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GIS-integrated multi-criteria suitability analysis for healthcare facilities site selection in Rajouri district, Jammu and Kashmir, India
12-29Views:425The study aims to develop a Decision Support model for the selection of a suitable site to establish a new healthcare center with adequate facilities based on the analytical hierarchy process (AHP) in the Rajouri district of Jammu and Kashmir. This study utilized AHP and GIS to identify an appropriate location for a new healthcare center. The study employed eight criteria to evaluate potential locations and utilized pairwise comparison to assign weights to each criterion. GIS-based spatial analysis was used to create factor and suitability maps for each criterion. Suitability was evaluated on a scale of 0 to 10 and each factor map was combined using the ArcGIS weighted overlay selection tool. The final map of the study represents the suitable site for a healthcare center in the Rajouri district and it shows the sites from the highly suitable to the least suitable area. In Rajouri district, mostly the central part can be considered very suitable as the population density of this area is higher compared to other areas of the district. The southwestern parts of the district are moderately suitable or least suitable sites for a new healthcare center. The study displays the pattern of the existing location of healthcare centers, mostly, the existing locations are not proper and suitable. Therefore, in the future, the allocation of healthcare centers must be in more adequate areas. Policymakers and healthcare professionals can be benefitted from this study in selecting suitable locations for future hospitals, which could ultimately improve access to healthcare services in the region. Additionally, the study can be contemplated in developing new policies for better transportation system in the study area.
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Coastal landuse land cover change and transformations in-between Cuddalore and Nagore, south east coast of India using remote sensing and GIS
11-24Views:232This study was conducted to assess the Land use and land cover (LULC) changes in a dynamic coastal zone; this is also an essential factor of studying the relationships between the human activity and coastal environment. The study region has been suffered from various natural hazards such as cyclone impacts, coastal erosion and rarely tsunamis. LULC changes was studied and reported for the period of 4 decades from 1980 to 2020. The overall accuracy assessment and Kappa coefficient values shows the substantial results of LULC maps. In the study area LULC changes has been classified in the six classes. The result shows reduction in plantations, coastal wetland and fallow land. Whereas improvement found in barren land, built-up land and water body of the study area from 1980 to 2020. Immediate attention is required to the increase the mangrove forest to be as a natural protection from the calamities in coastal wetlands. The information resulting from this study can be used in forthcoming management plans for urbanization and towards the sustainable development of the region. This study can be adapted to the world’s any coastal region to establish a strategic plan of action to protect the coastal communities and the environment.
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Remote Sensing and GIS based site suitability analysis for tourism development in Vaishali block, Bihar, India
12-22Views:888Geographic Information Systems (GIS) and Remote Sensing are presently recognized generally as an improve instrument for overseeing, breaking down, and showing gigantic volumes of fluctuated information suitable to numerous neighborhood and provincial arranging exercises. Because of the composite idea of the travel industry arranging issues, the planned of GIS in settling these issues is progressively perceived. This paper will think a portion of the conceivable outcomes of GIS applications in the travel industry arranging. For the most part, GIS applications in the travel industry have been tight to recreational office stock, the travel industry situated land the board, and diversion untamed life strife; and have been thin by absence of financing, and awkward techniques. Utilizing the case of site wellness investigation for the travel industry improvement and mapping, this paper features a few uses of GIS in the travel industry arranging in vaishali square, Bihar. According to our present investigation; the most reasonable the travel industry site recognized by the examination is inside significant towns. The urban focus with plausibility to develop into the travel industry focuses. The rest of the land shows a low appropriateness scale because of absence of significant appreciation for make a solid force factor. Availability is an essential for the travel industry advancement. Great street organize availability with closeness to railroads station or air terminal demonstrated solid vacationer potential site, this combined with proximity to grand magnificence delineates high appropriateness. Significant vacation destinations, for example, legacy locales, gardens and water bodies or lake demonstrated high appropriateness. This can be corresponded to the way that legacy destinations and other high appropriate highlights are converted into reasonable the travel industry site.
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A deep analytics for prediction and forecasting of air quality in Chennai
33-53Views:95Air pollution is a global crisis with profound implications for public health and environmental sustainability. In addressing this issue in Chennai, Tamil Nadu, a novel Hadoop-based real-time air pollution prediction system is proposed. This research offers accurate air quality information for specific Chennai regions, aiding decisions and mitigating pollution risks through big data analytics and deep learning for air quality prediction. To expedite air quality prediction, a vast air pollution dataset is rigorously analyzed using a Hadoop-based analytics model. Specific locations in Chennai, including Perungudi, Royapuram, Manali, Alandur, Arumbakkam, Kodungaiyur, and Velachery, are assessed for upto- date air quality evaluations. The core of the research revolves around implementing deep learning models—Long Short-Term Memory, Convolutional Neural Network, and a hybrid Long Short-Term Memory-Convolutional Neural Network model. These models are trained to forecast AQI for selected areas over the next five years, with the hybrid model emerging as the standout performer, achieving 99% of accuracy rate and mean absolute error, mean square error, root mean square error rates of 7.95, 101.71, 9.65. This high accuracy and low error rates empowers policymakers and environmental agencies to make informed decisions, fostering healthier living conditions in Chennai. The integration of big data analytics and deep learning, promises improved air quality management in urban areas globally, addressing similar environmental challenges and enhancing overall quality of life.