Classification and prediction of rural socio-economic vulnerability (IRSV) integrated with social-ecological system (SES)

Abstract

Vulnerability is one of the prominent features of rural areas due to their distinctive characteristics, such as remoteness, geographical conditions, and socio-economic dependence on primary sectors. Addressing the vulnerability of rural areas in terms of the rural development paradigm is both urgent and relevant. This study aims to address this issue using the current state-of-the-art machine learning method, using the socio-ecological framework and integrated vulnerability index of villages in Lampung Province in Indonesia. The study attempts to predict and classify villages' vulnerability to be applied for better planning and rural development. Based on random forest classification and decision tree algorithm, the results show that the village governance system represented by rural water management and the level of education of village leaders are suitable prediction variables related to the low vulnerability index. This study can draw lessons learned to improve rural development in developing countries.

Machine Learning Applied to Gender Violence: A Systematic Mapping Study

“…Among the many fields of AI, ML is of great help in the social sciences because it is applied to create models that provide predictions that help make political decisions, develop theories [13] or even predict socioeconomic vulnerabilities in rural areas considering factors such as remoteness, geographical conditions or socioeconomic dependence on primary sectors, which helps to have better planning for rural development [14].…”

“…ML has been useful in several contexts. In the society in general, it can be applied to politics, socioeconomics, environment, wealth, or the development of theories, due to its capability to predict multiple scenarios [11], [12], [13], [14]. On a more specific level, it has been used in health, where it is very innovative since it can deliver traditional surveys and predict the risk levels of different diseases such as cancer, angina, or mental illnesses [26], [27], [29], [30], [32].…”

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