http://localhost:8081/jspui/handle/123456789/125 2026-10-21T22:23:19Z 2026-10-21T22:23:19Z Yadav, Abhimanyu http://localhost:8081/jspui/handle/123456789/21533 2026-09-17T11:50:30Z 2023-05-01T00:00:00Z

Title: PREDICTIVE MODELING FOR WATER QUALITY ASSESSMENT: INTEGRATING MACHINE LEARNING WITH LULC, GROUNDWATER LEVELS, RAINFALL DISTRIBUTION, AND HEALTH IMPACTS Authors: Yadav, Abhimanyu Abstract: To guarantee the safety of drinking water sources in the future, it is crucial to develop a comprehensive understanding of the current state of groundwater quality and pollution levels. Accurate water quality prediction is pivotal in effectively controlling water pollution and enhancing water management practices. This study focused on predicting groundwater quality for drinking and irrigation purposes in Madhya Pradesh, a central state in India. The dataset used in the study comprised groundwater samples of monitoring stations across 52 districts of Madhya Pradesh from May 2000 to May 2018. Various machine learning models, including Deep Neural Network (DNN), Artificial Neural Network (ANN), Random Forest (RF), and XGBoost, were used to predict different water quality indices such as Sodium Absorption Ratio (SAR), Soluble Sodium Percentage (SSP), Kelly's Ratio (KR), Permeability Index (PI), Magnesium Hazard (MH), and Water Quality Index (WQI). The performance of these models was compared using minimal input variables. The results showed that DNN and SAR best predicted WQI, while ANN achieved the highest accuracy for SSP, KR and PI predictions. RF and XGBoost also yielded satisfactory results for all the indices. Furthermore, the study assessed the impact of nitrate and fluoride contamination on human health through a risk assessment. The findings revealed that infants were at the highest risk, followed by children, adult females, and adult males, emphasizing the need for addressing nitrate and fluoride contamination in the study area. Additionally, the study examined the influence of land use/land cover, rainfall, and groundwater level on groundwater quality. The land use/land cover classification revealed various classes, including forest, cropland, built-up land, range land, flooded vegetation, and water bodies. The results showed that cropland had the most deteriorated groundwater quality due to excessive usage of fertilizers and pesticides. Built-up areas and water bodies also exhibited poor groundwater quality due to improper wastewater treatment and disposal. The study also analyzed the relationship between groundwater quality and normal rainfall distribution, indicating that lower rainfall was associated with poorer groundwater quality. The analysis revealed that as the groundwater depth increases, the proportion of poor to unsuitable water quality also increases. In conclusion, this study provides insights into the prediction of groundwater quality using machine learning models and highlights the risks posed by nitrate and fluoride contamination on human health. The findings underscore the importance of addressing land use practices, rainfall patterns, and groundwater management strategies in Madhya Pradesh to ensure sustainable groundwater resources.

2023-05-01T00:00:00Z A, Kalel Ahamed http://localhost:8081/jspui/handle/123456789/21532 2026-09-17T11:50:03Z 2023-05-01T00:00:00Z

Title: MODELING THE IMPACTS OF CLIMATE CHANGE FOR IRRIGATION WATER MANAGEMENT : A CASE STUDY ON LOWER BHAVANI SAGAR PROJECT COMMAND AREA, TAMIL NADU Authors: A, Kalel Ahamed Abstract: This thesis evaluates the Reliability (time & volume), vulnerability, and resilience of the Lower Bhavanisagar Reservoir, Erode, Tamil Nadu, a multipurpose reservoir that served as a critical source of water for industries, irrigation, hydroelectric generation and environmental preservation. Future impacts of the climate change on the water availability are recognized and for which the effective management strategies to improve the efficiency and capacity of the reservoir for various purposes are derived. Rule curves are being generally utilized to manage efficiently the reservoirs, ensuring the demand is met throughout the drawdown-refill cycle. The study proposes a framework for analysing the extent of climate change including the present and future periods and improving the Adaptive Management of water resource systems. Special attention to institutional setups is recommended to speed up the process of implementing Adaptive Management. Hydrologic models, such as SWAT, are used to model the catchment runoff yield, and also providing a better understanding of the historical, current, and future scenarios related to the change in reservoir inflow behaviour. The efficiency of SUFI-2 and other techniques for hydrologic model uncertainty analysis, calibration, and validation are explored, with SUFI-2 often proving the most effective method. The study aims to design an optimal hedging strategy based on the simulated historical and climate change-affected hydrology to ensure the reservoir's optimal performance. The study concludes that effective control operation of reservoirs is essential to ensure that the allocation of available water results in optimal system performance. Despite facing challenges, the water resource officials and farmers in the Lower Bhavanisagar Reservoir system have shown a positive attitude towards minimizing the climate change impacts on the irrigation and other uses of water. Therefore, the recommendations of the study presented here will assist in more scientifically informed way to cope up with future climate impacts improving the resilience and adaptability.

2023-05-01T00:00:00Z Kumar, Piyush http://localhost:8081/jspui/handle/123456789/21531 2026-09-17T11:49:49Z 2023-05-01T00:00:00Z

Title: ANALYSIS AND SCOPE OF CARBON CREDIT TRADING IN WATER RESOURCES PROJECTS Authors: Kumar, Piyush Abstract: Environmental degradation and global warming are imminent catastrophe with irreversible consequences. The increase in emissions of the GHGs (Green House Gases) is the major factor leading to the climate change and global warming. It is the main agenda of discussion at meetings held at global level including the Conference of Parties. To alleviate the dangerous atmospheric deviation, carbon credit is a significant part at global and public levels. Carbon credits exchanging gives a method for lessening fossil fuel dependence and lowers the emission of by-products generated from it. These carbon credits can be exchanged between two business entities or bought from worldwide market at an extremely ostensible cost. This sum can likewise be utilized to back different carbon reducing plans among the exchanging associations all over the planet. Carbon trading as a concept is not fully developed in India and huge awareness needs to be created to make it a great business opportunity as foreign companies in developed nations are bind to GHG quota allotted under Kyoto Protocol and therefore can buy surplus credits on exceeding it. Efforts at individual level and at governmental level need to be taken in addition to policy push towards expanding carbon trading. Carbon credit trading find its scope in various sectors such as agriculture, building sectors and industries, energy, marine, technology, forest, agroforestry etc. Developing countries like India must focus on this concept and explore the scope of carbon credit trading in various sectors by adopting proper methods and technologies. This will not only lead to economic development but also will lead to development of nation as a whole by providing opportunity for people to contribute to betterment of nation with their skills. In this review paper, carbon trading has a concept has been studied and its scope in various sectors such as processing industries, energy sector, marine, technology, power sector, agroforestry, carbon sink in urban spaces etc. is explored, analysed and presented and an attempt has been made to provide comprehensive preview.

2023-05-01T00:00:00Z Robert, Rama http://localhost:8081/jspui/handle/123456789/21530 2026-09-17T11:49:36Z 2023-05-01T00:00:00Z

Title: Soil Loss Assessment using Revised Universal Soil Loss Equation (RUSLE), Remote Sensing and GIS - Case Study of Rwanda Authors: Robert, Rama Abstract: Erosion, a global concern, and a threat to developing countries was revealed as the major problem enhancing food scarcity and hindering economic growth. Consequently, the degraded ecosystem has affected land productivity due to a decline in soil health and depleted soil fertility which is triggered by land use/land cover changes. Rwanda is a small and land-locked, globally densely populated country. A land blessed with hilly and steep topography and tropical climate. Through the rivers draining the country, precious resources, viz. water, soils, and organic matter are seasonally lost exhibited by the muddy water and debris in the runoff and streamflow. In this study, the investigation of rain induced topsoil loss, Sediment Yield Potential, and erosional water hazard assessment over 8 sub-watersheds of Rwanda i.e., AKA, AKL, AKU, KIV, MUK, MUV, NYL, and NYU in the period between 2021 and 1981 has been carried out. The Revised Universal Soil Loss Equation (RUSLE) Model integrated with GIS and Remote Sensing was adopted to estimate long-term average soil loss, whereas the CORINE model was utilized to examine the risk caused by the erosional water and finally, the SYI model was employed to calculate Sediment Yield Index and to prioritize sub-watersheds for catchment area treatment plan that responds to the ecosystem conservation. The results of the study revealed that Rwanda is greatly affected by water erosion that is ultimately increasing, with the estimated soil loss rate of approximately 45.9, 44.95, and 117.58 t ha-1yr-1 in 1990, 2008, and 2021, respectively. The estimated average annual soil loss at the sub-watershed scale i.e., KIV, NYU, and AKA sub-watersheds fall under the very severe erosion category with a mean soil loss rate nearly to 212.40 t ha-1yr-1, 180.76 t ha-1yr-1, and 115.86 t ha-1yr-1, respectively. Additionally, agricultural fields experienced a loss of 50.1 tons per hectare per year between 2008 and 2021. The erosion hotspot assessments revealed that nationwide 24.6 % of the land is severely vulnerable and 48.7% is moderately prone to erosion risk. The SYI model revealed that NYU, AKA, and MUV sub-watersheds are more prone to erosion as compared to AKU, NYL, AKL, KIV, and MUK sub-watersheds. The SYI values range between 1297.2 to 1517.4 for the sub-watersheds. Finally, biological i.e., Afforestation, strip cropping, contour farming, etc, and structural interventions i.e., construction of check dams, deep trenches, and terraces were suggested as soil and water conservation measures. Land degradation, Land use/ land cover, Remote Sensing and GIS, RUSLE Model, soil and water conservation, Soil erosion hazards, Soil erosion rate, Sediment Yield Index

2023-05-01T00:00:00Z