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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Model-Based User Interface Reengineering,for model evaluation. Results show that both models perform well but Bi-LSTM is slightly better than LSTM in terms of both statistical measures. These results of both models are with different values of hyperparameters. The performance of these models can be different with the same values of hyperpa

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GLOOM

Franco Dammacco,Domenico Sansonnod that linear ranked selected GA-ANN is the best-fitted model for both training and testing data. The CSS obtained for optimized input values for clay fraction by weight (CP), weighted geometric standard deviation of sediment mixture (SM), and dimensionless dry bulk unit weight of cohesive sediment

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Erythropoietin

Franco Dammacco,Domenico Sansonnoor of 0.142, compared to LSTM’s coefficient of determination of 0.865 and root mean square error of 0.148. GRU also had a lower mean absolute error of 0.097 compared to LSTM’s mean absolute error of 0.101. The study concludes that both GRU and LSTM can be used effectively in SSL modeling. However, G

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https://doi.org/10.1007/978-4-431-67005-6f each dataset in the case of extreme rainfalls with respect to IMDAA gridded rainfall (i.e., IMD Re-analysis), which has been taken as the observed/reference dataset. In this study, the quantile mapping and linear scaling bias correction methods are utilized to correct the rainfall datasets. The pr

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2520-1298 s the impact of climate change on flood risks, drought occurrences, and reservoir operations, providing insights into how these phenomena affect water resource management...To provide practical solutions, the b978-3-031-64405-4978-3-031-64403-0Series ISSN 2520-1298 Series E-ISSN 2520-1301

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