Browsing by Author "Srinivas, Rallapalli"

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Advances in Computational Modeling and Simulation
(Springer, 2022) Srinivas, Rallapalli; Kumar, Rajesh
Agricultural watershed conservation and optimization using a participatory hydrological approach
(Springer, 2024-07) Srinivas, Rallapalli
Maximizing the impact of agricultural wastewater conservation practices (CP) to achieve total maximum daily load (TMDL) scenarios in agricultural watersheds is a challenge for the practitioners. The complex modeling requirements of sophisticated hydrologic models make their use and interpretation difficult, preventing the inclusion of local watershed stakeholders’ knowledge in the development of optimal TMDL scenarios. The present study develops a seamless modeling approach to transform the complex modeling outcomes of Hydrologic Simulation Program Fortran (HSPF) into a simplified participatory framework for developing optimized management scenarios. The study evaluates seven conservation practices in the Pomme de Terre watershed in Minnesota, USA, focusing on sediment and phosphorus pollutant load reductions incorporating farmers’ opinions to guide practitioners toward implementing cost-effective CPs. Results show reduced tillage and filter strips are the most cost-effective practices for non-point source pollution reduction, followed by conservation cover perennials. The integration of SAM with HSPF is crucial for sustainable field-scale implementation of conservation practices through enhanced involvement of amateur-modeling stakeholders and farmers directly connected to fields.
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.
Attention-enabled Deep Neural Network for Enhancing UAV-Captured Pavement Imagery in Poor Visibility
(IEEE, 2023) Singh, Ajit Pratap; Srinivas, Rallapalli; Narang, Pratik
Integrating Unmanned Aerial Vehicle (UAV) technology with Artificial Intelligence AI and Computer Vision has revolutionized asset management, particularly pavement health monitoring. However, current AI-based methods often struggle in low-visibility scenarios, limiting their effectiveness. To address this, we present a novel end-to-end deep learning pipeline that detects image degradation using an efficient Attention mechanism and performs subsequent enhancement. This algorithm can be seamlessly integrated into drones or used for post-processing of pavement imagery. Its efficiency allows for scalability, making it a valuable tool for downstream road health monitoring tasks, such as cost estimation for road repairs. Our approach achieves mean accuracies of 93.34% with a mean inference time of 0.154 sec., demonstrating its efficacy.
Bio-chelate assisted leaching for enhanced heavy metal remediation in municipal solid waste compost
(2024-06) Singhal, Anupam; Srinivas, Rallapalli
Municipal solid waste compost, the circular economy's closed-loop product often contains excessive amounts of toxic heavy metals, leading to market rejection and disposal as waste material. To address this issue, the study develops a novel approach based on: (i) utilizing plant-based biodegradable chelating agent, l-glutamic acid, N,N-diacetic acid (GLDA) to remediate heavy metals from contaminated MSW compost, (ii) comparative assessment of GLDA removal efficiency at optimal conditions with conventional nonbiodegradable chelator EDTA, and (iii) enhanced pre- and post-leaching to evaluate the mobility, toxicity, and bioavailability of heavy metals. The impact of treatment variables, such as GLDA concentration, pH, and retention time, on the removal of heavy metals was investigated. The process was optimized using response surface methodology to achieve the highest removal effectiveness. The findings indicated that under optimal conditions (GLDA concentration of 150 mM, pH of 2.9, retention time for 120 min), the maximum removal efficiencies were as follows: Cd-90.32%, Cu-81.96%, Pb-91.62%, and Zn-80.34%. This process followed a pseudo-second-order kinetic equation. Following GLDA-assisted leaching, the geochemical fractions were studied and the distribution highlighted Cd, Cu, and Pb's potential remobilization in exchangeable fractions, while Zn displayed integration with the compost matrix. GLDA-assisted leaching and subsequent fractions illustrated transformation and stability. Therefore, this process could be a sustainable alternative for industrial applications (agricultural fertilizers and bioenergy) and social benefits (waste reduction, urban landscaping, and carbon sequestration) as it has controlled environmental footprints. Hence, the proposed remediation strategy, chemically assisted leaching, could be a practical option for extracting heavy metals from MSW compost, thereby boosting circular economy.
Bio-chelation for sustainable heavy metal remediation in municipal solid waste compost: a critical review of chelation technologies
(Springer, 2025-04) Singhal, Anupam; Srinivas, Rallapalli
Municipal solid waste (MSW) compost is a promising solution for sustainable urban waste management, widely used as a soil amendment and for carbon sequestration. However, heavy metals in MSW compost pose risks to ecosystems, food safety and human health. This review critically examines three decades of research (1994–2024) on heavy metal contamination in MSW compost and household hazardous waste (HHW), identifying gaps in managing these pollutants, particularly regarding hazardous waste co-disposal. It evaluates existing remediation strategies for heavy metal removal, with a focus on chemical-assisted leaching using chelating agents. Key treatment parameters—such as chelating agent concentration, pH, contact time, liquid/solid ratio, temperature and flow rate—are analysed in both batch and continuous modes. The study advocates for biodegradable chelating agents as an effective approach to enhancing MSW compost quality, with applications in landfill reclamation and agriculture. Emphasizing the need for eco-friendly heavy metal mitigation, the review underscores the importance of safe urban composting practices. The findings contribute to the circular economy and Sustainable Development Goals by promoting sustainable and safe MSW compost applications, fostering environmental protection and public health and guiding research and industry toward scalable, marketable remediation solutions.
Bio-inspired and artificial intelligence enabled hydro-economic model for diversified agricultural management
(Elsevier, 2022-07) Srinivas, Rallapalli
Neoteric phenomena such as climate change, scarce water availability and excessive fertilizer usage necessitate an augmentation of resource utilisation efficiencies in the agricultural sector. There is a need to reorient the agroecosystems to curb stress on environmental resources while meeting rising socio-economic objectives under changing hydro-climatic conditions. Considering this, optimal land allocation for diversified agriculture is essential. We propose a combinatorial optimisation approach for land allocation considering agronomic, socio-economic, environmental and hydro-climatic objectives using bio-inspired optimization algorithms. The stochastic approach tackles the problem of optimal agricultural land allocation for crops in a multidimensional context by simultaneously addressing the conflicting goals of farm-level risk management as well as district-level contingency planning. The efficiencies and sensitivity of the proposed framework are assessed through a case study of the Dharwad district in Karnataka, India using the data (water and fertilizer consumption and cost, crop type, cultivable land, man and machine hours, etc.) from the year 2019–2020. Results indicate that Multi-objective Genetic Algorithm (MOGA) is more capable of optimising agricultural resources management by suggesting optimal land allocation for diversified crop planning. Although Cuckoo Search (CS) and Particle Search Optimisation (PSO) also produced productive Pareto fronts, they were observed to be less effective than MOGA. The annual increase in profits and crop yield obtained using MOGA are 103% and 97% respectively, while water usage is reduced by 5% compared to the conventional routines in Dharwad. The proposed hydro-agronomic decision support framework (DSF) can be utilised to assist the AI-enabled crop planning process for the sustainable management of agroecosystems.
Bioinspired modeling and biogeography-based optimization of electrocoagulation parameters for enhanced heavy metal removal
(Elsevier, 2022-03) Srinivas, Rallapalli
Electrocoagulation is an effective wastewater treatment process for the removal of heavy metals. This study focuses on deriving optimal conditions for removing heavy metals, viz. Lead (Pb), Cobalt (Co), and Manganese (Mn) from simulated wastewater by investigating removal efficiency and energy consumption of electrocoagulation process. Five operational parameters namely pH (2–10), current density (0.076–0.189 A/cm2), inter-electrode distance (3–7 cm), solution temperature (30–70 °C) and charging time (5–25 cm) have been analyzed. To improve the treatment of heavy metals, a novel coupled approach, namely Artificial neural network - non-dominated sorting Biogeography based optimization (ANN-NSBBO), has been proposed. Using the experimental data, a feed-forward backpropagation ANN model is used with removal efficiency and energy consumption as the outputs. Optimal values of operational parameters for maximum removal efficiency and minimum energy consumption were obtained using multi-objective NSBBO over the trained ANN model. True pareto fronts for Cobalt, Lead and Manganese were obtained after 100 iterations of the optimization algorithm. The maximum removal efficiency of 98.66% was obtained for Cobalt at the electrical energy consumption of 0.204 kWh. Minimum energy consumption for electrocoagulation of Lead (5.34 x 10−6 kWh) gave 82.48% removal efficiency. The maximum removal efficiency of Manganese (101.238%) was achieved at 7.64 pH, 0.084 A/cm2 current density, 3.188 cm inter-electrode distance, 47.49 °C solution temperature, 19.758 min charging time, and 0.145 kWh energy consumption. The non-dominated optimum tradeoff between removal efficiency and energy consumption provides clarity on operating conditions for the electrocoagulation process. The proposed approach of enhancing heavy metal treatment could assist municipalities, industries, and the scientific communities in achieving the United Nation's sustainable development goal of heavy metal remediation.
Cloud-based neuro-fuzzy hydro-climatic model for water quality assessment under uncertainty and sensitivity
(Springer, 2022-04) Srinivas, Rallapalli
River water quality is a function of various bio-physicochemical parameters which can be aggregated for calculating the Water Quality Index (WQI). However, it is challenging to model the nonlinearity and uncertain behavior of these parameters. When data is deficient and noisy, it creates missing and conflicting parameters within their complex inter-relationships. It is also essential to model how climatic variations and river discharge affect water quality. The present study proposes a cloud-based efficient and resourceful machine learning (ML) modeling framework using an artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and advanced particle swarm optimization (PSO). The framework assesses the sensitivity of five critical water quality parameters namely biochemical oxygen demand (BOD), dissolved oxygen (DO), pH, temperature, and total coliform toward WQI of the River Ganges in India. Monthly datasets of these parameters, river flow, and climate components (rainfall and temperature) for a nine-year (2011–2019) period have been used to build the models. We also propose collecting the data by placing various monitoring sensors in the river and sending the data to the cloud for analysis. This helps in continuous monitoring and analysis. Results indicate that ANN and ANFIS capture the nonlinearity in the relationship among water quality parameters with a root mean square error (RMSE) of 7.5 × 10−7 (0.002%) and 1.02 × 10−5 (0.029%), respectively, while the combined ANN-PSO model gives normalized mean square error (NMSE) of 0.0024. The study demonstrates the role of cloud-based machine learning in developing watershed protection and restoration strategies by analyzing the sensitivity of individual water quality parameters while predicting water quality under changing climate and river discharge.
Compounding effects of urbanization, climate change and sea-level rise on monetary projections of flood damage
(Elsevier, 2023-05) Srinivas, Rallapalli; Goonetilleke, Ashantha
Climate change and urbanization play critical roles in compounding future flood risk due to their adverse impacts on the rainfall regime and sea level rise. Although past studies have predicted the spatiotemporal variations in flood risk, these have appreciable limitations, viz. (i) flood risk is predicted mainly by accounting for one driver at a time (either ocean flooding or fluvial flooding); and (ii) monetization of flood damage due to future flooding had not been investigated. However, multiple drivers could lead to flooding in coastal areas. This study presents an innovative approach for investigating the cumulative effects of urbanization, changes to the rainfall regime, and sea level rise on consequential flood damage in a coastal urban area. A comprehensive flood damage and hazard prediction model was developed by integrating 1D-2D aspects of MIKE FLOOD and GIS technology to assess the flood scenarios for 2040, 2070, and 2100 by investigating three predictor variables: urbanization, rainfall regime, and sea level rise. The factorial design approach was used to construct a total of 27 future flood scenarios. Time horizons of 30 years provided for effectively capturing climate change and its influence on the hydrologic regime. The Generalized Linear Model (GLM) was applied to create a statistical model based on future scenarios for each time horizon. Results confirmed that changes to the rainfall regime significantly influence the average annual damage (AAD) caused by flooding for all time horizons. At the same time, the significance of the effects of urbanization and sea level rise was found to vary. The model predicts that by 2040, urbanization would exacerbate AAD, with a significant contribution from sea level rise. In contrast, sea level rise would provide a marginally greater and more significant contribution to AAD compared to urbanization in 2040 and 2070. Compared to the base year 2017, AAD was 78%, 197%, and 351% higher in 2040, 2070, and 2100, respectively. The proposed flood damage prediction model developed can guide modelers and decision-makers in assessing the compounding flood damage for future flood management in any geographic location.
Detecting SARS-CoV-2 RNA prone clusters in a municipal wastewater network using fuzzy-Bayesian optimization model to facilitate wastewater-based epidemiology
(Elsiever, 2021-07-15) Singh, Ajit Pratap; Srinivas, Rallapalli
The 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.
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 Pratap
The 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.
Developing strategic and staging optimization pathways for urban flood damage mitigation
(Elsevier, 2025-10) Srinivas, Rallapalli; Singh, Ajit Pratap; Goonetilleke, Ashantha
Despite significant advancements in flood risk assessment and damage monetization, research is lacking for simultaneously examining the impacts of the complexity of factors such as rising sea levels, changing rainfall patterns, and urbanization, on flood damage assessment. This study adopts an innovative staging procedure that progressively and strategically optimizes flood damage mitigation measures while addressing the uncertainties associated with the implementation of flood mitigation measures over three different time horizons (2040, 2070, and 2100), with each subsequent stage refined based on the constraints and optimal results of the previous stage. Using the Non-dominated Sorting Genetic Algorithm (NSGA II), the study compares 27 optimized pathways for mitigating flood damages, balancing investment costs and Average Annual Damage (AAD) reduction. The results demonstrate that the proposed approach achieves an AAD reduction of up to 2.89% in 2040, 4.03% in 2070, and 2.12% in 2100 under the most comprehensive mitigation pathways while balancing the costs. The study highlights cost-effective alternatives, such as combining dredging and permeable asphalt, achieving a 1.31% AAD reduction in 2040 with no additional costs. Compared to static single-stage mitigation policies, the proposed staging approach offers greater flexibility and efficiency in addressing dynamic urbanization and climate change scenarios. These results underline the trade-offs between cost and effectiveness, equipping policymakers with a robust decision-making framework to tailor flood mitigation strategies for diverse global contexts. Overall, this study significantly advances the strategic planning of urban flood damage mitigation, enabling adaptation to evolving environmental and socio-economic challenges.
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.
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
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.
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.
Development of an advanced entropy-based decision support system to assess the feasibility of linking of rivers in a sustainable manner
(Taylor & Francis, 2020-01) Singh, Ajit Pratap; Srinivas, Rallapalli
Interlinking rivers (ILRs) is a recent global initiative towards sustainable and equitable distribution and utilization of water resources. However, there are diverse, uncertain, and conflicting viewpoints of various water resource planning and management stakeholders towards ILR approach and its outcomes. On one hand, policy makers are seeing ILR as advantageous to the economic development of the nation; on the other hand, there is a growing concern about the negative environmental impacts of ILR. To ensure sustainable development, watershed planners across the globe need a decision support system (DSS) to effectively plan the implementation of ILR projects while considering the diverse stakeholder’s perspectives and conflicting socio-economic, environmental, and several technical criteria. Therefore, it is essential to identify and mathematically evaluate the strengths, weaknesses, opportunities, and threats (SWOT) of ILR projects to achieve economic and environmental objectives. It is indeed a challenge for watershed managers to simultaneously address the uncertainty associated with multiple criteria and group of decision-makers (stakeholders) involved in developing the SWOT model. To address these concerns, the present study proposes an advanced entropy-based SWOT fuzzy decision support system to evaluate the feasibility of ILR on a case study of ‘Ken-Betwa project’, India. The novel aspect of the advanced approach is its ability to devise seven hybrid mechanisms, which give the flexibility to reach the best solutions along with nominal, optimistic and pessimistic perspectives of the stakeholders while addressing uncertainty issues. Results demonstrate that adopting measures to minimize impact on biodiversity and climate change and implementation of sustainable construction techniques are pivotal for successful implementation of ILR project with a score of 0.12 and 0.11, respectively. The DSS provides a platform to the state and federal agencies to systematically achieve socio-economic and environmental objectives while incorporating suggestions of all decision-makers towards ILR projects.
Development of an advanced entropy-based decision support system to assess the feasibility of linking of rivers in a sustainable manner
(Taylor & Francis, 2020-06) Srinivas, Rallapalli; Singh, Ajit Pratap
Interlinking rivers (ILRs) is a recent global initiative towards sustainable and equitable distribution and utilization of water resources. However, there are diverse, uncertain, and conflicting viewpoints of various water resource planning and management stakeholders towards ILR approach and its outcomes. On one hand, policy makers are seeing ILR as advantageous to the economic development of the nation; on the other hand, there is a growing concern about the negative environmental impacts of ILR. To ensure sustainable development, watershed planners across the globe need a decision support system (DSS) to effectively plan the implementation of ILR projects while considering the diverse stakeholder’s perspectives and conflicting socio-economic, environmental, and several technical criteria. Therefore, it is essential to identify and mathematically evaluate the strengths, weaknesses, opportunities, and threats (SWOT) of ILR projects to achieve economic and environmental objectives. It is indeed a challenge for watershed managers to simultaneously address the uncertainty associated with multiple criteria and group of decision-makers (stakeholders) involved in developing the SWOT model. To address these concerns, the present study proposes an advanced entropy-based SWOT fuzzy decision support system to evaluate the feasibility of ILR on a case study of ‘Ken-Betwa project’, India. The novel aspect of the advanced approach is its ability to devise seven hybrid mechanisms, which give the flexibility to reach the best solutions along with nominal, optimistic and pessimistic perspectives of the stakeholders while addressing uncertainty issues. Results demonstrate that adopting measures to minimize impact on biodiversity and climate change and implementation of sustainable construction techniques are pivotal for successful implementation of ILR project with a score of 0.12 and 0.11, respectively. The DSS provides a platform to the state and federal agencies to systematically achieve socio-economic and environmental objectives while incorporating suggestions of all decision-makers towards ILR projects.
Effect of urbanization on the urban lake water quality by using water quality index (WQI)
(Elsevier, 2023-07) Srinivas, Rallapalli; Singhal, Anupam
Lake water serves an efficient source of drinking, irrigation, agriculture, industry, construction, domestic and recreation use for the urban and rural population of developing countries. The paper focuses on the assessment of water quality on the selected lakes which is affected by the speedy development of the city under the sprawl of urbanization and concretization by applying Water Quality Index (WQI) tool. Four lakes, namely Hebbal, Ulsoor, Allasandra and Mahadevapura are selected in the silicon city, Bengaluru for water quality assessment. A total of 10 parameters were taken into consideration, such as pH, turbidity, total alkalinity, total acidity, total phosphorus, COD, BOD, DO, nitrates and total nitrogen from 2 sampling sites depending upon the source of wastewater or sewage discharges. Water samples were collected and prepared for composite samples. These composite samples were examined for their different chemical and physical properties and the results were compared with standard permissible values. The results of WQI of Hebbal Lake (70.89–72.74), Ulsoor Lake (83.44–83.3), Allasandra Lake (54.47–51.84) and Mahadevapura Lake (159.41–155.81) showed that the lakes fall under poor, very poor and unsuitable categories. The results pointed out the anthropogenic activities and entry of untreated sewage into the lake. This confirms the urgent need for regular monitoring of lakes and setting up of certain policies for lake water management.