Ensemble machine learning paradigms in hydrology: A review
Recently, there has been a notable tendency towards employing ensemble learning
methodologies in assorted areas of engineering, such as hydrology, for simulation and …
methodologies in assorted areas of engineering, such as hydrology, for simulation and …
A review of deep learning and machine learning techniques for hydrological inflow forecasting
SD Latif, AN Ahmed - Environment, Development and Sustainability, 2023 - Springer
Conventional machine learning models have been widely used for reservoir inflow and
rainfall prediction. Nowadays, researchers focus on a new computing architecture in the …
rainfall prediction. Nowadays, researchers focus on a new computing architecture in the …
[HTML][HTML] Future trends of reference evapotranspiration in Sicily based on CORDEX data and Machine Learning algorithms
F Di Nunno, F Granata - Agricultural Water Management, 2023 - Elsevier
In years of increasing impact of climate change effects, a reliable characterization of the
spatiotemporal evolutionary dynamics of evapotranspiration can enable a significant …
spatiotemporal evolutionary dynamics of evapotranspiration can enable a significant …
Multiscale groundwater level forecasting: Coupling new machine learning approaches with wavelet transforms
Groundwater level (GWL) forecasting is crucial for irrigation scheduling, water supply and
land development. Machine learning (ML)(eg, artificial neural networks) has been …
land development. Machine learning (ML)(eg, artificial neural networks) has been …
A stacking ensemble learning model for monthly rainfall prediction in the Taihu Basin, China
The prediction of monthly rainfall is greatly beneficial for water resources management and
flood control projects. Machine learning (ML) techniques, as an increasingly popular …
flood control projects. Machine learning (ML) techniques, as an increasingly popular …
Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms
Floods and droughts are environmental phenomena that occur in Peninsular Malaysia due
to extreme values of streamflow (SF). Due to this, the study of SF prediction is highly …
to extreme values of streamflow (SF). Due to this, the study of SF prediction is highly …
Medium-and long-term precipitation forecasting method based on data augmentation and machine learning algorithms
Accurate medium and long-term precipitation forecasting plays a vital role in disaster
prevention and mitigation and rational allocation of water resources. In recent years, there …
prevention and mitigation and rational allocation of water resources. In recent years, there …
Covariance matrix adaptation evolution strategy for improving machine learning approaches in streamflow prediction
Precise streamflow estimation plays a key role in optimal water resource use, reservoirs
operations, and designing and planning future hydropower projects. Machine learning …
operations, and designing and planning future hydropower projects. Machine learning …
Spatio-temporal analysis of drought in Southern Italy: a combined clustering-forecasting approach based on SPEI index and artificial intelligence algorithms
F Di Nunno, F Granata - Stochastic Environmental Research and Risk …, 2023 - Springer
A reliable prediction of the spatio-temporal drought variation can lead to a reduction in
vulnerability and an improvement in the management of drought-dependent businesses. In …
vulnerability and an improvement in the management of drought-dependent businesses. In …
[HTML][HTML] Development of an enhanced bidirectional recurrent neural network combined with time-varying filter-based empirical mode decomposition to forecast weekly …
Evapotranspiration is one of agricultural water management's most significant and impactful
hydrologic processes. A new multi-decomposition deep learning-based technique is …
hydrologic processes. A new multi-decomposition deep learning-based technique is …