Water Price Prediction for Increasing Market Efficiency Using Random Forest Regression: A Case Study in the Western United States

@article{Xu2019WaterPP,
  title={Water Price Prediction for Increasing Market Efficiency Using Random Forest Regression: A Case Study in the Western United States},
  author={Ziyao Xu and J. Lian and Lingling Bin and Kaixun Hua and K. Xu and Hoi Yi Chan},
  journal={Water},
  year={2019},
  volume={11},
  pages={228}
}
  • Ziyao Xu, J. Lian, +3 authors Hoi Yi Chan
  • Published 2019
  • Geology
  • Water
  • The existence of water markets establishes water prices, promoting trading of water from low- to high-valued uses. However, market participants can face uncertainty when asking and offering prices because water rights are heterogeneous, resulting in inefficiency of the market. This paper proposes three random forest regression models (RFR) to predict water price in the western United States: a full variable set model and two reduced ones with optimal numbers of variables using a backward… CONTINUE READING
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