovery of Surplus Desalinated Water in Coastal Aquifers.

Storage of water in aquifers using injection wells is an efficient way for utilizing excess desalinated water in arid regions. In this investigation we estimate the benefits of optimally recharging seasonal surplus desalinated water into a strategic coastal aquifer already benefitting from natural recharge of flash-floods water by a recharge dam. Since, usually the buyers of desalinated water commit to purchase surplus desalinated water under take-or-pay contracts, any attempt in utilizing the paid water is beneficial.

Estimating Groundwater Mounding in Sloping Aquifers for Managed Aquifer Recharge.

Design of managed aquifer recharge (MAR) for augmentation of groundwater resources often lacks detailed data, and simple diagnostic tools for evaluation of the water table in a broad range of parameters are needed. In many large-scale MAR projects, the effect of a regional aquifer base dip cannot be ignored due to the scale of recharge sources (e.g.