Stochastic analysis of seepage under water-retaining structures

This paper investigated the problem of confined flow under dams and water retaining structures using stochastic modelling. The approach advocated in the study combined a finite elements method based on the equation governing the dynamics of incompressible fluid flow through a porous medium with a random field generated hydraulic conductivity using a lognormal probability distribution. The resulting model was then used to analyse confined flow under a hydraulic structure. Cases for a structure provided with cutoff wall and when the wall did not exist were both tested. Various statistical parameters that reflected different degrees of heterogeneity were examined and the changes in the mean seepage flow, the mean uplift force and the mean exit gradient observed under the structure were analysed. Results reveal that under heterogeneous conditions, the reduction made by the sheetpile in the uplift force and exit hydraulic gradient may be underestimated when deterministic solutions are used

Applications of machine learning to water resources management: A review of present status and future opportunities

Data availability: No data was used for the research described in the article.The corrected proof will be replaced by version of record in due course.Copyright © 2024 The Authors. Water is the most valuable natural resource on earth that plays a critical role in the socio-economic development of humans worldwide. Water is used for various purposes, including, but not limited to, drinking, recreation, irrigation, and hydropower production. The expected population growth at a global scale, coupled with the predicted climate change-induced impacts, warrants the need for proactive and effective management of water resources. Over the recent decades, machine learning tools have been widely applied to various water resources management-related fields and have often shown promising results. Despite the publication of several review articles on machine learning applications in water-related fields, this review paper presents for the first time a comprehensive review of machine learning techniques applied to water resources management, focusing on the most recent achievements. The study examines the potential for advanced machine learning techniques to improve decision support systems in the various sectors within the realm of water resources management, which includes groundwater management, streamflow forecasting, water distribution systems, water quality and wastewater treatment, water demand and consumption, hydropower and marine energy, water drainage systems, and flood management and defence. This study provides an overview of the state-of-the-art machine learning approaches to the water industry and how they can be used to ensure water supply sustainability, quality, and flood and drought mitigation. This review covers the most recent related studies to provide the most recent snapshot of machine learning applications in the water industry. Overall, LSTM networks have been proven to exhibit reliable performance, often outperforming ANN models, traditional machine learning models, and established physics-based models. Hybrid ML techniques have exhibited great forecasting accuracy across all water-related fields, often showing superior computational power over traditional ANNs architectures. In addition to purely data-driven models, physical-based hybrid models have also been developed to improve prediction performance. These efforts further demonstrate that Machine learning can be a powerful practical tool for water resources management. It provides insights, predictions, and optimisation capabilities to help enhance sustainable water use and management and improve socio-economic development, healthy ecosystems and human existence.EPSRC project reference 2339403 to S. Sayed and A. Ahmed

Determination of aquifer units using vertical electrical sounding technique: a case study of federal low cost housing estate, Okeho, SW Nigeria

The groundwater development of Federal Low Cost Housing Estate Okeho involved the use of schlumberger vertical electrical sounding technique. The result of the survey showed that qualitatively three major curve types H, QH and KH were observed. The geoelectric layers range from 3 to 4 while the quantitative interpretation resulted in deducing layer parameters of 190-1103Ώm and 0.70-1.10m for the topsoil. The intermediate layer has layer resistivity of 93 -1590Ώm and thickness of 0.9-4.70m while the weathered basement has resistivity and thickness of 18-50Ώm and 6.0-10.4m. The bedrock resistivity range from 426-6284Ώm with an infinite thickness; the bedrock resistivity of less than 1000Ώm in this area and the weathered layer constitute the aquifer. Keywords: aquifer occurrences, geoelectric parameters, sounding curves Nigeria Journal of Pure and Applied Physics Vol. 4(1) 2005: 60-6