Data driven crop insurance model using ensemble learning and extreme risk analysis

Abstract:

Climate has a significant effect on agriculture since farmers rely on rainfall, temperature, and water level to determine the yield of crops. The traditional crop insurance models tend to utilize few inputs and only single predictions thus dealing with the uncertainty and extreme conditions may be hard to manage, such as floods and droughts. This influences the calculation of premium, payouts and benefits to farmers. Moreover, the alteration of climatic conditions and unforeseeable weather patterns also expose farmers to more risk. Thus, the even more developed and validated prediction techniques are required to enhance the accuracy of insurance and promote the better decision-making in the field of agriculture. In order to enhance precision, the models used in this project include Extra Trees, Gradient Boosting and MLP with some other weather and seasonal attributes. CQR is applied to give prediction ranges, whereas Extreme event analysis is provided by Extreme Value Theory (EVT) and Generalized Pareto Distribution (GPD). R-Vine Copula (RVC) models interaction of weather variables, Hidden Markov Model (HMM) conditions of dry, normal, and wet, and Ridge Regression (RR) is the integration of various risks into one measure. In the workflow, data collection, feature engineering, model training, ensemble prediction, uncertainty analysis, stress testing, and Monte Carlo simulation are included. Findings indicate an increase in reliability with a lower RMSE of 3.2995e-04 and MAE of 2.4003e-04, and high adoption rates (up to 88.4) and premium forecasting to make good insurance decisions.