Department Of Geography

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Browsing Department Of Geography by Author "?urin, Bojan"

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    Assessment of Ground Water Quality of Lucknow City under GIS Framework Using Water Quality Index (WQI)
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023-08-28T00:00:00) Saqib, Nazmu; Rai, Praveen Kumar; Kanga, Shruti; Kumar, Deepak; ?urin, Bojan; Singh, Suraj Kumar
    Continuous groundwater quality monitoring is crucial for ensuring safe drinking and irrigation by mitigating risks from geochemical contaminants through appropriate treatment methods. Therefore, the primary objective of this study was to assess the suitability of groundwater collected from Lucknow, India, for both drinking and irrigation. Forty samples were collected from different sites within the study area to evaluate groundwater quality. Various parameters such as pH, turbidity, total dissolved solids (TDS), chlorides ((Formula presented.)), total alkalinity, total hardness, sulphate ((Formula presented.)), nitrate ((Formula presented.)), fluorides ((Formula presented.)), iron (Fe), arsenic (As), magnesium ((Formula presented.)), and calcium ((Formula presented.)) were analyzed. The weighted arithmetic water quality index (WAWQI), a vital rating system representing overall water quality, was employed to classify the water into different categories, such as very good, good, moderate, poor, and unfit for drinking. This classification is invaluable for public awareness and decision-making to make informed decisions regarding effective management, treatment, and sustainable societal development on a broader scale. A correlation matrix was generated and analyzed to observe correlations between the various parameters. Additionally, spatial distribution maps for the analyzed parameters and WQI were prepared using the inverse distance weighted (IDW) method. The study found that WQI values in the area ranged from 2.64 to 168.68, indicating good water quality in most places except for the Kukrail region, where the water quality is unfit for drinking purposes. The water quality map shows that 86% of the area falls under the very good category, 14.63% under good to moderate quality, and 0.37% is categorized as unfit for drinking. Consequently, the findings suggest that the groundwater in the studied area is safe and suitable for drinking and irrigation purposes. � 2023 by the authors.
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    Groundwater Potential Zone Mapping in the Ghaggar River Basin, North-West India, Using Integrated Remote Sensing and GIS Techniques
    (MDPI, 2023-03-02T00:00:00) Upadhyay, Ritambhara K.; Tripathi, Gaurav; ?urin, Bojan; �amanovi?, Sanja; Cetl, Vlado; Kishore, Naval; Sharma, Mukta; Singh, Suraj Kumar; Kanga, Shruti; Wasim, Md; Rai, Praveen Kumar; Bhardwaj, Vinay
    The immense dependence of the growing population on groundwater has resulted in depletion at a fast pace can be seen nowadays. Identifying a groundwater potential zone can be proved as an aid to provide insight to the decision-makers and local authorities for planning purposes. This study evaluated the delineation of groundwater potential zones using integrated remote sensing and GIS approach. Various thematic layers such as geology, geomorphology, lineament, slope, drainage, soil, land use/land cover, and rainfall were considered in this study as these have influence on the occurrence of groundwater and its cycle, and maps have been prepared in GIS domain. Afterward, appropriate weights were assigned to these layers based on multi-criteria decision analysis, i.e., Analytical Hierarchy Process (AHP). Groundwater potentiality has been delineated in different zones (low, moderate, high, and very high) in the study region based on weighted overlay analysis. The study reveals zones with different groundwater prospects viz. low (1.27%), moderate (15.65%), high (75.54%), and very high (7.29%). The ground survey data provided by CGWB (Central Ground Water Board) of nearly 100 wells/dug wells/borewells/piezometers have been used for validation purposes, showing comparable results with the groundwater prospects zones. It also confirms that the majority of these wells fall under very high or high groundwater potential zones. They were also found to be thereby indicating that there is the existence of a permeable reservoir with considerable water storage in the subsurface. One of the most important issues for users and governments is groundwater depletion. Planning for the available groundwater resource is made easier by identifying the potential for groundwater (low to high). � 2023 by the authors.
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    Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023-09-18T00:00:00) Rani, Ankush; Gupta, Saurabh Kumar; Singh, Suraj Kumar; Meraj, Gowhar; Kumar, Pankaj; Kanga, Shruti; ?urin, Bojan; Dogan?i?, Dragana
    The main aim of this study is to comprehensively analyze the dynamics of land use and land cover (LULC) changes in the Bathinda region of Punjab, India, encompassing historical, current, and future trends. To forecast future LULC, the Cellular Automaton�Markov Chain (CA) based on artificial neural network (ANN) concepts was used using cartographic variables such as environmental, economic, and cultural. For segmenting LULC, the study used a combination of ML models, such as support vector machine (SVM) and Maximum Likelihood Classifier (MLC). The study is empirical in nature, and it employs quantitative analyses to shed light on LULC variations through time. The result indicates that the barren land is expected to shrink from 55.2 km2 in 1990 to 5.6 km2 in 2050, signifying better land management or increasing human activity. Vegetative expanses, on the other hand, are expected to rise from 81.3 km2 in 1990 to 205.6 km2 in 2050, reflecting a balance between urbanization and ecological conservation. Agricultural fields are expected to increase from 2597.4 km2 in 1990 to 2859.6 km2 in 2020 before stabilizing at 2898.4 km2 in 2050. Water landscapes are expected to shrink from 13.4 km2 in 1990 to 5.6 km2 in 2050, providing possible issues for water resources. Wetland regions are expected to decrease, thus complicating irrigation and groundwater reservoir sustainability. These findings are confirmed by strong statistical indices, with this study�s high kappa coefficients of Kno (0.97), Kstandard (0.95), and Klocation (0.97) indicating a reasonable level of accuracy in CA prediction. From the result of the F1 score, a significant issue was found in MLC for segmenting vegetation, and the issue was resolved in SVM classification. The findings of this study can be used to inform land use policy and plans for sustainable development in the region and beyond. � 2023 by the authors.
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    Spatial and Temporal Analysis of Hydrological Modelling in the Beas Basin Using SWAT+ Model
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023-09-24T00:00:00) Singh, Suraj Kumar; Kanga, Shruti; Gulati, Bhavneet; Rai?, Mirna; Sajan, Bhartendu; ?urin, Bojan; Singh, Saurabh
    In this research, the SWAT+ model was employed to elucidate hydrological dynamics within the Beas Basin. The primary objectives encompassed the calibration of the SWAT model for accurate water balance quantification, annual simulation of salient hydrological components, and a decadal analysis of trends in fluvial discharge and sediment transport. The methodology encompasses simulating hydrological data with the SWAT+ model, followed by calibration and validation using flow data from Larji and Mahadev hydroelectric plants. The model�s efficacy in depicting streamflow and other hydrological components is corroborated by statistical measures such as the Nash�Sutcliffe efficiency and PBIAS. The water balance analysis delivers insights into the basin�s hydrological characteristics, including surface flow, water yield, and evapotranspiration. The temporal analysis exposes intricate seasonal and interannual variability in flow and sediment discharge, while spatial distribution highlights heterogeneity across the basin. These findings have practical implications for water resource management, including optimizing water allocation, hydroelectric power generation, irrigation, and environmental concerns. Limitations, such as data quality and model simplifications, are acknowledged, and future data collection and observations are recommended for improved model performance. In essence, these researches enhance understanding of the Beas Basin�s hydrology, setting a course for future investigations to integrate more data sources, refine model parameters, and consider climate and land-use changes for a richer comprehension of the basin�s hydrological dynamics. � 2023 by the authors.