Geoenvironmental Disasters

Landslides are one of the most important geohazards. In 2004–2016, more than 55,000 people lost their lives to landslides and this does not include deaths caused by seismically triggered landslides. Overall losses were estimated to be at USD 20 billion annually. The lives of many could be saved if more had been known regarding forecasting and mitigation. Studies have shown an increasing trend in landslides occurrence and fatalities. Over recent years, landslide risk assessment has been carried out extensively by geo-scientists worldwide. This review concentrates on the societal risks posed by landslides in various countries and the risk criteria used by various parts of the world in assessing landslide risks. The landslide risk tolerance criteria are strongly governed by utilitarian concerns i.e. financial power and the need for development. In developing countries, surprisingly high levels of tolerance are proposed for landslides. The risk criteria of Hong Kong and that of the Australian Geomechanics Society are widely employed in many countries. Although various risk tolerance levels have been proposed by various nations, many of them are still not being applied in their real-life scenarios. The procedures for setting risk criteria call for a wide agreement between geo-scientists, government decision makers, and the community. Risk criteria should be developed locally with historical landslide inventory, public perception, and engineering aspects being considered.

Recent years have shown a significant increase in the occurrence of floods globally, with an impact on habitation and different sectors of the economy. This, in turn, necessitates the use of different flood mitigation strategies, wherein flood vulnerability assessment plays a significant role. The proposed work presents a methodology that combines vulnerability under physical-environmental and socio-economic domains to assess the overall flood vulnerability at the local self-government level. The methodology was illustrated to the case of Aluva municipality, located on the banks of River Periyar, in Kerala state, India. The spatial variation of hazard inducing factors and population statistics were analysed using Geographic Information System (GIS) tools. The machine learning algorithm, Random Forest, which uses hazard inducing factors as input was implemented for the evaluation of physical-environmental vulnerability. The social vulnerability of the region was analysed using the GIS Multi-criteria decision analysis approach (MCDA), with criteria weights to incorporate the interests of different stakeholders. The critical combinations of the two domains of vulnerability in the assessment of the vulnerability to flood, to have efficient flood management in local self-government was demonstrated in this study and can be made use of for any flood event.