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  1. Home
  3. Estimation Of Seasonal Ecological Water Demand In Arid Zone Of Northwest China: An Approach Using The Lstm-random Forest Regression Model.
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  3. Estimation Of Seasonal Ecological Water Demand In Arid Zone Of Northwest China: An Approach Using The Lstm-random Forest Regression Model.

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Estimation of seasonal ecological water demand in arid zone of Northwest China: An approach using the LSTM-random

Chao Wang1, Qi Zhang2, Min Tao3

  • 1State Key Laboratory of Water Disaster Prevention, Yangtze Institute for Conservation and Development, Hohai University, Nanjing, 210024, China; College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China.

Journal of Environmental Management
|December 7, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study developed a new model to predict ecological water demand in arid regions, accounting for uncertainties. The findings highlight seasonal water needs in the Shiyang River Basin, crucial for sustainable water resource management.

Keywords:
Ecological water demandLSTM modelProbability correction coefficientRandom forest regression modelSen-MK trend analysis

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Area of Science:

  • Environmental Science
  • Hydrology
  • Water Resource Management

Background:

  • Arid regions face increasing water supply-demand conflicts, necessitating dynamic ecological water demand assessments.
  • Previous deterministic models failed to capture uncertainties from climate change and human activities.
  • A novel approach is needed to improve the accuracy and reliability of ecological water demand predictions.

Purpose of the Study:

  • To develop a coupled LSTM-Random Forest Regression model with Bayesian optimization for dynamic ecological water demand assessment.
  • To introduce a probability modification coefficient (PMC) for interval-range predictions, overcoming single-value limitations.
  • To analyze the spatiotemporal patterns of fractional vegetation cover (FVC) and their impact on ecological water demand.

Main Methods:

  • Constructed a coupled Long Short-Term Memory (LSTM)-Random Forest Regression model.
  • Integrated Bayesian optimization for model parameter tuning.
  • Applied the probability modification coefficient (PMC) for uncertainty quantification and interval prediction.
  • Utilized the Sen-Mann-Kendall (Sen-MK) trend test to analyze FVC spatiotemporal evolution.

Main Results:

  • The coupled model achieved high accuracy in FVC prediction (R² = 0.951 on the test set).
  • Ecological water demand in the lower Shiyang River Basin (SRB) showed significant seasonal variation.
  • Summer months require increased upstream water supply, while late spring offers high ecological benefits with low consumption.

Conclusions:

  • The proposed coupled model effectively addresses uncertainties in dynamic ecological water demand assessment in arid zones.
  • Prioritizing early spring water replenishment and summer buffer supply in the lower SRB is recommended.
  • The study provides a scientific basis for improved water resource management strategies in arid regions.