Analysis and prediction of water quality using deep learning and auto deep learning techniques

Prasad, D. Venkata Vara; Venkataramana, Lokeswari; Kumar, P. Senthil; Prasannamedha, G.; Harshana, S.; Srividya, S; Harrinei, K.; Indraganti, Sravya

doi:10.1016/j.scitotenv.2022.153311

Cited by 64 publications

(9 citation statements)

References 22 publications

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“…Briefly, conventional deep learning performed better than auto deep learning for binary and multiclass classification. Artificial neural networks achieved 86% and 77%, recurrent neural networks generated 87% and 89%, and long short-term memory scored 92% and 94% for binary and multiclass classification, respectively [ 74 ]. As can be seen manifested in our results, decision forest, decision jungle and boosted decision tree achieved satisfactory scores in accuracy and precision metrics with and without cross-validation.…”

Section: Resultsmentioning

confidence: 99%

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

Tselemponis,

Stefanis,

Giorgi

et al. 2023

IJERPH

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In this study, machine learning models were implemented to predict the classification of coastal waters in the region of Eastern Macedonia and Thrace (EMT) concerning Escherichia coli (E. coli) concentration and weather variables in the framework of the Directive 2006/7/EC. Six sampling stations of EMT, located on beaches of the regional units of Kavala, Xanthi, Rhodopi, Evros, Thasos and Samothraki, were selected. All 1039 samples were collected from May to September within a 14-year follow-up period (2009–2021). The weather parameters were acquired from nearby meteorological stations. The samples were analysed according to the ISO 9308-1 for the detection and the enumeration of E. coli. The vast majority of the samples fall into category 1 (Excellent), which is a mark of the high quality of the coastal waters of EMT. The experimental results disclose, additionally, that two-class classifiers, namely Decision Forest, Decision Jungle and Boosted Decision Tree, achieved high Accuracy scores over 99%. In addition, comparing our performance metrics with those of other researchers, diversity is observed in using algorithms for water quality prediction, with algorithms such as Decision Tree, Artificial Neural Networks and Bayesian Belief Networks demonstrating satisfactory results. Machine learning approaches can provide critical information about the dynamic of E. coli contamination and, concurrently, consider the meteorological parameters for coastal waters classification.

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Section: Resultsmentioning

confidence: 99%

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

Tselemponis,

Stefanis,

Giorgi

et al. 2023

IJERPH

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“…The primary focus of this study by Prasad, et al [15] is to develop a water quality forecasting model that utilizes reliable and accurate data. As the production of big data from IoT-based smart WQ monitoring systems continues to increase, the complexity of WQ data has become more pronounced.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Comparative Study of Machine Learning Algorithms for Water Potability Classification

Ahmad Musleh

2024

IJCDS

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Water quality (WQ) prediction is of utmost importance due to the scarcity of uncontaminated water resources. In this study, six machine learning (ML) algorithms, including Bagging classifier, Logistic regression (LR), J48, Random Forest (RF), IBk, and AdaBoostM1, were employed to assess water potability. Evaluation metrics such as accuracy, recall, precision, F-measure, false positive (FP) rate, receiver operating characteristic (ROC) area, and precision-recall curve (PRC) area were used to compare the capability of the models. The outcomes of the comparative analysis revealed that RF and J48 achieved the highest accuracy values of 0.993, followed closely by the Bagging classifier with an accuracy of 0.992. The AdaBoostM1 algorithm achieved an accuracy of 0.971, while the LR algorithm achieved an accuracy of 0.958. The IBK algorithm showed a lower accuracy of 0.714. The comparative analysis of the FP rate metric demonstrated that RF achieved the lowest rate of 0.006, followed closely by the Bagging classifier and J48, both with a rate of 0.007. AdaBoostM1, LR, and IBK had higher rates of 0.026, 0.041, and 0.289, respectively. Regarding precision, RF and J48 achieved the highest precision rates of 0.993, followed by the Bagging classifier at 0.992. The AdaBoostM1 algorithm achieved a precision rate of 0.972, and LR achieved 0.958. IBK showed less precision rate of 0.714. For the recall metric, RF and J48 achieved the highest recall values of 0.993, followed closely by the Bagging classifier with a recall value of 0.992. The AdaBoostM1 algorithm obtained a recall value of 0.971, while LR and IBK achieved values of 0.958 and 0.714, respectively. The study highlights the effectiveness of RF, J48, and the Bagging classifier in predicting water potability. These findings contribute valuable insights for the implementation of accurate prediction models, supporting the sustainable management of water resources.

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“…Thus, this combination helps to achieve the less accuracy during forecasting. Prasad et al [14] reported that handling the complexity of existing schemes is cumbersome process. Thus, authors developed artificial intelligence based machine learning approach.…”

Section: Literature Surveymentioning

confidence: 99%

Ensemble Tree Classifier based Analysis of Water Quality for Layer Poultry Farm: A Study on Cauvery River

Deepika¹,

Nagarathna²,

Channegowda³

2022

IJACSA

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Indian poultry industry has evolved from a simple backyard occupation to a large commercial agri-based enterprise. Chicken dominates poultry production in India, accounting for almost 95% of total egg production. Several factors affect the egg production such as feeding material, drinking water, environmental factors etc. Analyzing the water quality is one of the important tasks. Cauvery River is considered as the study area because of its importance in several states of South India which have significant contribution in poultry farming. The aim of the proposed study is to develop an automated approach of water quality analysis and present a novel machine learning approach which considers an improved feature ranking method and ensemble tree classifier with majority voting. The experimental result shows that proposed approach performs better with an accuracy of 95.12%.

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Analysis and prediction of water quality using deep learning and auto deep learning techniques

Cited by 64 publications

References 22 publications

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms

A Comprehensive Comparative Study of Machine Learning Algorithms for Water Potability Classification

Ensemble Tree Classifier based Analysis of Water Quality for Layer Poultry Farm: A Study on Cauvery River

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