WaterQualityNeT: Prediction of Seasonal Water Quality of Nepal Using Hybrid Deep Learning Models

Biplov Paneru, Bishwash Paneru
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Abstract

Ensuring a safe and uncontaminated water supply is contingent upon the monitoring of water quality, especially in developing countries such as Nepal, where water sources are susceptible to pollution. This paper presents a hybrid deep learning model for predicting Nepal's seasonal water quality using a small dataset with many water quality parameters. The model integrates convolutional neural networks (CNN) and recurrent neural networks (RNN) to exploit temporal and spatial patterns in the data. The results demonstrate significant improvements in forecast accuracy over traditional methods, providing a reliable tool for proactive control of water quality. The model that used WQI parameters to classify people into good, poor, and average groups performed 92% of the time in testing. Similarly, the R2 score was 0.97 and the root mean square error was 2.87 when predicting WQI values using regression analysis. Additionally, a multifunctional application that uses both a regression and a classification approach is built to predict WQI values.
WaterQualityNeT:利用混合深度学习模型预测尼泊尔的季节性水质
确保安全和不受污染的供水取决于对水质的监测,尤其是在尼泊尔等水源易受污染的发展中国家。本文介绍了一种混合深度学习模型,该模型利用包含许多水质参数的小型数据集预测尼泊尔的季节性水质。该模型整合了卷积神经网络(CNN)和循环神经网络(RNN),以利用数据中的时间和空间模式。结果表明,与传统方法相比,该模型的预测准确率有了明显提高,为主动控制水质提供了可靠的工具。使用 WQI 参数将人们分为好、差和一般组的模型在测试中的表现达到了 92%。同样,在使用回归分析预测 WQI 值时,R2 得分为 0.97,均方根误差为 2.87。
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