Water 2025, 17(10), 1465; https://doi.org/10.3390/w17101465 (registering DOI) - 13 May 2025
Abstract
Accurate runoff forecasting is crucial for disaster prevention and mitigation, as well as water resource allocation planning. However, the accuracy of runoff forecasting in high mountain watersheds is limited by the complexity of terrain and the scarcity of observation data. In recent years,
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Accurate runoff forecasting is crucial for disaster prevention and mitigation, as well as water resource allocation planning. However, the accuracy of runoff forecasting in high mountain watersheds is limited by the complexity of terrain and the scarcity of observation data. In recent years, machine learning models have been widely used for runoff prediction. In order to explore the application effect of the Long Short-Term Memory (LSTM) network in high mountain watersheds, this paper takes the Nyang River Basin (NRB) in a typical watershed on the Qinghai–Tibet Plateau (QTP) as the research object, and uses LSTM models to study the impact of different input variable combinations on runoff prediction under multiple prediction periods. The results indicate that with the extension of the forecast period, the impact of historical runoff on runoff prediction accuracy gradually decreases, while the impact of precipitation and temperature on runoff prediction accuracy gradually increases. When the forecast period exceeds 13 days, the contribution of precipitation increases more significantly. The use of historical runoff and forecasting that includes historical runoff and precipitation yields the most robust results, with good forecasting performance within 25 days of the forecast period. Moreover, the larger the watershed area, the better the runoff forecasting effect.
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(This article belongs to the Section Hydrology)
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