{"title":"WaterQualityNeT: Prediction of Seasonal Water Quality of Nepal Using Hybrid Deep Learning Models","authors":"Biplov Paneru, Bishwash Paneru","doi":"arxiv-2409.10898","DOIUrl":null,"url":null,"abstract":"Ensuring a safe and uncontaminated water supply is contingent upon the\nmonitoring of water quality, especially in developing countries such as Nepal,\nwhere water sources are susceptible to pollution. This paper presents a hybrid\ndeep learning model for predicting Nepal's seasonal water quality using a small\ndataset with many water quality parameters. The model integrates convolutional\nneural networks (CNN) and recurrent neural networks (RNN) to exploit temporal\nand spatial patterns in the data. The results demonstrate significant\nimprovements in forecast accuracy over traditional methods, providing a\nreliable tool for proactive control of water quality. The model that used WQI\nparameters to classify people into good, poor, and average groups performed 92%\nof the time in testing. Similarly, the R2 score was 0.97 and the root mean\nsquare error was 2.87 when predicting WQI values using regression analysis.\nAdditionally, a multifunctional application that uses both a regression and a\nclassification approach is built to predict WQI values.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.10898","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.