Advanced hybrid frameworks for water quality index prediction

IF 6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Ain Shams Engineering Journal Pub Date : 2025-05-13 DOI:10.1016/j.asej.2025.103478

Mohammad Ehteram , Somayeh Soltani-Gerdefaramarzi

引用次数: 0

Abstract

The water quality index (WQI) is a critical parameter that must be accurately predicted to ensure the sustainable management of water resources. Thus, our study develops the sine cosine optimization algorithm (SCOA)- long short-term memory (LSTM) − Extreme gradient boosting (XGBoost), SCOA- LSTM − least square support vector machine (LSSVM), crow optimization algorithm (COA)- LSTM-XGBoost, and COA-LSTM-LSSVM models to predict WQI in Aidoghmoush river, Iran. First, COA and SCOA adjust the parameters of LSTM, LSSVM, and XGBoost. Then, LSTM captures temporal patterns in the time series data, which include water quality parameters. Finally, the LSSVM and XGBoost models use the captured patterns to make final predictions. Our results demonstrate that the SCOA-LSTM-XGBoost model achieves a Willmott’s index (WI) of 0.96, an explained variance score (EVS) of 0.95, and a t-statistic (TS) of 0.021. The results of our paper show that SCOA-LSTM-XGBoost is a reliable model for predicting WQI.

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水质指数预测的先进混合框架

水质指数（WQI）是保证水资源可持续管理必须准确预测的关键参数。因此，我们的研究开发了正弦余弦优化算法(SCOA)-长短期记忆(LSTM) -极限梯度增强（XGBoost）， SCOA- LSTM-最小二乘支持向量机（LSSVM）， crow优化算法(COA)- LSTM-XGBoost和COA-LSTM-LSSVM模型来预测伊朗Aidoghmoush河的WQI。首先，COA和SCOA调整LSTM、LSSVM和XGBoost的参数。然后，LSTM捕获时间序列数据中的时间模式，其中包括水质参数。最后，LSSVM和XGBoost模型使用捕获的模式进行最终预测。结果表明，SCOA-LSTM-XGBoost模型的Willmott指数（WI）为0.96，解释方差得分（EVS）为0.95，t统计量（TS）为0.021。研究结果表明，SCOA-LSTM-XGBoost是一种可靠的WQI预测模型。

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

Ain Shams Engineering Journal Engineering-General Engineering

CiteScore

10.80

自引率

13.30%

发文量

441

审稿时长

49 weeks

期刊介绍： in Shams Engineering Journal is an international journal devoted to publication of peer reviewed original high-quality research papers and review papers in both traditional topics and those of emerging science and technology. Areas of both theoretical and fundamental interest as well as those concerning industrial applications, emerging instrumental techniques and those which have some practical application to an aspect of human endeavor, such as the preservation of the environment, health, waste disposal are welcome. The overall focus is on original and rigorous scientific research results which have generic significance. Ain Shams Engineering Journal focuses upon aspects of mechanical engineering, electrical engineering, civil engineering, chemical engineering, petroleum engineering, environmental engineering, architectural and urban planning engineering. Papers in which knowledge from other disciplines is integrated with engineering are especially welcome like nanotechnology, material sciences, and computational methods as well as applied basic sciences: engineering mathematics, physics and chemistry.