Water quality prediction using ARIMA-SSA-LSTM combination model

Water Supply Pub Date : 2024-03-28 DOI:10.2166/ws.2024.060

Tingyu Wang, Wei Chen, Bo Tang

引用次数: 0

Abstract

The water quality index model is a popular tool for evaluating drinking water quality. To overcome low precision and significant errors in the traditional single prediction model, a novel autoregressive integrated moving average (ARIMA)-sparrow search algorithm (SSA)-long short-term memory (LSTM) combination model is proposed to accurately predict residual chlorine, turbidity, and pH in drinking water. First, the ARIMA model is used to extract the linear part of water quality data and output the nonlinear residual. Then, the LSTM model is used to predict the residual, and the SSA is used to find the optimal hyperparameters of the LSTM model, which plays an essential role in reducing the error of the model. To prove the superiority of the model developed, the ARIMA-SSA-LSTM model is compared with SSA-LSTM, whale optimization algorithm-LSTM, PSO-LSTM, ARIMA-LSTM, ARIMA, and LSTM. The results show that the coefficient of determination (R2) of the combination model for residual chlorine, turbidity, and pH are 0.950, 0.990, and 0.998, respectively, which are greater than all comparison models. Therefore, the model is more suitable for the prediction and analysis of water quality data.

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利用 ARIMA-SSA-LSTM 组合模型进行水质预测

水质指数模型是评价饮用水水质的常用工具。为了克服传统单一预测模型精度低、误差大的问题，本文提出了一种新型的自回归积分移动平均（ARIMA）-麻雀搜索算法（SSA）-长短期记忆（LSTM）组合模型，用于准确预测饮用水中的余氯、浊度和 pH 值。首先，使用 ARIMA 模型提取水质数据的线性部分，并输出非线性残差。然后，利用 LSTM 模型预测残差，并利用 SSA 找到 LSTM 模型的最优超参数，这对减少模型误差起着至关重要的作用。为了证明所建立模型的优越性，将 ARIMA-SSA-LSTM 模型与 SSA-LSTM、鲸鱼优化算法-LSTM、PSO-LSTM、ARIMA-LSTM、ARIMA 和 LSTM 进行了比较。结果表明，组合模型对余氯、浊度和 pH 的判定系数（R2）分别为 0.950、0.990 和 0.998，大于所有比较模型。因此，该模型更适合预测和分析水质数据。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Water Supply

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