Browsing by Author "Jayadeep Pati"

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Classification and Hazards of Arsenic in Varanasi Region Using Machine Learning
(Springer Science and Business Media Deutschland GmbH, 2022) Siddharth Kumar; Arghya Chattopadhyay; Jayadeep Pati
Groundwater plays a significant role in sustaining life in terrestrial and marine ecosystems. Arsenic contamination in aquifers poses a serious threat to the ecosystem due to its carcinogenic effect. Arsenic contamination in aquifers of the Varanasi region was noticed after water sampled from random sites of the Varanasi region of Uttar Pradesh, India. Under the Capacity Building of Urban Development (CBUD) scheme, Varanasi was chosen by the Ministry of Housing and Urban Poverty Alleviation (MoHUPA) and the Ministry of Urban Development (MoUD). In this study, various machine learning classifiers have been developed to classify water samples collected from the Varanasi region as safe or unsafe for consumption. The water with less than 10 µg/L As concentration is termed safe per World Health Organisation (WHO). Firstly the water samples parameters were ranked then the samples were trained and tested. Various parameters obtained from confusion matrices such as accuracy, precision, and recall are used to analyze the performance of different machine learning classifiers like Simple Logistic, MLP Classifier, and Random Forest. Among these models, Simple Logistic outperforms other classifier models. The Simple Logistic algorithm was considered the best model among the different classifiers. It has the highest accuracy of 79.03%, the highest precision of 77.00%, the highest recall of 79.00%, and a high ROC area of 69.40%. Thus, this model can be used for classification, and policymakers may devise plans to tackle the As poisoning in the Varanasi region. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Classification of arsenic in groundwater samples using extreme learning machine and crow search algorithm for smart cities
(IGI Global, 2024) Siddharth Kumar; Harshdeep Singh Dhillon; Arghya Chattopadhyay; Jayadeep Pati
This research aims to classify the arsenic contamination in the groundwater along the banks of river Ganga of Varanasi, India. The groundwater is vital for various purposes, including agriculture and drinking. Groundwater contamination with high levels of arsenic pose a significant health risk. To tackle this problem, the authors build a model for classifying arsenic levels in groundwater samples that incorporates the extreme learning machine (ELM) algorithm and crowd search optimisation (CSO) technique. In the hybrid approach, they initialize the ELM components and randomly assign weights while employing CSO to guide the search for optimal solutions. By classifying new groundwater samples as having high or low arsenic concentrations, the developed model can be used to evaluate the new groundwater samples. The proposed hybrid approach offers a promising solution for monitoring and managing groundwater quality, ensuring a healthier environment for the city's population. © 2024, IGI Global. All rights reserved.
The machine learning and geostatistical approach for assessment of arsenic contamination levels using physicochemical properties of water
(IWA Publishing, 2023) Arghya Chattopadhyay; Anand Prakash Singh; Siddharth Kumar; Jayadeep Pati; Amitava Rakshit
Arsenic contamination in groundwater due to natural or anthropogenic sources is responsible for carcinogenic and non-carcinogenic risks to humans and the ecosystem. The physicochemical properties of groundwater in the study area were determined in the laboratory using the samples collected across the Varanasi region of Uttar Pradesh, India. This paper analyses the physicochemical properties of water using machine learning, descriptive statistics, geostatistical and spatial analysis. Pearson correlation was used for feature selection and highly correlated features were selected for model creation. Hydrochemical facies of the study area were analyzed and the hyperparameters of machine learning models, i.e., multilayer perceptron, random forest (RF), naïve Bayes, and decision tree were optimized before training and testing the groundwater samples as high (1) or low (0) arsenic contamination levels based on the WHO 10 μg/L guideline value. The overall performance of the models was compared based on accuracy, sensitivity, and specificity value. Among all models, the RF algorithm outclasses other classifiers, as it has a high accuracy of 92.30%, a sensitivity of 100%, and a specificity of 75%. The accuracy result was compared to prior research, and the machine learning model may be used to continually monitor the amount of arsenic pollution in groundwater. © 2023 The Authors.