Department of Civil Engineering

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Author: search.filters.author.Singh, Ajit PratapSubject: search.filters.subject.River basin

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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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    Impact assessment of industrial wastewater discharge in a river basin using interval-valued fuzzy group decision-making and spatial approach
    (Springer, 2020) Srinivas, Rallapalli; Singh, Ajit Pratap
    Sustainable and integrated river basin planning and management is a complex process involving uncertain data at different stages of decision-making process. Moreover, there are multiple decision makers at different institutions with contrasting interests and objectives, and thus, a collaborative decision making is required to resolve the conflicts. Although the formulation or modeling of such problems under fuzzy framework provides a very strong ground to deal with the uncertain and complex judgments, there is scope to model the problem more accurately. The present study develops a novel approach of dealing with uncertainty associated with group decision making in a river basin, by extending fuzzy Delphi process using interval-valued fuzzy sets. A case study of assessing the impact of industrial wastewaters on the Ganges River basin, India, has also been presented to demonstrate the effectiveness of the proposed methodology. A total of 33 industrial units, mainly paper pulp, tanneries and textiles, discharging massive quantities of wastewater in the Ganges River basin have been chosen for the analysis. These industries are rated by the expert decision makers to represent their objective judgments (and/or subjective preferences) on the basis of ten essential sets of criteria such as impact on river, impact on groundwater, critical pollutants level, impact on public health. The ratings are analyzed and aggregated using modified fuzzy decision-making approach, and industries are ranked accordingly. To enhance the decision-making process, the results are also represented spatially under GIS environment. Analysis of results clearly demonstrates the contribution of crucial indicators/criteria in ensuring the sustainable use of water resources with respect to environmental, social and economic dimensions. The results obtained are compared and validated with the recent research works and reports of pollution control boards. The study recommends several policy implementations, primarily revisal in prescribed effluent discharge standards of the industries. The model developed herein can be an efficient and productive tool for complex group decisions in water resources planning by facilitating participation and knowledge sharing among the experts.
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    Holistic approach for quantification and identification of pollutant sources of a river basin by analyzing the open drains using an advanced multivariate clustering
    (Springer, 2018-11) Singh, Ajit Pratap; Srinivas, Rallapalli
    Global scarcity of freshwater has been gearing towards an unsustainable river basin management and corresponding services to the humans. It needs a holistic approach, which exclusively focuses on effective river water quality monitoring and quantification and identification of pollutant sources, in order to address the issue of sustainability. These days, rivers are heavily contaminated due to the presence of organic and metallic pollutants released from several anthropogenic sources, such as industrial effluents, domestic sewage, and agricultural runoff. It is astonishing to note that even in many developing countries, most of these contaminants are carried through open drains, which enter river premises without proper treatment. Such practice not only devastates riverine ecosystem but also gives rise to deadly diseases, such as minimata and cancer in humans. Considering these issues, the present study develops a novel approach towards simultaneous identification of major sources of pollution in the rivers,
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    Development of a HEC-HMS-based watershed modeling system for identification, allocation, and optimization of reservoirs in a river basin
    (Springer, 2017-12) Srinivas, Rallapalli; Singh, Ajit Pratap
    One of the primary objectives of river basin planning and management is to assess the behavior of the river towards man-made and natural changes. In recent times, the self-purifying capacity of the river is found to be substantially affected because of extensive use of water for agricultural and industrial purposes. Any variation in the flow regime of a river poses a severe impact on the aquatic ecosystem, which affects its self-purifying capacity. Diverting river water for industrial and agricultural uses through dams and barrages reduces the natural flow rate of the river. The present study develops a novel approach by couplingWatershed Modeling System (WMS ver. 10.1) with linear optimization to provide an alternate means of water supply for such users. To explain the effectiveness of the model, a case study on the Ganges river basin of India has been considered. The ecosystem of the Ganges provides such a magnificent biological fabric, that its self-purifying capacity exceeds that of any other river water across the globe. However, the industries found in the river’s most polluted stretch consume around 1200 million liters of water every day. In addition, 80% of the river water diverts at Narora barrage for agricultural purposes. As a result, the flow of the river in dry seasons is as less as 300 m3/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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    An evidence based integrated watershed modelling system to assess the impact of non-point source pollution in the riverine ecosystem
    (Elsevier, 2020-02) Srinivas, Rallapalli; Singh, Ajit Pratap
    Assessing the impact of land use cover on the river water quality is a pre-requisite to sustainable river basin planning and management. In recent times, non-point source pollution generated from agricultural watersheds has been significantly deteriorating water quality of major rivers such as river Ganges in India as described in this case study. The present work develops a Geographical Information System based mechanism to model non-point source pollution using multivariate regression analysis. The watershed model delineates runoff direction and identifies its lowest elevation points (outlets) near the river body where maximum pollution is caused by non-point source pollution, and thus provides a concrete evidence that agricultural runoff is the primary cause of increasing concentration of nitrogen and phosphorus compounds in the river. A case study of river Ganges basin, India is considered to demonstrate the applicability of the model. Relationships among six land cover and eleven critical water quality parameters are studied using multivariate regression near three selected sampling stations obtained using geographical information systems model. The results indicate that inorganic farming practices have a direct impact on the river water quality, leading to positive correlation (R2 ≥ 0.65) amongst ‘double-crop cover’ and ‘build-up area’ with Temperature, nitrogen as nitrite, nitrogen as nitrate, nitrite + nitrate, phosphorous as orthophosphate within the river body. Trend analysis study of temperature using Mann-Kendall test and Sen slope reveals an average of 0.23 °C/year positive trend in river temperature due to discharge of NPS pollution through agricultural watersheds. The study alarms the policymakers to educate the farmers to adopt best management practices such as increasing soil matter, usage of tile drainage, bioreactors, nutrient removal wetlands, using cover crops, etc. not only to increase crop productivity, but also to enhance the water quality in the riverine ecosystem.
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    Conceptual Modeling and Management of Water Quality in a River Basin
    (Capital Books, 2003-01) Singh, Ajit Pratap
    The paper presents a systematic methodology to predict the dissolved oxygen (DO) concentration in a river under fast purification by considering both the dispersion and sedimentation effects. The model evaluates coefficient of reaeration and deoxygenation through simulation analysis and then DO concentration at any point in a reach of the river is calculated. The model does not require a prior knowledge of the initial DO deficit at the top of every intermediate reach. Taking a case study of Delhi stretch of river Yamuna where BOD removal rates are seen to be higher than the normal rates, BOD and DO deficit occurring at a point in different reaches is predicted and compared with the observed values which is followed by important model-development tasks like calibration, verification and sensitivity analysis. The model will help to predict the future DO concentration and BOD to monitor the river water quality and to set up the efficient and optimum wastewater treatment facilities
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    ASSESSMENT OF AIR QUALITY IN HAORA RIVER BASIN
    (EM International, 2015) Singh, Ajit Pratap
    In this paper Air Quality Indices (AQIs) have been derived to assess the status of ambient air quality along Haora river basin using modified Environmental Protection Agency procedure. Air samples have been collected from 10 sampling stations located along the river. Six air pollutants namely, sulfur dioxide, nitrogen dioxide, carbon monoxide, suspended particulate matter, PM10 and lead were monitored during April to June 2012 continuously for 24 hours. It appears from the analysis that among all six air pollutants, PM10 was found to be the responsible pollutant to deteriorate ambient air quality which ranges from 130.6 μg/m3 to 572.14 μg/m3. Suspended particulate matter also exist at a critical level ranging from 142.31 μg/m3 to 684.95 μg/m3. The piecewise linear function with maximum operator as aggregation function was used to compute the AQI scores. The computed AQI value for the selected study region varied from moderately pollution level with the AQI score of 160.80 to the severe pollution level with the AQI score of 484.68 at sampling stations S1 and S8 respectively. The index developed was found suitable to demonstrate spatial variations of ambient air quality.
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    Impact assessment of industrial wastewater discharge in a river basin using interval-valued fuzzy group decision-making and spatial approach
    (Springer, 2017-06) Singh, Ajit Pratap; Srinivas, Rallapalli
    Sustainable and integrated river basin planning and management is a complex process involving uncertain data at different stages of decision-making process. Moreover, there are multiple decision makers at different institutions with contrasting interests and objectives, and thus, a collaborative decision making is required to resolve the conflicts. Although the formulation or modeling of such problems under fuzzy framework provides a very strong ground to deal with the uncertain and complex judgments, there is scope to model the problem more accurately. The present study develops a novel approach of dealing with uncertainty associated with group decision making in a river basin, by extending fuzzy Delphi process using interval-valued fuzzy sets. A case study of assessing the impact of industrial wastewaters on the Ganges River basin, India, has also been presented to demonstrate the effectiveness of the proposed methodology. A total of 33 industrial units, mainly paper pulp, tanneries and textiles, discharging massive quantities of wastewater in the Ganges River basin have been chosen for the analysis. These industries are rated by the expert decision makers to represent their objective judgments (and/or subjective preferences) on the basis of ten essential sets of criteria such as impact on river, impact on groundwater, critical pollutants level, impact on public health. The ratings are analyzed and aggregated using modified fuzzy decision-making approach, and industries are ranked accordingly. To enhance the decision-making process, the results are also represented spatially under GIS environment. Analysis of results clearly demonstrates the contribution of crucial indicators/criteria in ensuring the sustainable use of water resources with respect to environmental, social and economic dimensions.
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    Development of a HEC-HMS-based watershed modeling system for identification, allocation, and optimization of reservoirs in a river basin
    (Springer, 2017-12) Singh, Ajit Pratap; Srinivas, Rallapalli
    One of the primary objectives of river basin planning and management is to assess the behavior of the river towards man-made and natural changes. In recent times, the self-purifying capacity of the river is found to be substantially affected because of extensive use of water for agricultural and industrial purposes. Any variation in the flow regime of a river poses a severe impact on the aquatic ecosystem, which affects its self-purifying capacity. Diverting river water for industrial and agricultural uses through dams and barrages reduces the natural flow rate of the river. The present study develops a novel approach by coupling Watershed Modeling System (WMS ver. 10.1) with linear optimization to provide an alternate means of water supply for such users. To explain the effectiveness of the model, a case study on the Ganges river basin of India has been considered. The ecosystem of the Ganges provides such a magnificent biological fabric, that its self-purifying capacity exceeds that of any other river water across the globe. However, the industries found in the river’s most polluted stretch consume around 1200 million liters of water every day. In addition, 80% of the river water diverts at Narora barrage for agricultural purposes. As a result, the flow of the river in dry seasons is as less as 300 m3/s. The study suggests the need to develop economically feasible and efficient storage reservoirs to store the rainwater, which can be used to supply industrial and agricultural needs. The WMS software is used to acquire the watershed basin, outlet location, simulated runoff volume, proposed reservoir site, and the hydrograph using the monitored rainfall data of 5 years (2010–2014). The simulated runoff volume is then used to develop an optimization model to determine the required capacity of each reservoir using LINGO software (ver. 16.0). Four different storage reservoirs are proposed in the selected industrial sites of Unnao district, Uttar Pradesh, India. These reservoirs can supply the needs of industries, and thus reducing their dependency on the river Ganges.