Water Research|使用机器学习方法优化水质指数模型 Optimization of water quality index models using machine learning approaches 出版:Water Research 作者:Fei Ding , Wenjie Zhang , Shaohua Cao , Shilong Hao , Liangyao Chen , Xin Xie, Wenpan Li, Mingcen Jiang 原文链接: 使用机器学习方法优化水质指数模...
DEEP learningMany human-made activities currently pollute groundwater supplies, with mining operations playing a substantial role in this degradation. In this study, water quality index (WQI) was calculated and forecasted for groundwater in gold mining sites of Kolar Gold Fields, Karnataka, using ...
The water quality index (WQI) has been used to identify threats to water quality and to support better water resource management. This study combines a machine learning algorithm, WQI, and remote sensing spectral indices (difference index, DI; ratio index, RI; and normalized difference index, ND...
Improvingpredictionofwaterqualityindicesusingnovelhybrid machine-learningalgorithms DuieTienBui a,b ,KhabatKhosravi c ,JohnTiefenbacher d ,HoangNguyen e, ⁎,NerantzisKazakis f, ⁎ a GeographicInformationScienceResearchGroup,TonDucThangUniversity,HoChiMinhCity,VietNam b FacultyofEnvironmentandLabourSafety,...
They also used seven machine learning algorithms for comparison. Experimental results show that the proposed nine-layer MLP achieved an accuracy value of 99% for water-quality prediction using the KNN imputer. A dependable approach was proposed by Nida Nasir et al. [4] for predicting water ...
Third, the weighting factor for each water quality parameter is determined and fourth, a final single value water quality index is calculated by an aggregation function using the sub-indices and weighting factors for all water quality parameters. Many different WQI models have been developed with ...
The results of this study have also demonstrated that the machine learning models are efficient tools for accurately predicting the quality of irrigation water by only using the parameters that can be directly measured in a short time. Consequently, the implementation of the automated sensor ...
0.1 indicate good prediction performance for all the indices except RSC. These results highlight potential of using multiple regressions and the developed machine learning methods in predicting the index of irrigation water quality, and can be rapid decision tools for modelling irrigation water quality....
Understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in the future. These challenges arise partly due to complex processes that regulate water quality, and arduou
Although several studies have forecasted drinking water quality using machine learning models (e.g., MR, ANNs, and HCs, etc.) (Nadiri et al., 2013; Chen and Liu 2015; Abba et al., 2017; Alizamir and Sobhanardakani, 2017; Nhantumbo et al., 2018; Wagh et al., 2017, 2018; Kadam...