A Transdisciplinary Framework for AI-driven Disaster Risk Reduction for Low-income Housing Communities in Kenya

In the past 50 years, natural disasters worldwide have accounted for 2.06 million deaths and US$3640 billion in economic losses. These natural disasters are heavily influenced by the composite earth system processes and human interactions. In this paper, we focus our investigation to assess the impact of flooding in rivers and coastal regions and its impact on low-income communities. For this purpose, a transdisciplinary perspective from Artificial Intelligence (AI), Climate science, Socio-economics discipline is leveraged to map and identify their inter-relationships and challenges using Soft System Methodology (SSM). A transdisciplinary framework, named ADRELO1 Disaster Support System (ADSS), is therefore proposed to (1) identify the key parameters that can influence climate change, (2) stitch together a reusable multilayered transdisciplinary knowledge model, and (3) apply the observed multivariant data to AI-based algorithm to forecast climate change, analyze the impact of climate change on socio-economic outcomes and suggest potential disaster risk reduction actions. Research-based outcomes, from the given framework, will be used for policy prescription towards making flood-affected local communities self-resilient. ADSS will be applied first in a flood-prone region, such as Nyando in Kenya and Mozambique. It will then be extrapolated in other coastal regions of Florida and North-eastern Brazil to examine the applicability of the framework.