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

Hindawi Advances in Meteorology Volume 2020, Article ID 5462040, 18 pages https://doi.org/10.1155/2020/5462040 Research Article Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions Chih-Chiang Wei Department of Marine Environmental Informatics and Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung City, Taiwan Correspondence should be addressed to Chih-Chiang Wei; [email protected] Received 27 December 2019; Accepted 8 July 2020; Published 23 July 2020 Academic Editor: 'eodore Karacostas Copyright © 2020 Chih-Chiang Wei. 'is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Taiwan, being located on a path in the west Pacific Ocean where typhoons often strike, is often affected by typhoons. 'e accompanying strong winds and torrential rains make typhoons particularly damaging in Taiwan. 'erefore, 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). 'ese 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. 'e 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%. 'ese results indicate that the inclusion of numerically simulated values in wind speed prediction models enhanced their prediction accuracy. 1. Introduction Taiwan even if the typhoon itself does not hit Taiwan. 'erefore, typhoons constitute a serious natural disaster in A typhoon is a severe natural disaster that affects tropical Taiwan. and subtropical coastal countries, and it occurs most fre- For example, the 2015 Typhoon Soudelor was the most quently in the northwestern Pacific Ocean. Taiwan is to the destructive typhoon that occurred in Taiwan in recent east of the Eurasian Continent and at the western side of the history, with gust intensity exceeding 12 on the Beaufort Pacific Ocean; its climate is intermediate between tropical wind force scale (32.7 m/s). Its strong gusts caused wide- and subtropical climate. 'us, typhoons frequently occur in spread damage to infrastructure, affecting gas supply, power Taiwan, generally in summer and fall. Typhoons affecting and utilities, transportation and communication, and Taiwan typically develop at the sea surface southeast of weather radar stations. Electricity was cut off in approxi- Taiwan, and most typhoons are accompanied by torrential mately 4.5 million households simultaneously during Ty- rains and strong winds [1]. Such rain and wind add to the phoon Soudelor—the greatest recorded number in recent damage from typhoons, posing a great threat to the trans- history. 'e economic loss from the typhoon was estimated portation, economic, agricultural, and fishery activities in to be as high as US$76 million [2]. 2 Advances in Meteorology 'erefore, we aimed to establish an accurate wind speed results to be explained in terms of physical relationships. prediction model for future typhoons, allowing for better NWP models simulate the atmosphere on user-defined grid preparation to mitigate a typhoon’s toll on life and property. scales as a moving fluid. 'rough several types of param- In this study, cutting-edge machine learning (ML) tech- eterizations, NWP models also account for the influence of niques were used to improve predictive accuracy. In general, subgrid physical processes on grid-resolved motions ML algorithms learn from a huge dataset, improving their [31, 32]. NWP models, such as the Weather Research and ability to identify patterns in the data. Specifically, ML in- Forecasting model (WRF), have been increasingly popular as volves creating algorithms to make prediction from sets of a low-cost alternative source of data for such assessments of unknown data. Given their ability to perform parameter climate parameters [33]. WRF offers a wide variety of adjustment and achieve optimization through self-learning, physical and dynamical elements to choose from; these el- neural network algorithms in ML are particularly powerful ements must be put together to form model configurations, [3]. Such algorithms have been extensively used in recent with which the model can be run [34]. However, because of wind speed prediction models, and these models are in- imperfect models and uncertain initial boundary atmo- creasingly data-driven due to developments in ML [4–11]. In spheric conditions, errors exist in the NWP output [35, 36]. the development of neural networks, multilayer perceptron Recent studies have applied the NWP model to typhoons (MLP) networks are a classic approach that are often used and tropical cyclones [37–39]. However, the prediction of and compared with other neural network models. For ex- severe meteorological phenomena (such as typhoons), ample, Wei [12] compared the accuracy of MLP with that of which result from a multiplicity of mutually interacting adaptive network-based fuzzy-inference-system neural multiscale processes, remains a major challenge for NWP networks in the construction of typhoon wind speed pre- systems [25, 40, 41]. 'ese models are typically unable to diction models. predict wind intensity with satisfactory accuracy, even at Deep learning has become possible due to the expo- short forecast times and a high horizontal resolution nential increase in computing power in recent years. 'is [42, 43]. Some studies have tried combining NWP with ML approach is the further derivation of multiple neural layers models to develop an integrated climate prediction model. from the original neural layers of a model. Such derivation For example, Zhao et al. [44] evaluated the performance and improves an algorithm’s ability to learn, better approxi- enhanced accuracy of a day-ahead wind-power forecasting mating the complex neural network structure of a human system. 'e system comprised artificial neural networks and being. For example, Hu et al. [13] formulated multilayer an NWP model. deep neural networks that were trained using data from Furthermore, to enhance the predictive accuracy of data-rich wind plants. 'ese networks extracted wind speed constructed ML models, other researchers have used nu- patterns, and the mapping was finely tuned using data from merically simulated results as input data for the construction newly constructed wind plants. Tiancheng et al. [14] pro- of ML models. For example, Zhao et al. [45] developed the posed a sandstorm prediction method that considered both ARIMAX model, where wind speed results from the WRF the effect of atmospheric movement and ground factors on simulation were chosen as an exogenous input variable. sandstorm occurrence, called improved naive Bayesian However, to the best of our knowledge, in the literature on convolutional neural network classification algorithm. short-term wind speed prediction for typhoons (or tropical In the field of neural networks, recurrent neural networks cyclones), numerically simulated values have seldom been (RNNs), which can analyze sequential (or time-series) data, used as an input variable for ML prediction models. have recently been developed [15–20]. RNNs are con- 'erefore, in relation to the construction of a ML-based nectionist models with the ability to selectively pass infor- wind speed prediction model, we evaluated the improve- mation across sequence steps while processing sequential data ments to predictive accuracy afforded by the use of nu- one element at a time [21]. 'erefore, RNNs are important, merically simulated values (by comparing between its use especially in the analysis of sequential data. A particular type and nonuse). of RNNs is long short-term memory (LSTM), a class of ar- 'erefore, our study has two primary aims: (1) develop tificial neural networks where connections between units an ML- and neural network-based wind speed prediction form a directed cycle [22] introduced the LSTM primarily to model and compare the predictive accuracy of various overcome the problem of diminishing gradients. LSTM neural network-based algorithms and (2) evaluate the im- creates

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