Analysing Process and Probability of Built-Up Expansion Using Machine Learning and Fuzzy Logic in English Bazar, West Bengal

Assessing tropical cyclone risk for improving mitigation strategies in Coastal Odisha, India

Tropical cyclone causes large-scale devastation and destruction in the coastal plains of India, particularly in Odisha, which is the most cyclone-affected state in the country. Tropical cyclones are projected to be more powerful and widespread due to changing climate. Hence, the risk assessment of tropical cyclone is necessary to identify cyclone-risk areas in coastal Odisha which may help in the mitigation of the damages caused by cyclones. Therefore, this study utilizes geospatial techniques to produce a comprehensive risk map posed by tropical cyclones and to estimate the degree of risk for coastal districts of Odisha. For this, we evaluated the district-level cyclone risk for coastal Odisha using multi-criteria decision-making (MCDM) technique by considering 21 parameters for each of the four components of risk, i.e., exposure, hazard, vulnerability, as well as mitigation capacity. For each criterion, thematic raster map layers were created and weighted using a fuzzy analytical hierarchy process (FAHP). We prepared individual risk component maps using weighted overlay techniques and finally integrated all indices to create the risk map. The study shows that 13% area of the study area comes under a very high-risk zone whereas, 25% area comes under a high-risk zone. The central (Cuttack, northern parts of Khordha, and south-western parts of Jajpur district) and the eastern part (most of the parts of Jagatsinghpur, Kendrapara, and northern parts of Puri district) of the study area come under high to very high tropical cyclone impact zone. Almost 67% of the total area is highly vulnerable to tropical cyclones and mainly concentrated near the shoreline. The applied approach and results can assist the local authorities in identifying vulnerable and hazardous locations and developing workable solutions for the mitigation of revised cyclone threats in the coastal districts of Odisha.

Interactions of urbanisation, climate variability, and infectious disease dynamics: insights from the Coimbatore district of Tamil Nadu

Climate change and shifts in land use/land cover (LULC) are critical factors affecting the environmental, societal, and health landscapes, notably influencing the spread of infectious diseases. This study delves into the intricate relationships between climate change, LULC alterations, and the prevalence of vector-borne and waterborne diseases in Coimbatore district, Tamil Nadu, India, between 1985 and 2015. The research utilised Landsat-4, Landsat-5, and Landsat-8 data to generate LULC maps, applying the maximum likelihood algorithm to highlight significant transitions over the years. This study revealed that built-up areas have increased by 67%, primarily at the expense of agricultural land, which was reduced by 51%. Temperature and rainfall data were obtained from APHRODITE Water Resources, and with a statistical analysis of the time series data revealed an annual average temperature increase of 1.8 °C and a minor but statistically significant rainfall increase during the study period. Disease data was obtained from multiple national health programmes, revealing an increasing trend in dengue and diarrhoeal diseases over the study period. In particular, dengue cases surged, correlating strongly with the increase in built-up areas and temperature. This research is instrumental for policy decisions in public health, urban planning, and climate change mitigation. Amidst limited research on the interconnections among infectious diseases, climate change, and LULC changes in India, our study serves as a significant precursor for future management strategies in Coimbatore and analogous regions.

Modelling built-up land expansion probability using the integrated fuzzy logic and coupling coordination degree model

The expansion of built-up area is the most noticeable form of urbanization-induced land use/land cover (LULC) change. In the global cities of south, the urban sprawl is increasing rapidly with even higher probabilities of future built-up expansion. These cities are witnessing unsustainable urban growth with no consideration of eco-friendly environmental condition and quality of life due to rapid expansion in built-up area. Indian cities too have been witnessing rapid urban growth and built-up expansion especially in the large metropolitan cities like Delhi. Therefore, the main objective of this study is to model the built-up expansion probabilities in Delhi National Capital Region (Delhi NCR) using remote sensing datasets and an integrated fuzzy logic and coupling coordination degree model (CCDM). For this, initially, the LULC classification was done using random forest (RF) classifier to extract the built-up area. Further, analytical hierarchy process (AHP) integrated fuzzy sets were applied using the extracted built-up area along with a set of economic, demographic, proximity parameters, topographic, and utility services. Five zones of built-up expansion probabilities were made namely very high, high, medium, low and very low. The result shows that the probability of built-up expansion in Delhi NCR is maximum under very high and high probability zones, whereas minimum expansion probabilities come in the very low probability zone for both base year i.e., 2018 and future years. Moreover, between base year and future years, the probability of built-up expansion has increased maximum (5.72%) under the very high zone while it declined by 14.06% in low probability zone. The validation of built-up probability using CCDM shows that the AHP integrated fuzzy logic-based probability model is robust while predicting built-up probability. The results of this study may provide useful insights for the urban planning department and policy makers to mitigate the adverse impacts of built-up expansion. Similar approach may be utilized in the analyzing the built-up urban expansion of other major cities of the world similar geographical conditions.

Modeling Spatiotemporal Patterns of Land Use/Land Cover Change in Central Malawi Using a Neural Network Model

We examine Land Use Land Cover Change (LULCC) in the Dedza and Ntcheu districts of Central Malawi and model anthropogenic and environmental drivers. We present an integrative approach to understanding heterogenous landscape interactions and short- to long-term shocks and how they inform future land management and policy in Malawi. Landsat 30-m satellite imagery for 2001, 2009, and 2019 was used to identify and quantify LULCC outcomes based on eight input classes: agriculture, built-up areas, barren, water, wetlands, forest-mixed vegetation, shrub-woodland, and other. A Multilayer Perceptron (MLP) neural network was developed to examine land-cover transitions based on the drivers; elevation, slope, soil texture, population density and distance from roads and rivers. Agriculture is projected to dominate the landscape by 2050. Dedza has a higher probability of future land conversion to agriculture (0.45 to 0.70) than Ntcheu (0.30 to 0.45). These findings suggest that future land management initiatives should focus on spatiotemporal patterns in land cover and develop multidimensional policies that promote land conservation in the local context.