# Typhoon disaster emergency forecasting method based on big data

## TL;DR

This paper introduces a big data-based model using neural networks to predict typhoon trends, showing good performance for strong storms but needing improvement for weaker ones.

## Contribution

A novel typhoon disaster forecasting method using neural networks and big data for improved prediction accuracy.

## Key findings

- The model showed good fit for predicting strong tropical storms.
- Predicted typhoon center positions had small average errors and aligned closely with actual paths.
- Forecasting accuracy for tropical depressions and typhoons requires improvement.

## Abstract

Typhoons are natural disasters characterized by their high frequency of occurrence and significant impact, often leading to secondary disasters. In this study, we propose a prediction model for the trend of typhoon disasters. Utilizing neural networks, we calculate the forgetting gate, update gate, and output gate to forecast typhoon intensity, position, and disaster trends. By employing the concept of big data, we collected typhoon data using Python technology and verified the model’s performance. Overall, the model exhibited a good fit, particularly for strong tropical storms. However, improvements are needed to enhance the forecasting accuracy for tropical depressions, typhoons, and strong typhoons. The model demonstrated a small average error in predicting the latitude and longitude of the typhoon’s center position, and the predicted path closely aligned with the actual trajectory.