Interpretable machine learning guided by physical mechanisms reveals drivers of runoff under dynamic land use changes

This publication has 59 references indexed in Scilit:

• Land use/land cover changes and its impact on runoff using SWAT model in the upper Prek Thnot watershed in Cambodia
  Environmental Earth Sciences, 2022
• Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP
  Accident Analysis & Prevention, 2021
• Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach
  Engineering Structures, 2020
• Comparing the effects of dynamic versus static representations of land use change in hydrologic impact assessments
  Environmental Modelling & Software, 2019
• Effects of the land use/cover on the surface runoff and soil loss in the Niğde-Akkaya Dam Watershed, Turkey
  CATENA, 2018
• Individual and combined impacts of future climate and land use changes on the water balance
  Ecological Engineering, 2017
• From spatially variable streamflow to distributed hydrological models: Analysis of key modeling decisions
  Water Resources Research, 2016
• Physically based modeling in catchment hydrology at 50: Survey and outlook
  Water Resources Research, 2015
• An overview of methods to evaluate uncertainty of deterministic models in decision support
  Environmental Modelling & Software, 2015
• Wavelet Regression Model as an Alternative to Neural Networks for River Stage Forecasting
  Water Resources Management, 2010