An integrated dual-interpretable framework for nonstationary hydrostatic depth forecasting in residential zones under river–groundwater interactions

This publication has 31 references indexed in Scilit:

• Data-Driven Insights into Climate Change Effects on Groundwater Levels Using Machine Learning
  Water Resources Management, 2025
• Application of machine learning and deep learning for predicting groundwater levels in the West Coast Aquifer System, South Africa
  Earth Science Informatics, 2024
• Enhancing Groundwater Recharge Prediction: A Feature Selection‐Based Deep Forest Model With Bayesian Optimisation
  Hydrological Processes, 2024
• Bee-inspired insights: Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction
  Environmental Monitoring and Assessment, 2024
• Spatial predictions of groundwater potential using automated machine learning (AutoML): a comparative study of feature selection and training sample size in Qinghai Province, China
  Environmental Science and Pollution Research, 2023
• Assessing the changing trends of groundwater level with spatiotemporal scale at the northern part of Bangladesh integrating the MAKESENS and ARIMA models
  Modeling Earth Systems and Environment, 2023
• Shapley values for cluster importance
  Data Mining and Knowledge Discovery, 2022
• Assessment of composite beam performance using GWO–ELM metaheuristic algorithm
  Engineering with Computers, 2021
• Advances in understanding river‐groundwater interactions
  Reviews of Geophysics, 2017
• Groundwater–surface water interactions in a North German lowland floodplain – Implications for the river discharge dynamics and riparian water balance
  Journal of Hydrology, 2007