Dengue risk zone mapping of Thiruvananthapuram district, India: a comparison of the AHP and F-AHP methods.

Dengue fever, which is spread by Aedes mosquitoes, has claimed many lives in Kerala, with the Thiruvananthapuram district bearing the brunt of the toll. This study aims to demarcate the dengue risk zones in Thiruvananthapuram district using the analytical hierarchy process (AHP) and the fuzzy-AHP (F-AHP) methods. For the risk modelling, geo-environmental factors (normalized difference vegetation index, land surface temperature, topographic wetness index, land use/land cover types, elevation, normalized difference built-up index) and demographic factors (household density, population density) have been utilized. The ArcGIS 10.8 and ERDAS Imagine 8.4 software tools have been used to derive the risk zone maps. The area of the risk maps is classified into five zones. The dengue risk zone maps were validated using dengue case data collected from the Integrated Disease Surveillance Programme portal. From the receiver operating characteristic (ROC) curve analysis and the area under the ROC curve (AUC) values, it is proved that the F-AHP method (AUC value of 0.971) has comparatively more prediction capability than the AHP method (AUC value of 0.954) in demarcating the dengue risk zones. Also, based on the comparison of the risk zone map with actual case data, it was confirmed that around 82.87% of the dengue cases occurred in the very high and high-risk zones, thus proving the efficacy of the model. According to the dengue risk map prepared using the F-AHP model, 9.09% of the area of Thiruvananthapuram district is categorized as very high risk. The prepared dengue risk maps will be helpful for decision-makers, staff with the health, and disaster management departments in adopting effective measures to prevent the risks of dengue spread and thereby minimize loss of life.

Flood vulnerability of a few areas in the foothills of the Western Ghats: a comparison of AHP and F-AHP models.

Flooding is one of the most destructive natural catastrophes that can strike anywhere in the world. With the recent, but frequent catastrophic flood events that occurred in the narrow stretch of land in southern India, sandwiched between the Western Ghats and the Arabian Sea, this study was initiated. The goal of this research is to identify flood-vulnerable zones in this area by making the local self governing bodies as the mapping unit. This study also assessed the predictive accuracy of analytical hierarchy process (AHP) and fuzzy-analytical hierarchy process (F-AHP) models. A total of 20 indicators (nine physical-environmental variables and 11 socio-economic variables) have been considered for the vulnerability modelling. Flood-vulnerability maps, created using remotely sensed satellite data and geographic information systems, was divided into five zones. AHP and F-AHP flood vulnerability models identified 12.29% and 11.81% of the area as very high-vulnerable zones, respectively. The receiver operating characteristic (ROC) curve is used to validate these flood vulnerability maps. The flood vulnerable maps, created using the AHP and F-AHP methods, were found to be outstanding based on the area under the ROC curve (AUC) values. This demonstrates the effectiveness of these two models. The results of AUC for the AHP and F-AHP models were 0.946 and 0.943, respectively, articulating that the AHP model is more efficient than its chosen counterpart in demarcating the flood vulnerable zones. Decision-makers and land-use planners will find the generated vulnerable zone maps useful, particularly in implementing flood mitigation plans.

Migrating rivers, consequent paleochannels: The unlikely partners and hotspots of flooding.

Furious floods have become an omnipresent reality with the dawn of climate change and its transition to adulthood. Since climate change has now become an accepted reality, analysing the factors that favour or disfavour floods are an urgent requirement. Here we showcase the role of paleochannels, a product of migrating rivers, in a catastrophic flood in the south-western part of the Indian Peninsula. This study exposes whether these geomorphic features facilitate or impede floods. For the purpose of extracting paleochannels and floodwater mapping, we utilized multiple satellite datasets and took advantage of diversified feature selection algorithms. Paleochannels were demarcated viz., initial identification of a few paleochannels from literature and confirmation through high-resolution Google Earth (GE) images, followed by Principal Component Analysis (PCA) of Sentinel-2 images using Google Earth Engine (GEE), and a supervised classification of the principal bands 1, 2, and 3. False-positives were eliminated using Object-Oriented Analysis (OOA), which reduced the 964,254 polygons to 23,254. These polygons were visually affirmed using GE images that resulted in 115 paleochannels as the final collection. A few locations were verified through Vertical Electrical Sounding (VES) using the Schlumberger method. The features were analysed with the floodwaters of the 2018 catastrophic flood, extracted from Synthetic Aperture Radar (SAR) data, which was delineated for different temporal limits including the day of peak flood of August 17, 2018. During the peak flood, the inundation of the study area extended to 534.86 km2 with all the paleochannels getting immersed in floodwater. After 44 days of peak flood, the post-flood analysis revealed that when the floodwater receded 50%, the paleochannels emptied 87.39%, with the midland paleochannels discharging more than those of lowlands. Thus, such geomorphic features can be flood hotspots, but can be considered for discharging floodwater to mitigate flood risk in case of unprecedented rain.