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The vulnerability and flood risk assessment of Bhitarkanika National Park in Odisha, India, was conducted using a data-driven approach and a machine-based embedded decision support system. The park, located in the estuaries of the Brahmani, Baitarani, Dharma, and Mahanadi river systems, is home to India’s second-largest mangrove environment and the world’s most active and diverse saline wetland. To evaluate its vulnerability and risk, various threats were considered, with a focus on floods. Satellite imageries, such as Landsat 8 OLI, SRTM digital elevation model, open street map, Google pro image, reference map, field survey, and other ancillary data, were utilized to develop vulnerability and risk indicators. These indicators were then reclassified into ‘Cost’ and ‘Benefit’ categories for better understanding. The factors were standardized using the max-min standardization method before being fed into the vulnerability and risk model. Initially, an analytical hierarchy approach was used to develop the model, which was later compared with machine learning algorithms (e.g., SVM) and uncertainty analysis indices (e.g., overall accuracy, kappa, map quality, etc.). The results showed that the SVM-RBF machine learning algorithm outperformed the traditional geostatistical model (AHP), with an overall accuracy of 99.54% for flood risk mapping compared to AHP’s 91.12%. The final output reveals that a large area of Bhitarkanika National park falls under high flood risk zone. The Eastern coastal regions of Govindapur, Kanhupur, Chinchri, Gobardhanpur and Barunei fall under high risk zone of tidal floods, The Northern and western regions of Ramachandrapur, Jaganathpur, Kamalpur, Subarnapur, Paramanandapur, etc., Fall under high risk region of riverine floods. The study also revealed that the areas covered with mangroves have a higher elevation and hence are repellent to any kind of flood. In the event of a flood high priority conservation measures should be taken along all high flood risk areas. This study is helpful for decision-making and carrying out programs for the conservation of natural resources and flood management in the national park and reserve forest for ecological sustainability to support sustainable development goals (e.g., SDGs-14, 15).

Intense urbanization in the Indian cities has brought about phenomenal changes in the existing landuse patterns, whereby increasing trends of surface temperature and the presence and absence of green cover and water bodies, their shape, pattern, configuration significantly modifies the thermal properties of the city. The present study comprehendeds how different biophysical, anthropogenic, and landscape composition determine and interact with the surface temperature in different climatic zones in India, by taking Jaipur and Guwahati cities as examples. This study has also incorporated several landscape metrics of composition and configuration and spatial cluster analyses to identify the most fragmented landscape in the two cities. Such complex relationship among the landuse and landcover, bio-physical parameters and the surface temperature can be assessed by using the Generalised Additive Model (GAM). Result shows that Jaipur has experienced high temperature changes in city centre in comparison to Guwahati, which can be attributed to the differential presence of environmental sensitive land class in the city cores and peripheries. Results also indicate that medium patches of environmental sensitive land class, as well as close proximity and configuration with environmental non-sensitive class reduces the surface temperature in two heterogenous landscape. Thus, Policymakers and urban planners could be assisted by our finding to mitigate surface temperature in the city.