Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions

Chih-Chiang Wei

doi:10.1155/2020/5462040

Abstract

Taiwan, being located on a path in the west Pacific Ocean where typhoons often strike, is often affected by typhoons. The accompanying strong winds and torrential rains make typhoons particularly damaging in Taiwan. Therefore, we aimed to establish an accurate wind speed prediction model for future typhoons, allowing for better preparation to mitigate a typhoon’s toll on life and property. For more accurate wind speed predictions during a typhoon episode, we used cutting-edge machine learning techniques to construct a wind speed prediction model. To ensure model accuracy, we used, as variable input, simulated values from the Weather Research and Forecasting model of the numerical weather prediction system in addition to adopting deeper neural networks that can deepen neural network structures in the construction of estimation models. Our deeper neural networks comprise multilayer perceptron (MLP), deep recurrent neural networks (DRNNs), and stacked long short-term memory (LSTM). These three model-structure types differ by their memory capacity: MLPs are model networks with no memory capacity, whereas DRNNs and stacked LSTM are model networks with memory capacity. A model structure with memory capacity can analyze time-series data and continue memorizing and learning along the time axis. The study area is northeastern Taiwan. Results showed that MLP, DRNN, and stacked LSTM prediction error rates increased with prediction time (1–6 hours). Comparing the three models revealed that model networks with memory capacity (DRNN and stacked LSTM) were more accurate than those without memory capacity. A further comparison of model networks with memory capacity revealed that stacked LSTM yielded slightly more accurate results than did DRNN. Additionally, we determined that in the construction of the wind speed prediction model, the use of numerically simulated values reduced the error rate approximately by 30%. These results indicate that the inclusion of numerically simulated values in wind speed prediction models enhanced their prediction accuracy.

Highlights

A typhoon is a severe natural disaster that affects tropical and subtropical coastal countries, and it occurs most frequently in the northwestern Pacific Ocean
Two datasets could be built, one comprising typhoon data and the other comprising wind simulation results from the numerical weather prediction (NWP) model. e datasets were split into testing and trainingvalidation subsets. e training-validation set was used for the learning of several machine learning (ML)-based wind velocity prediction models, and the testing set was used for the identification of the optimal prediction model among these models
We evaluated whether forecast accuracy in ML-based models improved when NWP numerical solutions were used as input

Summary

Introduction

A typhoon is a severe natural disaster that affects tropical and subtropical coastal countries, and it occurs most frequently in the northwestern Pacific Ocean. Typhoons affecting Taiwan typically develop at the sea surface southeast of Taiwan, and most typhoons are accompanied by torrential rains and strong winds [1]. Such rain and wind add to the damage from typhoons, posing a great threat to the transportation, economic, agricultural, and fishery activities in Taiwan even if the typhoon itself does not hit Taiwan. The 2015 Typhoon Soudelor was the most destructive typhoon that occurred in Taiwan in recent history, with gust intensity exceeding 12 on the Beaufort wind force scale (32.7 m/s). Electricity was cut off in approximately 4.5 million households simultaneously during Typhoon Soudelor—the greatest recorded number in recent history. e economic loss from the typhoon was estimated to be as high as US$76 million [2]

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