Enhanced Water Quality Prediction in the Yellow River Basin: The Application of the HHO-LSTM Model

Minning Wu, Eric B. Blancaflor, Fei Ren, Yong Wang, Ting Dong
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Abstract

In the pivotal water resource region of the Yellow River Basin in China, precise prediction of water resources is essential for their effective and rational management. This study introduces a novel approach to water resource prediction by employing the Harris Hawks Optimization-Long Short-Term Memory (HHO-LSTM) model. This method overcomes the constraints faced by traditional techniques in processing time series data and various variable factors. It encompasses a comprehensive description of the multi-source hydrological data collection process within the Yellow River Basin, followed by meticulous data preprocessing. The data set for this study includes estimates of four critical water quality parameters, and the efficacy of the model is gauged through the mean squared error (MSE) and root mean squared error (RMSE) metrics. This facilitates the projection of future water quality trends in specific areas by leveraging historical water quality data. The HHO-LSTM model has demonstrated outstanding accuracy and robustness in predicting water quality across diverse temporal scales and water resource variables, marking a significant advancement in water resource management within the Yellow River Basin. This approach not only enhances current management strategies but also contributes valuable insights for ongoing water resource research and decision-making processes.
黄河流域水质强化预测:HHO-LSTM 模型的应用
在中国黄河流域这一水资源枢纽地区,精确的水资源预测对于有效合理地管理水资源至关重要。本研究采用哈里斯-霍克斯优化-长短期记忆(HHO-LSTM)模型,提出了一种新的水资源预测方法。该方法克服了传统技术在处理时间序列数据和各种可变因素时所面临的限制。它包括对黄河流域多源水文数据收集过程的全面描述,以及细致的数据预处理。本研究的数据集包括四个关键水质参数的估算值,并通过均方误差 (MSE) 和均方根误差 (RMSE) 指标来衡量模型的有效性。这有助于利用历史水质数据预测特定区域的未来水质趋势。HHO-LSTM 模型在预测不同时间尺度和水资源变量的水质方面表现出卓越的准确性和鲁棒性,标志着黄河流域水资源管理的重大进步。这种方法不仅增强了当前的管理策略,还为正在进行的水资源研究和决策过程提供了宝贵的见解。
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