A Study of KNN Classifier to Predict Water Pollution Index

Mohurle, Savita; Devare, Manoj

doi:10.1007/978-981-32-9515-5_44

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…The detection attributes used in the study included BOD, COD, NH3, fecal coliform, and total coliform. Mohurle [20] focused on the issue of drinking water contamination. The study analyzed the current status of drinking water quality and the fundamentals of the K-Nearest Neighbor (KNN) classifier.…”

Section: K-nearest Neighbor (Knn)mentioning

confidence: 99%

Comparative Study of Algorithms Used in Water Pollution Prevention and Control

Wang,

Liu

2023

Frontiers in Artificial Intelligence and Applications

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Water pollution prevention and control is crucial to ensure the safety of water environment and human health, and various types of algorithms play an important role in it. We introduce the history and algorithm overview of various algorithms in water pollution prevention and control, analyze the current research status and recent research results in this field, compare and evaluate the advantages and disadvantages of various algorithms, and focus on the following algorithms: neural network, convolutional neural network, decision tree, random forest, naive Bayes, SVM, K-Means, and AdaBoost. Through the comparative analysis of these algorithms, we hope to provide a more effective method for water pollution prevention and control.

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Section: K-nearest Neighbor (Knn)mentioning

confidence: 99%

Comparative Study of Algorithms Used in Water Pollution Prevention and Control

Wang,

Liu

2023

Frontiers in Artificial Intelligence and Applications

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“…The KNN has proven to be a nonparametric method for classi cation and regression tasks [29]. The concept of this method is to select the k-closest neighbors to the studied point in order to predict its value.…”

Section: K-nearest Neighbors (Knn)mentioning

confidence: 99%

Total phosphorus removal in multi-soil-layering nature-based technology: Evaluation of influencing factors and prediction using data-driven methods

Sbahi

Mandi

Ouazzani

et al. 2022

Preprint

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Excess phosphorus (P) in wastewater can produce eutrophication, posing a serious risk to the safety of water resources and ecosystems. Therefore, effective pollutant removal including P from wastewater is the key strategy to save the environment and public health. Multi-soil-layering (MSL) is a promising nature-based technology that mainly relies on a soil mixture containing iron to remove P-pollution from wastewater. In the MSL influent, fourteen water quality indicators were measured, including pH, dissolved oxygen, total suspended solids, electrical conductivity, organic matter, nutrients, and coliform bacteria, to determine which ones have the strongest relationship with total phosphorus (TP) removal. The influence of hydraulic loading rate (HLR) and climatic variables (air temperature, rainfall, and evaporation) on the removal of TP was investigated. Four data-driven methods including multiple linear regression (MLR), k-nearest neighbors (KNN), random forest (RF), and neural network (NN) were conducted to predict TP removal at the MSL system outlet. In contrast to climatic variables, the results reveal that the HLR has a significant impact (p < 0.05) on TP removal (47% − 90%) in the MSL system. Furthermore, using a feature selection technique, the HLR, pH, PO43− and TP were suggested as the relevant input variables affecting TP removal in the MSL system, while an examination of accuracy shows that the RF model achieves good prediction accuracy (R2 = 0.93) and can help to understand MSL behavior for pollutants.

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“…For example, [83] proposes a tenfold approach for cross-validation to obtain optimal k values. The applications of kNN in SWN are to classify drinking water quality, predict water pollution index [52], detect water pipe leakage [53], and so on. Reference [54] uses kNN to control nutrient levels in aquaponics.…”

Section: B Supervised MLmentioning

confidence: 99%

Cyber-Physical Systems for Smart Water Networks: A Review

et al. 2021

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There is a growing demand to equip Smart Water Networks (SWN) with advanced sensing and computation capabilities in order to detect anomalies and apply autonomous event-triggered control. Cyber-Physical Systems (CPSs) have emerged as an important research area capable of intelligently sensing the state of SWN and reacting autonomously in scenarios of unexpected crisis development. Through computational algorithms, CPSs can integrate physical components of SWN, such as sensors and actuators, and provide technological frameworks for data analytics, pertinent decision making, and control. The development of CPSs in SWN requires the collaboration of diverse scientific disciplines such as civil, hydraulics, electronics, environment, computer science, optimization, communication, and control theory. For efficient and successful deployment of CPS in SWN, there is a need for a common methodology in terms of design approaches that can involve various scientific disciplines. This paper reviews the state of the art, challenges, and opportunities for CPSs, that could be explored to design the intelligent sensing, communication, and control capabilities of CPS for SWN. In addition, we look at the challenges and solutions in developing a computational framework from the perspectives of machine learning, optimization, and control theory for SWN.

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A Study of KNN Classifier to Predict Water Pollution Index

Cited by 3 publications

References 3 publications

Comparative Study of Algorithms Used in Water Pollution Prevention and Control

Comparative Study of Algorithms Used in Water Pollution Prevention and Control

Total phosphorus removal in multi-soil-layering nature-based technology: Evaluation of influencing factors and prediction using data-driven methods

Cyber-Physical Systems for Smart Water Networks: A Review

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