This study presents the ‘Dual Path CNN-MLP’, a novel hybrid deep neural network (DNN) architecture that merges the strengths of convolutional neural networks (CNNs) and multilayer perceptrons (MLPs) for regional groundwater flow simulations. This model stands out from previous DNN approaches by managing mixed input types, including both imagery and numerical vectors. Such flexibility allows the diverse nature of groundwater data to be efficiently utilized without the need to convert it into a uniform format, which often leads to oversimplification or unnecessary expansion of the dataset. When applied to the northeast Qatar aquifer, the model demonstrates high accuracy in simulating transient groundwater flow fields, benchmarked against the well-established MODFLOW model. The model's efficacy is confirmed through k-fold cross-validation, showing an error margin of less than 12% across all examined locations. The study also examines the model's ability to perform uncertainty analysis using Monte Carlo simulations, finding that it achieves around 1% average absolute percentage error in estimating the mean hydraulic head. Errors are mostly found in areas with significant variations in the hydraulic head. Switching to this machine learning model from the conventional MODFLOW simulator boosts computational efficiency by about 99%, showcasing its advantage for tasks like uncertainty analysis in repetitive groundwater simulations.
In a complex pressurized water diversion project (WDP), the combined optimal operation of multiple hydraulic facilities is computationally expensive owing to the requirement of massive mathematical simulation model runs. A parallel multi-objective optimization based on adaptive surrogate model (PMO-ASMO) was proposed in this study to alleviate the computational burden while maintaining its effectiveness. At the simulation level, an adaptive surrogate model was established, while a paralle
Rainfall has a dominant role in rainfall-runoff models, with the rendering of these models depending on the data accuracy and on the way that rainfall is spatially allocated. The research proposes a methodological framework where a genetic algorithm (GA)-based method responsible for the spatial distribution of gauge observations at the basin scale is coupled with the HEC-HMS hydrological model to produce simulated discharges of high accuracy. The custom-developed GA is used to divide a 2D
Urban floods pose a significant threat to human communities, making its prediction essential for comprehensive flood risk assessment and the formulation of effective resource allocation strategies. Data-driven deep learning approaches have gained traction in urban emergency flood prediction, addressing the efficiency constraints of physical models. However, the spatial structure of rainfall, which has a profound influence on urban flooding, is often overlooked in many deep learning invest
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