An Improved Cluster‐Wise Typhoon Rainfall Forecasting Model Based on Machine Learning and Deep Learning Models Over the Northwestern Pacific Ocean
Md. Jalal Uddin, Yubin Li, Abdus Sattar, Mingyang Liu, Nan Yang
Abstract
Abstract Though large amounts of work with artificial intelligence are used in typhoon rainfall forecasting, the predictive skills of existing models are unsatisfactory. To address this problem, this study aims to propose an improved cluster‐wise typhoon rainfall forecasting model that integrates the grid‐search cross‐validation method with machine learning and deep learning (DL) models including support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), convolutional neural network (CNN), and long short‐term memory (LSTM). Grid‐search cross‐validation is a modified parameterization technique that helps to find the best parameters for machine/DL models. In the first stage, a second‐order polynomial regression model was used to cluster the typhoon track; and in the second stage, cluster‐wise typhoon rainfall was recognized within a 500 km radius from each typhoon center. After that, a modified cluster‐wise typhoon rainfall forecasting model was proposed using cluster‐wise antecedent hourly typhoon rainfall within this distance for 1–6 hr lead time. Results show that the proposed model based on the SVM, RF, AdaBoost, CNN, and LSTM is capable of providing more accurate forecasts (the efficiency of the forecast is increased by 45%–90%) than the existing typhoon rainfall forecasting models that are based on SVM with a genetic algorithm, RF, artificial neural network, multilayer perceptron network, and deep neural network. Therefore, the current study recommends using cluster‐wise typhoon rainfall forecasting model with a grid‐search cross‐validation method for disaster prevention and mitigation.