Extreme Weather Hazards and Insurance Revenues Based on Bi-directional LSTM.

As extreme weather becomes more prevalent, it has serious implications for the insurance industry, real estate owners, and historic buildings. First of all, for the extreme weather, we summarize extreme weather into three parts: extreme temperature, extreme precipitation, and extreme humidity, and we process the data by combining the actual situation and applying the improved Critic method, and we demonstrate the data processing by taking the three data of Lhasa and Luxor as examples. anomalies. Subsequently, a future extreme climate prediction model for a region based on the Bi-directional LSTM method is developed. A time-weather model was fitted based on the collected data, with the highest and lowest points as the anomalies. At the same time, we simplify the formula to derive the insurance company's revenue, thus modeling the relationship between the degree of extreme weather hazard and insurance revenue, and derive that the insurance company will not be able to underwrite when the combined hazard is greater than the ratio of the insurance claim cost to the insurance payment. We applied the resulting model to Lhasa and Luxor, and plotted the combined hazard level of the actual data and the predicted data to help the insurance company visualize the combined hazard level of the region each month, and then choose whether to underwrite the policy in that region or not. [ABSTRACT FROM AUTHOR]