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Item Comparison of the Multiple Imputation Approaches for Imputing Rainfall Data: A Humid Tropical River Basin Case Study(Springer Nature, 2025) Kumar, G.P.; Dwarakish, G.S.Accurate rainfall data is crucial for agriculture, hydrology, and climate research as it guides water management, crop planning, and disaster preparedness. Missing data affects reliability, requiring effective imputation. The purpose of this study is to address the critical challenge of imputing missing daily rainfall data, which is especially important given rainfall’s nonlinear distribution and variability in missingness patterns. This research aims to develop and evaluate advanced imputation algorithms to improve data completeness and integrity in humid tropical regions. This study evaluates ten imputation algorithms: K-nearest neighbors (KNN), classification and regression trees (CART), predictive mean matching (pmm), random forest (rf), mean method, and Bayesian methods (norm. boot, lasso. norm, norm, norm. nob, midastouch) for addressing missing daily rainfall data. Using 37 years of data from thirteen stations in the Kali River Basin, the methods leverage descriptive statistics to enhance accuracy in humid tropical regions. The proposed algorithms incorporate descriptive statistics of the rainfall time series and are evaluated using 37 years of daily data from thirteen selected rainfall stations in the Kali River Basin, a humid tropical region. Model performance was assessed at four missingness levels (1%, 5%, 10%, and 20%) and evaluated with accuracy metrics root mean square error (RMSE), mean absolute error (MAE), index of agreement (d), and RMSE-observations standard deviation ratio (RSR). Among the evaluated methods, KNN consistently demonstrated superior performance across all levels of missingness (RMSE = 13.22 to 15.42; MAE = 4.68 to 6.08; d = 0.87 to 0.90; RSR = 0.57 to 0.61), followed closely by CART (RMSE = 16.48 to 20.77; MAE = 6.20 to 8.31; d = 0.81 to 0.86; RSR = 0.71 to 0.79). Overall, KNN, CART, pmm, and rf emerged as reliable methods for imputing missing rainfall data of varying lengths, contributing to more accurate weather forecasting and climate change analyses. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.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.