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Estimation of dam piezometric water level using new hybrid intelligent models for dam safety assessment

International Journal of Energy and Water Resources 2024年第4期8卷 461-473页

作者： Ziggah, Y.Y. Issaka, Y. Department of Geomatic Engineering Faculty of Geosciences and Environmental Studies University of Mines and Technology Tarkwa Ghana School of Railways and Infrastructure Development University of Mines and Technology Essikado Sekondi‑Takoradi Ghana

The piezometric water level is a significant factor in seepage analysis for identifying and maintaining the stability of engineering structures like dams. Hence, unattended monitoring of the piezometric water level can lead to enormous disasters. To this end, this paper integrates the genetic optimisation algorithm (GA) with two artificial intelligence methods, including an emotional neural network (ENN) and an adaptive neuro-fuzzy inference system (ANFIS), to predict piezometric water level for three different piezometers located on the dam. In the study, the GA was used to evolve the initial weights and bias of the ENN and ANFIS to minimise their respective prediction errors. The prediction efficiency of the developed hybrid GA-ENN and GA-ANFIS models was compared with the artificial neural network (ANN), GA-ANN and ANFIS, respectively. The experimental results implied that GA-ENN and GA-ANFIS models demonstrated no absolute superiority between them but were superior to ANN, GA-ANN and ANFIS. This was evidenced by their excellent prediction accuracy for the three piezometers considered in terms of the root mean square error (RMSE), mean absolute error (MAE), maximum absolute relative error (errMax), scatter index (SI), correlation coefficient (R) and variance accounted factor (VAF). Thus, GA-ENN and GA-ANFIS had RMSE, MAE, errMax, SI, R and VAF in the range [3.01 × 10^–08, 1.06 × 10^–05], [2.60 × 10^–08, 9.66 × 10^–06], [-8.72 × 10^–07, 1.26 × 10^–05], [1.85 × 10^–10, 6.49 × 10^–08], [0.99999, 1] and [99.9999%, 100%], respectively. This study provides viable alternative models for dam engineers, engineering surveyors and allied professionals when predicting dam piezometric water level for seepage analysis. © The Author(s), under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University 2023.

关键词： Artificial intelligence bio-inspired optimisation algorithm Dam deformation Seepage analysis

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Allocation of energy in surface water treatment plants for maximum energy conservation

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2020年第4期22卷 3347-3370页

作者： De, Paulami Majumder, Mrinmoy Natl Inst Technol Sch Hydroinformat Engn Civil Engn Dept Agartala India

Surface water treatment plants are major energy consumers in all countries. In recent years, the increasing scarcity of fossil fuels and the growth in demand for energy resulting from the needs of development have prompted optimisation of the use of energy. SWTPs are responsible for the supply of treated water to consumers. Although a significant amount of the total energy produced is consumed by WTPs, the utilisation of this resource is variable, and sufficient amounts of it remain unutilised or wasted in the treatment process. Energy resources supplied to a WTP must be optimally allocated. At present, no mechanism exists to ensure this, and allocation is performed as and when it is needed, with no regulation or control. As a result, much energy is returned unutilised. This results in excess expenditures and affects carbon emissions from the plant because both too much and too little utilisation of energy in running water treatment equipment can result in the generation of greenhouse gases to the atmosphere. Unnecessary consumption of energy reduces its availability for other users. Thus, the economy, the environment, and social well-being are affected by the non-optimal utilisation of energy. This problem is common to all parts of the world but is especially acute in developing countries. Lack of intelligent allocation methods compromises the sustainability not only of the plant but also of the dependent population. Here, nature-based optimisation algorithms (OAs) and a modified analytical hierarchy process (mAHP), an objective multi-criteria decision-making method, were utilised to conduct intelligent, automatic allocation of energy among elements of wastewater treatment plants (WTPs). OAs are used to weight elements according to their relative capacity to ensure reliability and restrict risk to plants (resulting in a reliability-risk index);energy is allocated accordingly using mAHP. Tested at a working WTP in India, it minimised energy wastage down to 0.037% of

关键词： Water-energy nexus bio-inspired optimisation algorithm Water treatment plant

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