Department of Civil Engineering
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Item Development of a comprehensive fuzzy based approach for evaluating sustainability and self-purifying capacity of river Ganges(Taylor & Francis, 2017-11) Singh, Ajit Pratap; Srinivas, RallapalliWith accelerated and uncontrolled developments, large amount of untreated wastes is discharged into river water courses through various open drains. Though rivers possess self-purifying capacity, water withdrawals for different beneficial uses have impacted it significantly by reducing its flow. Presently, sustainability has also become an important affair of river basin planning and management. Therefore, assessment of behavior of river under sustainability criteria is necessary. However, the uncertainty and complexity associated with the sustainability criteria, randomness of hydrologic variables, decision-makers, and missing data have become a concern for water managers. Such problems can be modeled under fuzzy logic framework. The present work develops a comprehensive artificial intelligence approach, namely ‘MATLAB Fuzzy Inference system’ to determine the self-purifying capacity of the River Ganges. Thirty-three wastewater drains are identified, which discharge untreated wastes along Kanpur–Varanasi stretch of Ganges. Critical water quality parameters have been analyzed and impact of discharge of river at 12 sampling stations is studied. The model developed to measure the sustainability is flexible to incorporate spatial/temporal changes. Final results give emphatic information to water authorities to maintain adequate flow in the river needed to dilute the waste and also in determining the treatment technology and capacity for open drains.Item Detecting SARS-CoV-2 RNA prone clusters in a municipal wastewater network using fuzzy-Bayesian optimization model to facilitate wastewater-based epidemiology(Elsevier, 2021-07) Srinivas, Rallapalli; Singh, Ajit PratapThe current pandemic disease coronavirus (COVID-19) has not only become a worldwide health emergency, but also devoured the global economy. Despite appreciable research, identification of targeted populations for testing and tracking the spread of COVID-19 at a larger scale is an intimidating challenge. There is a need to quickly identify the infected individual or community to check the spread. The diagnostic testing done at large-scale for individuals has limitations as it cannot provide information at a swift pace in large populations, which is pivotal to contain the spread at the early stage of its breakouts. Recently, scientists are exploring the presence of SARS-CoV-2 RNA in the faeces discharged in municipal wastewater. Wastewater sampling could be a potential tool to expedite the early identification of infected communities by detecting the biomarkers from the virus. However, it needs a targeted approach to choose optimized locations for wastewater sampling. The present study proposes a novel fuzzy based Bayesian model to identify targeted populations and optimized locations with a maximum probability of detecting SARS-CoV-2 RNA in wastewater networks. Consequently, real time monitoring of SARS-CoV-2 RNA in wastewater using autosamplers or biosensors could be deployed efficiently. Fourteen criteria such as population density, patients with comorbidity, quarantine and hospital facilities, etc. are analysed using the data of 14 lac individuals infected by COVID-19 in the USA. The uniqueness of the proposed model is its ability to deal with the uncertainty associated with the data and decision maker's opinions using fuzzy logic, which is fused with Bayesian approach. The evidence-based virus detection in wastewater not only facilitates focused testing, but also provides potential communities for vaccine distribution. Consequently, governments can reduce lockdown periods, thereby relieving human stress and boosting economic growth.Item Regulation of water resources systems using fuzzy logic: a case study of Amaravathi dam(Springer, 2018-08-06) Singh, Ajit PratapRegulation of a water resource system is one of the challenging tasks due to uncertainty involved in demand and supply. It may be due to changes in the climatic conditions, living standards of people, land-use patterns and even because of changes in technology. The problem becomes even more complicated if the objectives pertaining to demand and supply are multiple and conflicting in nature. Therefore, this paper deals with regulation of water resources system based on “if–then” fuzzy logic-based rules which interlinks concepts of interpolative reasoning, logical implications and certain inference tools to infer knowledge about a water resource system using linguistic descriptions. Reasonable inferences have been drawn using concept of tautologies viz. modulus ponens and modulus tollens. Finally, the model is applied to a practical case study in order to demonstrate effectiveness of the proposed logic. The main motive of this study is to demonstrate applicability of fuzzy inference system for regulating operations of water resource systems.