Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Groundwater level modeling using Augmented Artificial Ecosystem Optimization(Elsevier B.V., 2023) Nguyen, N.; Deb Barma, S.D.; van Lam, T.; Kisi, O.; Mahesha, A.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 Artificial Ecosystem Optimization 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. © 2022 Elsevier B.V.Item Multi-criteria decision-making and machine learning-based CMIP6 general circulation model ensemble for climate projections in a tropical river basin in India(Springer Science and Business Media Deutschland GmbH, 2025) Kumar, G.P.; Vinod, D.; Dwarakish, G.S.; Mahesha, A.General circulation models (GCMs) are vital for accurate climate prediction and informing strategic water resource planning. The investigation explores the performance of five machine learning (ML) algorithms for ensembling the GCMs for top-5 and least-5 ranked models in multi-criteria decision-making (MCDM) in addition to 28 GCMs applicable to a tropical river basin in India and the performance of their ensemble using statistical metrics. The gridded datasets from the India Meteorological Department (IMD) are used as observed data. From the statistical metrics, an entire 28 GCMs ensemble showed superiority over top-5 and least-5 ranked ensembles for three meteorological variables. The random forest (RF) algorithm consistently demonstrated high accuracy and reliability in ensembling the GCMs for the three meteorological variables, followed by support vector machine (SVM) and multiple linear regression (MLR). By implementing the proposed approach, researchers can minimize biases, enable resource-efficient modeling, and deliver practical insights through robust and reliable climate projections. These results highlight the importance of thoughtful ensemble design, advocating using multi-model ensembles (MMEs) in comprehensive climate studies to ensure accurate predictions across diverse climate indices. The findings provide valuable insights into local climate conditions, supporting ecosystem management and informing policy decisions. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences 2025.