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The community’s well-being and economic livelihoods are heavily influenced by the water level of watersheds. The changes in water levels directly affect the circulation processes of lakes and rivers that control water mixing and bottom sediment resuspension, further affecting water quality and aquatic ecosystems. Thus, these considerations have made the water level monitoring process essential to save the environment. Machine learning hybrid models are emerging robust tools that are successfully applied for water level monitoring. Various models have been developed, and selecting the optimal model would be a lengthy procedure. A timely, detailed, and instructive overview of the models’ concepts and historical uses would be beneficial in preventing researchers from overlooking models’ potential selection and saving significant time on the problem. Thus, recent research on water level prediction using hybrid machines is reviewed in this article to present the “state of the art” on the subject and provide some suggestions on research methodologies and models. This comprehensive study classifies hybrid models into four types algorithm parameter optimisation-based hybrid models (OBH), pre-processing based hybrid models (PBH), the components combination-based hybrid models (CBH), and hybridisation of parameter optimisation-based with preprocessing-based hybrid models (HOPH); furthermore, it explains the pre-processing of data in detail. Finally, the most popular optimisation methods and future perspectives and conclusions have been discussed. [ABSTRACT FROM AUTHOR]

Most of the hydraulic structures rest on an impervious foundation to reserve water at the upstream side. The water heads difference leads to water movement from the higher to the lower head through the porous soil layer beneath the foundation, generating an uplift pressure under the structure floor. In this study, a new method is presented to estimate the uplift pressures at key points by performing sub-surface flow analysis using the Analysis SYStem (ANSYS) software. Then a statistical analysis to validate the proposed equations is conducted using the SPSS software. The case study for this research is a barrage in Kufa city-Iraq. The used data to implement this study was water levels, soil permeability, and length of imperious foundation. The obtained results show good outcomes from using the proposed method to develop uplift pressure equations. The comparison of the current study results with Khosla's equation showed good agreement where the coefficient of determination (R2) and the standard error of estimation (SEE) for the equations were between (99.9–97.8) and (0.024–0.11), respectively. [ABSTRACT FROM AUTHOR]