Assessing climate-induced agricultural vulnerable coastal communities of Bangladesh using machine learning techniques.

The agricultural arena in the coastal regions of South-East Asian countries is experiencing the mounting pressures of the adverse effects of climate change. Controlling and predicting climatic factors are difficult and require expensive solutions. The study focuses on identifying issues other than climatic factors using the Livelihood Vulnerability Index (LVI) to measure agricultural vulnerability. Factors such as monthly savings of the farmers, income opportunities, damage to cultivable lands, and water availability had significant impacts on increasing community vulnerability with regards to agricultural practice. The study also identified the need for assessing vulnerability after certain intervals, specifically owing to the dynamic nature of the coastal region where the factors were found to vary among the different study areas. The development of a climate-resilient livelihood vulnerability assessment tool to detect the most significant factors to assess agricultural vulnerability was done using machine learning (ML) techniques. The ML techniques identified nine significant factors out of 21 based on the minimum level of standard deviation (0.03). A practical application of the outcome of the study was the development of a mobile application. Custom REST APIs (application programming interface) were developed on the backend to seamlessly sync the app to a server, thus ensuring the acquisition of future data without much effort and resources. The paper provides a methodology for a unique vulnerability assessment technique using a mobile application, which can be used for the planning and management of resources by different stakeholders in a sustainable way.