Forecasting groundwater quality using automatic exponential smoothing model (AESM) in Xianyang City, China

Nsabimana, Abel; Wu, Jianhua; Wu, Jitian; Xu, Fei

doi:10.1080/10807039.2022.2087176

Cited by 12 publications

(3 citation statements)

References 67 publications

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“…In a case study-based model to predict the groundwater quality index of Rafsanian Plain, authors used electrical conductivity, total hardness, total dissolved solid, pH, chloride, bicarbonate, sulfate, phosphate, calcium, magnesium, potassium, and sodium as the inputs [19]. In a novel prediction-based model [20], authors analyzed the increase in WQI for nine wells. They found that in five wells, the WQI increased significantly due to the presence of Mn, NH 4 , and NO 3 .…”

Section: Introductionmentioning

confidence: 99%

Identifying the Most Discriminative Parameter for Water Quality Prediction Using Machine Learning Algorithms

Chatterjee,

Gogoi,

Samanta

et al. 2024

Water

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Groundwater quality is one of the major concerns. Quality of the groundwater directly impacts human health, growth of plants and vegetables. Due to the severe impacts of inadequate water quality, it is imperative to find a swift and economical solution. Water quality prediction may help us to manage water resources properly. The present study has been carried out considering thirty-seven water sample data points form the Pindrawan tank command area of Raipur district, Chhattisgarh, India. A total of nineteen physicochemical parameters were measured, out of which seventeen parameters were used to compute the weight-based groundwater quality index (WQI). In this present work, the primary goal is to identify the most effective parameters for WQI prediction. Out of the seventeen parameters tested, the Mann—Whitney—Wilcoxon (MWW) statistical test has revealed that five parameters Fe, Cr, Na, Ca, and Mg hold a strong statistical significance in distinguishing between drinkable and non-drinkable water. Out of these five parameters, Cr is the only parameter that maintains a different range of values for drinkable water and non-drinkable water. To validate the efficiency of these statistically significant parameters, machine learning techniques like Artificial Neural Networks (ANN) and Logistic Regression (LR) were used. The experimental results clearly demonstrate that out of all the seventeen parameters tested, utilizing only Cr yields remarkably high classification accuracy. ‘Cr’ achieved an accuracy of 91.67% using artificial neural networks. This is much higher than the accuracy of 66.67% obtained using a parameter set with all seventeen parameters. The proposed methodology achieved good accuracy when classifying water samples into drinkable and non-drinkable water using only one parameter, ‘Cr’.

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Section: Introductionmentioning

confidence: 99%

Identifying the Most Discriminative Parameter for Water Quality Prediction Using Machine Learning Algorithms

Chatterjee,

Gogoi,

Samanta

et al. 2024

Water

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“…Moreover, the groundwater quality can also be affected depending upon the type of pumping technique employed [2]. Several researchers have employed various techniques to assess the quality of groundwater for drinking purposes, for instance, multivariate statistics [3], an automatic exponential smoothing model [4], explanatory analysis [5], theoretical probability models [6,7], correlation and regression analyses [8], and other methods. However, groundwater quality is influenced by a complex interplay of physicochemical parameters, including electric conductivity (EC), pH, total dissolved solids (TDS), and concentrations of various ions and minerals.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning and Deep Learning Models for Groundwater Potability Classification

Suleiman,

Yousafzai,

Zubair

2023

The 4th International Electronic Conference on Applied Sciences

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“…2 Aside from environmental factors, including rainfall, sedimentary rock, erosion, etc., anthropogenic variables such as urbanization, industries, and agricultural practices affect water quality. 3 Their delicate ecosystem must balance environmental stability with their surroundings, especially in the context of human encroachment and pollution. 4 The principal causes contributing to the degradation of reservoir conditions can be classified into two categories: (a) contaminants that originate from fixed point sources (pollutants through industrial wastes and water runoff).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Water Quality Index and Non-Carcinogenic Risk for Ingestion of Nitrate for Drinking Purpose of Bhosga Reservoir, Karnataka, India

Sarikar

Vijaykumar

2022

Curr. World Environ

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Reservoirs are vital water sources that contribute significantly to local ecological and environmental balance, particularly in semi-arid regions. An evaluation of the drinking water quality index (WQI) has been conducted using ten water parameters like electrical conductivity (EC), chloride (Cl-), pH, total hardness (TH), total alkalinity (TA), phosphate (PO43-), dissolved oxygen (DO), nitrate (NO3-), total dissolved solids (TDS), and sulfate (SO42-). Weighed Arithmetic Index technique is utilized for examining drinking water quality status and USEPA model was utilized to determine the non-carcinogenic risk for ingestion of nitrate of Bhosga reservoir of Kalaburagi district which was measured at five predefined sites from October 2020 to September 2021. All the parameters of potable water were below the allowable limits based on given standards. The maximum WQI results were reported in monsoon season, with a mean WQI value of 48.14 from all the five selected sites, with the pre & post-monsoon seasons a mean WQI result of 46.08 and 43.68, respectively. Overall, the WQI value signifies that the water was of good quality, making it worthy for domestic use. The outcome of this study reveals that the pH and DO position have a substantial impact on reservoir WQI. Non-carcinogenic risk for nitrate ingestion reveled that, seasonal HQ average results for adults and children are ranging between 0.036 to 0.040 and 0.065 to 0.071, respectively and the HQ results for both adults and children were below 1, indicating neither adults nor children had any negative effect. Principal component analysis and cluster analysis revealed that primary causes of water quality degradation were agricultural runoff and anthropogenic activities. To ensure the security and the quality of water supply to people who live in the surrounding region, simple filter treating of reservoir water prior usage is needed.

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Forecasting groundwater quality using automatic exponential smoothing model (AESM) in Xianyang City, China

Cited by 12 publications

References 67 publications

Identifying the Most Discriminative Parameter for Water Quality Prediction Using Machine Learning Algorithms

Identifying the Most Discriminative Parameter for Water Quality Prediction Using Machine Learning Algorithms

Comparative Analysis of Machine Learning and Deep Learning Models for Groundwater Potability Classification

Assessment of Water Quality Index and Non-Carcinogenic Risk for Ingestion of Nitrate for Drinking Purpose of Bhosga Reservoir, Karnataka, India

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