Nature-inspired optimization is an active area of research in the artificial intelligence (AI) field and has recently been adopted in hydrology for the calibration (training) of both process-based and statistical mode...
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Nature-inspired optimization is an active area of research in the artificial intelligence (AI) field and has recently been adopted in hydrology for the calibration (training) of both process-based and statistical models. This study proposes an improved AI model, augmented artificial ecosystem optimization-based Multi-Layer Perceptron (AAEO-MLP), to build a monthly groundwater level (GWL) forecasting model. AAEO-MLP model is built on the novel augmented version of artificialecosystemoptimization and traditional MLP network. In AAEO, Levy -flight trajectory and Gaussian random are utilized in exploration and exploitation to improve the optimizing ability. The AAEO-MLP model is tested on two time-series (1989-2012) datasets collected at two wells in India. Various explanatory variables such as monthly cumulative precipitation, mean temperature, tidal height, and previous measurements of GWL were considered for predicting 1-month ahead of GWL. The performance of AAEO-MLP was benchmarked against 17 different models (original AEO, 3 different variants of AEO, and 13 well-known models) in terms of forecasting accuracy based on six metrics (e.g., mean absolute error, root mean square error, Kling-Gupta efficiency, normalized Nash-Sutcliffe efficiency, Pearson's correlation index, a20 index). Furthermore, convergence analysis and model stability are employed to indicate the effectiveness of AAEO-MLP. The compared results express that the AAEO-MLP is superior to other models in terms of prediction accuracy, convergence, and stability. Overall, the results depict that the AAEO is a promising approach for optimizing machine learning models (e.g., MLP) and should be explored for other hydrological forecasting applications (e.g., streamflow, rainfall) to further assess its strengths over existing methods.
Managing and controlling costly natural hazards such as floods has been a fundamental and essential issue for decision-makers and planners from the past to the present. artificial intelligence (AI) has recently proven...
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Managing and controlling costly natural hazards such as floods has been a fundamental and essential issue for decision-makers and planners from the past to the present. artificial intelligence (AI) has recently proven promising to improve disaster management. There is growing interest in using AI to predict and identify floodprone areas. However, creating accurate flood susceptibility maps with AI remains a significant challenge. Therefore, the present work endeavors to cope with this gap and produce the most efficient flood susceptibility maps employing Categorical Boosting (CatBoost) algorithms and three system-based metaheuristic methods, including augmented artificial ecosystem optimization (AAEO), Germinal Center optimization (GCO), and Water Circle Algorithm (WCA). We selected Jahrom County, Iran, to develop machine learning-based models as our case study. We used 13 flood conditioning geophysical factors as input parameters and flood occurrence (binary classification), derived from satellite imagery, as the output. Our results show that CatBoost-AAEO performs better in flood susceptibility mapping than the other combined models, CatBoost-WCA, CatBoostGCO, and the basic CatBoost model, which are mentioned in descending order of performance. The partial Dependence Plots (PDP) approach is used to interpret the results of the developed algorithms, highlighting that low slope, low elevation, minimal vegetation cover, flat curvature, and proximity to rivers significantly impact the performance of ML models to predict flood occurrence. The findings of this research can help planners manage and prevent floods and avoid development in sensitive areas to reduce financial losses caused by floods.
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