Classification Model for Water Quality using Machine Learning Techniques

Muhammad, Salisu Yusuf; Makhtar, Mokhairi; Rozaimee, Azilawati; Aziz, Azwa Abdul; Jamal, Azrul Amri

doi:10.14257/ijseia.2015.9.6.05

Cited by 47 publications

(27 citation statements)

References 7 publications

(5 reference statements)

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“…Summary of the experiment 1 result using 53 attribute We generated 35 classification models with different classifiers and feature selection algorithms [4]. Experiments taken using ConsistencySubsetEval, CfsSubsetEval,…”

Section: Resultsmentioning

confidence: 99%

Selection of classification models from repository of model for water quality dataset

Makhtar

Muhammad

Rozaimee

et al. 2018

J. Fundam and Appl Sci.

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This paper proposes a new technique, Model Selection Technique (MST) for selection andranking of models from the repository of models by combining three performance measures (Acc, TPR and TNR). This technique provides weightage to each performance measure to find the most suitable model from the repository of models. A number of classification models have been generated to classify water quality using the most significant features and classifiers such as J48, JRip and BayesNet. To validate this technique proposed, the water quality dataset of Kinta River was used in this research. The results demonstrate that the Function classifier is the optimal model with the most outstanding accuracy of 97.02%, TPR = 0.96 and TNR = 0.98. In conclusion, MST is able to find the most relevant model from the repository of models by using weights in classifying the water quality dataset.

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Section: Resultsmentioning

confidence: 99%

Selection of classification models from repository of model for water quality dataset

Makhtar

Muhammad

Rozaimee

et al. 2018

J. Fundam and Appl Sci.

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show abstract

“…Before comparing the proposed model to other classification algorithm and model, it is analyzed the proposed model so that important features can be determined which are presented to classify Kinta River (Perak Malaysia) water quality. Finally, overall observations prove that lazy typesarchetypalenforcing the algorithm of K-star to classify WQ is agreatest algorithm [8].…”

Section: Salisuyusaf Et Al [2015]mentioning

confidence: 86%

AnEstimating Model for Water quality of river Ganga using Artificial Neural Network

Soni¹,

Sejwar²

2019

IJITEE

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In given propose paper we have worked on the water quality of river ganga, not only for management of water resources, but also for the prevention of water pollution, the water quality forecast has a more practical significance. To evolvesuitableideals for the water quality (WQ) insidethe water physiquesobtainingcontaminant samples & then to confirm that these standards are encountered, this is the environmentalWQ management program have goal. In the realistic standard setting, the institutional capacity of the basin’s water science, environmental, & the land usagesituations, possibleusages of getting water bodies, &the determination & implementation of WQ ideals has been kept in mind. In this paper, an efficient Machine learning algorithmwas modeled. A feed forward error back propagation neural network is implemented with different training functionsnamely trainlm(LevenbergMarquardtbackpropagation), trainb(Batch training with weight & bias learning rules), trainr(Random order incremental training w/learning functions) & trainbr(Bayesian regularization). Five sampling stations along Ganga River stretch were selected from DEVPRAYAG-to-ROORKEE city inside the Uttarakhand state of the India.These states are Bihar, Uttarakhand, Delhi, UP& West Bengal. The hill rivers of the Uttarakhand are Alkananda, Bhagirathi, Mandakini.We have used the above given training functions at different-different learning rate (i.e., 0.01,0.03,0.05,0.07&0.09) to measure classification rate& use the mean square method to measure the performance of the model. Results indicate that the proposed algorithm gives best estimating model and generate less mean square error (i.e., 0.004)and accuracy is 99.5% at 0.09 learning rate with trainbr training functionin respect to othertraining function.

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“…Most of these articles use different scientific methods, approaches and ML models to predict air quality. S. Y. Muhammed et al in [12] points out that machine learning algorithms are best suited for air quality prediction. Some of them are discussed below.…”

Section: Machine-learning Prediction Modelsmentioning

confidence: 99%

Air Quality Prediction: Big Data and Machine Learning Approaches

Kang¹,

Gao²,

Chiao³

et al. 2018

IJESD

144

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Abstract-Monitoring and preserving air quality has become one of the most essential activities in many industrial and urban areas today. The quality of air is adversely affected due to various forms of pollution caused by transportation, electricity, fuel uses etc. The deposition of harmful gases is creating a serious threat for the quality of life in smart cities. With increasing air pollution, we need to implement efficient air quality monitoring models which collect information about the concentration of air pollutants and provide assessment of air pollution in each area. Hence, air quality evaluation and prediction has become an important research area. The quality of air is affected by multi-dimensional factors including location, time, and uncertain variables. Recently, many researchers began to use the big data analytics approach due to advancements in big data applications and availability of environmental sensing networks and sensor data. The aim of this research paper is to investigate various big-data and machine learning based techniques for air quality forecasting. This paper reviews the published research results relating to air quality evaluation using methods of artificial intelligence, decision trees, deep learning etc. Furthermore, it throws light on some of the challenges and future research needs.Index Terms-Air quality evaluation, big data analytics, data-driven air quality evaluation, and air quality prediction.

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Classification Model for Water Quality using Machine Learning Techniques

Abstract: The problem of water pollution is increasing every day, due to the industries '

Cited by 47 publications

References 7 publications

Selection of classification models from repository of model for water quality dataset

Selection of classification models from repository of model for water quality dataset

AnEstimating Model for Water quality of river Ganga using Artificial Neural Network

Air Quality Prediction: Big Data and Machine Learning Approaches

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