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Palabras clave – provistas por la editorial

ANN; roadside IoT sensors; simulations of the gridded rainstorms; 2D inundation simulation and real-time error correction; weather types and features; meteorological feature extraction; artificial neural network; self-organizing map (SOM); urban agriculture; resource utilization efficiency; urban northern Taiwan; machine learning; random forest; regression analysis; support vector machine; threshold rainfall; threshold runoff; XGBoost; stochastic rainfall generator; Huff rainfall curve; copula; GeoAI; artificial intelligence; hydrological; hydraulic; fluvial; water quality; geomorphic; modeling; anomaly detection; deep reinforcement learning; telemetry water level; time series; ensemble; multi-model ensemble; precipitation; forecasting; persian gulf; deep learning; dam inflow; RNN; LSTM; GRU; hyperparameter; rainfall time series; artificial neural networks; Multiple Linear Regression; Chania; CNN; ELM; temporary rivers; hydrological extremes; multivariate stochastic model; autoregressive model; Markov model; daily temperature; temperature generator; Bayesian neural network; forecasting uncertainty; multi-step ahead forecasting; probabilistic streamflow forecasting; variational inference; smart microclimate-control system (SMCS); system dynamics; water–energy–food nexus; agricultural resilience; hydroinformatics; hydrological modeling; early warning; uncertainty; sustainability