Modeling of monthly rainfall–runoff using various machine learning techniques in Wadi Ouahrane Basin, Algeria
Rainfall–runoff modeling has been the core of hydrological research studies for decades. To
comprehend this phenomenon, many machine learning algorithms have been widely used …
comprehend this phenomenon, many machine learning algorithms have been widely used …
Interpretable multi-step hybrid deep learning model for karst spring discharge prediction: Integrating temporal fusion transformers with ensemble empirical mode …
Karst groundwater is a critical freshwater resource for numerous regions worldwide.
Monitoring and predicting karst spring discharge is essential for effective groundwater …
Monitoring and predicting karst spring discharge is essential for effective groundwater …
Predicting and explaining karst spring dissolved oxygen using interpretable deep learning approach
Dissolved oxygen (DO) is one of the most important indicators of water quality and an
essential measure for the aquatic organisms and the local ecosystem. DO concentrations in …
essential measure for the aquatic organisms and the local ecosystem. DO concentrations in …
Linear and nonlinear ensemble deep learning models for karst spring discharge forecasting
Forecasting karst spring discharge is crucial for groundwater resource management in karst
aquifers. These aquifers, with their inherent heterogeneity and complexity influenced by a …
aquifers. These aquifers, with their inherent heterogeneity and complexity influenced by a …
Enhancing water use efficiency in precision irrigation: data-driven approaches for addressing data gaps in time series
Real-time soil matric potential measurements for determining potato production's water
availability are currently used in precision irrigation. It is well known that managing irrigation …
availability are currently used in precision irrigation. It is well known that managing irrigation …
On the role of the architecture for spring discharge prediction with deep learning approaches
Understanding karst spring flow is important to accommodate the increasing water demand
caused by the population growth and manage the freshwater water resource effectively …
caused by the population growth and manage the freshwater water resource effectively …
Comparative performance assessment of physical-based and data-driven machine-Learning models for simulating streamflow: a case study in three catchments …
Recent developments in computational techniques and data-driven machine-learning
models (MLMs) have shown great potential in capturing the rainfall-runoff relationship …
models (MLMs) have shown great potential in capturing the rainfall-runoff relationship …
Hybrid Multivariate Machine Learning Models for Streamflow Forecasting: A Two-Stage Decomposition–Reconstruction Framework
Robust and accurate streamflow forecasting holds significant importance for flood mitigation,
drought warning and water resource management. On account of the intricate nonlinear and …
drought warning and water resource management. On account of the intricate nonlinear and …
Pan evaporation forecasting using empirical and ensemble empirical mode decomposition (EEMD) based data-driven models in the Euphrates sub-basin, Turkey
C Sezen - Earth Science Informatics, 2023 - Springer
Forecasting evaporation, an important variable in the hydrological cycle, is crucial for
managing water resources and taking precautions against severe phenomena, such as …
managing water resources and taking precautions against severe phenomena, such as …
A hybrid self-adaptive DWT-WaveNet-LSTM deep learning architecture for karst spring forecasting
Karst spring discharge plays a vital role in understanding karst systems and managing karst
groundwater resources. Due to its inherent heterogeneity and complexity, hydrological …
groundwater resources. Due to its inherent heterogeneity and complexity, hydrological …