Titlebook: Applications of Machine Learning in Hydroclimatology; Roshan Srivastav,Purna C. Nayak Book 2025 The Editor(s) (if applicable) and The Auth

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River Discharge Forecasting in Mahanadi River Basin Based on Deep Learning Techniques,est for forecasting in engineering because of their forecasting accuracies. Two deep learning techniques long-short term memory (LSTM) and bidirectional long-short term memory (Bi-LSTM) have been applied and compared for forecasting river discharge data in this study. In LSTM, input flows in one dir

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Genetic Algorithm-Aided Neural Network for Sediment Critical Shear Stress Modeling,ment under clay influence which was limited to their own experimental data. The present study aims to develop an ANN model to compute CSS of coarser sediment present in mobile channel bed made of cohesive sediment mixture. The proposed model was optimized using parent-selected operator GA (genetic a

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The High-Resolution Statistical Downscaling of Seasonal Rainfall Forecasts Models for Comprehensivenowledge, providing a wealth of rainfall output from diverse global climate models. However, the inherent spatial resolution of these models often limits their direct applicability for regional scale assessments. This study introduces a Hybrid Gamma Probability Distribution (HGPD) Model designed for

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Prediction of Rainfall in One of the Wettest Regions in India Using Machine Learning Methods,f data and availability of recorded daily rainfalls may affect the feasibility of a larger range of rainfall-based studies in light of repercussions from climate change and extreme hydro-meteorological phenomena. A daily gridded rainfall time series dataset (2007–2020) over Mizoram state is construc

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Strafrechtliche Aspekte der Managerhaftungo predict streamflow. Because Artificial Neural Network (ANN) models forecast streamflow more accurately than time series models did in the 1990s, they became increasingly popular. Artificial intelligence (AI), an offshoot of computer science, can analyse long-series and large-scale hydrological dat