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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Applications of Physics-Guided Machine Learning Architectures in Hydrology,tical forms. According to a few recent studies, deep-machine learning-based models that come under the category of data-driven models outperform the well-established conceptual hydrological models. These studies reported that the deep-learning models can better capture the information available in t

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Estimation of Groundwater Levels Using Machine Learning Techniques,estimation. In addition, several studies from the recent past indicate the dominance of Ensemble Machine Learning in managing the sustainability of groundwater across the globe. So, the ability of ensemble machine learning models in estimating the groundwater level is discussed in the chapter. Furth

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Predictive Deep Learning Models for Daily Suspended Sediment Load in the Missouri River, USA,or 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