BITS Faculty Publications

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    Water Quality Index Calculation: Switching from MATLAB Fuzzy Toolbox to Python for Real-Time Implementation
    (IEEE, 2020) Gupta, Raj Kumar; Gupta, Karunesh Kumar
    Water quality assessment has always been of primary importance before consumption as most of the available water is polluted, which could transmit several waterborne diseases. Water Quality Index (WQI) is a unique single value to determine overall water quality. WQI summarizes the water quality parameters in a single value. MATLAB fuzzy is the standard toolbox to implement the water quality index. The user has to determine only the inputs as different water quality parameters and the membership functions based on the complexity of the application. This approach is offline, as it cannot be implemented in real-time. An alternate method may be the WQI measurement in the Python framework for real-time implementation. In this paper, we are trying to find out that it is possible to switch from MATLAB fuzzy toolbox to the Python framework for real-time implementation. The WQI measurement is performed in both the fuzzy toolbox from MATLAB® and Python 3.4. Based on the results, a comparative study has been done, and the switching possibility is found out.
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    Real-time water quality monitoring for distribution networks in IoT environment
    (Inder Science, 2022-04) Gupta, Karunesh Kumar; Gupta, Raj Kumar
    Water quality has always been a significant concern worldwide as a large portion of accessible water is either contaminated or polluted, which can spread serious diseases like dysentery, diarrhoea and cholera. Before consumption, the water quality should be tested to reduce the risk of infection. In real-time applications, the traditional approach for water quality monitoring is not appropriate, as on-site water sample collection is often a cost-intensive and time-consuming process. This paper introduces a real-time assessment of water quality parameters in distribution systems employing Raspberry Pi and Arduino development boards. The parameters were chosen based on the different categories identified by the Central Pollution and Control Board, Government of India. An Arduino development board was used at the sensing node for water quality sensor interfacing, data acquisition, and transmission to the wireless sensor network via Zigbee. Raspberry Pi was used at the server to collect data and upload data on the cloud platform. The 'Thingspeak' cloud platform was used for IoT implementation. The results were validated with the reference instrument.
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    Power and Area Efficient Intelligent Hardware Design for Water Quality Applications
    (International Frequency Sensor Association, 2018-11) Gupta, Anu; Gupta, Rajiv
    The paper presents a power efficient and computationally less intensive intelligent hardware using artificial neural network for water quality applications. A compact Hardware Neural Network algorithm has been developed that takes four water quality parameters as the input vector and perform classification of the parameters using a Multilayer Perceptron Network. The computational complexity in the implementation of logistic function has been reduced at a mathematical level by use of approximation methods such as Pad===?=== approximation for exponential function and non- linear approximation for sigmoid function. The network improves accuracy of the output by learning by back-propagation of the error. Results show that non-linear approximation method is 34.13 % power efficient and utilizes 15.53 % less number of hardware resources in comparison to Pad===?===. ASIC implementation is compact and has 99 % less power consumption as compared to FPGA implementation of the same algorithm.
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    Application of Fuzzy Multi-criteria Approach to Assess the Water Quality of River Ganges
    (Springer, 2017-11) Singh, Ajit Pratap; Srinivas, Rallapalli
    The purpose of this study is to develop a fuzzy multi-criteria decision-making framework to evaluate the water quality status of a river basin. The rampant and indiscriminate growth in the urban, agricultural, and industrial sector has directly or indirectly disrupted the water quality of the major rivers by discharging mammoth quantities of wastewaters. Regular and accurate evaluation of water quality of a river has become an important task of water authorities. However, the conventional way of evaluating water quality index has been unsuccessful in incorporating uncertainties and subjectivities associated with water quality analysis. Such limitations can be dealt effectively by using fuzzy logic concepts. The present study proposes an Interactive Fuzzy Water Quality Index (IFWQI) to evaluate the water quality status of river Ganges at Kanpur city, India. Multi-Criteria Decision-Making (MCDM) tool namely Fuzzy Inference System (FIS) of MATLAB has been used to obtain a qualitative and quantitative measure of water quality index at six different sites of Kanpur throughout the year by taking into consideration the six important water quality parameters. The results indicate a significant improvement in the accuracy of the index values and thus providing emphatic information to the planners to decide the remedial measures for sustainable management of river Ganges.
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    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, Rallapalli
    With 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.
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    A Scenario Based Impact Assessment of Trace Metals on Ecosystem of River Ganges Using Multivariate Analysis Coupled with Fuzzy Decision-Making Approach
    (Springer, 2017) Singh, Ajit Pratap; Srinivas, Rallapalli
    Abstract The growing consciousness about the health risks associated with environmental pollutants has brought a major shift in global concern towards prevention of hazardous/trace metals discharge in water bodies. Majority of these trace metals gets accumulated in the body of aquatic lives, which are considered as potential indicators of hazardous content. This results in an ecological imbalance in the form of poisoning, diseases and even death of fish and other aquatic lives, and ultimately affect humans through food chain. Trace metals such as Cd, Cr, Cu, Mn, Ni, Pb and Zn originated from various industrial operations containing metallic solutions and agricultural practices, have been contributing significantly to cause aquatic pollution. The present study develops a novel approach of expressing sustainability of river’s ecosystem based on health of the fish by coupling fuzzy sensitivity analysis into multivariate analysis. A systematic methodology has been developed by generating monoplot, two dimensional biplot and rotated component matrix (using ‘Analyze it’ and ‘SPSS’ software), which can simultaneously identify critical trace metals and their industrial sources, critical sampling stations, and adversely affected fish species along with their interrelationships. A case study of assessing the impact of trace metals on the aquatic life of river Ganges, India has also been presented to demonstrate effectiveness of the model. The clusters pertaining to various water quality parameters have been identified using Principal Component Analysis (PCA) to determine actual sources of pollutants and their impact on aquatic life. The fuzzy sensitivity analysis reveals the cause-effect relationship of these critical parameters. The study suggests pollution control agencies to enforce appropriate regulations on the wastewater dischargers responsible for polluting river streams with a particular kind of trace metal(s).
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    Evaluation and Quantification of Pollution Caused by Open Drains in Ganges River Basin Using Multivariate Cluster Analysis
    (IOS, 2019-09) Srinivas, Rallapalli; Singh, Ajit Pratap
    The colossal expansion and pace of global development have completely deteriorated the water quality of major river basins of the world such as Amazon, Ganges, Nile etc. Rivers have become receivers of wastewater discharged from industrial, agricultural and domestic sectors. Ganges river basin of India is considered as one of the heavily polluted river basin with 144 open drains entering into the river body without proper treatment. Therefore, monitoring and analysing the water quality of these drains and their sources is essential not only to suggest proper treatment procedures but also to ensure sustainability of the river ecosystem. The present study conducts an exhaustive quality analysis of 85 drains carrying both industrial and domestic sewage, either directly into river Ganges or indirectly through tributaries (Kali-East, Ram Ganga and Pandu) in ‘Haridwar to Kanpur’ stretch. Multivariate technique namely Principal Component Analysis (PCA) and Cluster Analysis (CA) have been employed using ‘Analyse it’ software to evaluate the intensity and sources of pollution. The methodology generates monoplots and two dimensional biplots to identify the relationships among pollutants and their sources. Finally, quality assessment of drains has been performed by calculating the water quality index of each drain, and sensitivity analysis is carried out to evaluate the effect of critical water quality parameters. Results direct the policy makers to identify the industries responsible for polluting the drains above critical levels and further measures are suggested to improve the deteriorating quality of drains.
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    Integrated decision support for promoting crop rotation based sustainable agricultural management using geoinformatics and stochastic optimization
    (Elsevier, 2022-09) Srinivas, Rallapalli
    DSTDecision Support Tool SPCStochastic Pairwise Comparison BMPBest Management Pratices EUEuropian Union FLMFunctional Land Management AHPAnalytical Hierarchy Process GISGeographic Information System DMDecision Maker GDPGross Domestic Product NDVINormalized Difference Vegetation Index ICARIndian Council of Agricultural Research CPCBCentral Pollution Control Board S1Highly Suitable S2Moderately Suitable S3Marginally Suitable NNot Suitable ECElectrical Conductivity OCOrganic Carbon COConstraint Optimization MIRMaximization of Income Returns MIWRMinimization of Irrigation Water Requirements
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    Managing nitrate-nitrogen in the intensively drained upper Mississippi River Basin, USA under uncertainty: a perennial path forward
    (Springer, 2022-08) Srinivas, Rallapalli
    The upper Mississippi River basin has been identified as the most significant contributor of excessive nutrients to the hypoxic zone in the Gulf of Mexico. The land-use changes from an internally drained prairie-wetland complex to an intensively managed corn-soybean production system drained by subsurface tile drainage system in the north-central Iowa and south-central Minnesota are the primary cause of nutrient loads into the Mississippi River and many other environmental stresses. The present study summarizes the water-quality degradation from landuse change and offers a fuzzy logic-based decision support for assessing degree of suitability of the four recommended perennial plant options for managing water and nitrate-nitrogen export.
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