Using Adjacent Buoy Information to Predict Wave Heights of Typhoons Offshore of Northeastern Taiwan

water Article Using Adjacent Buoy Information to Predict Wave Heights of Typhoons Offshore of Northeastern Taiwan Chih-Chiang Wei * and Chia-Jung Hsieh Department of Marine Environmental Informatics & Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung 20224, Taiwan; [email protected] * Correspondence: [email protected] Received: 2 November 2018; Accepted: 6 December 2018; Published: 7 December 2018 Abstract: In the northeastern sea area of Taiwan, typhoon-induced long waves often cause rogue waves that endanger human lives. Therefore, having the ability to predict wave height during the typhoon period is critical. The Central Weather Bureau maintains the Longdong and Guishandao buoys in the northeastern sea area of Taiwan to conduct long-term monitoring and collect oceanographic data. However, records have often become lost and the buoys have suffered other malfunctions, causing a lack of complete information concerning wind-generated waves. The goal of the present study was to determine the feasibility of using information collected from the adjacent buoy to predict waves. In addition, the effects of various factors such as the path of a typhoon on the prediction accuracy of data from both buoys are discussed herein. This study established a prediction model, and two scenarios were used to assess the performance: Scenario 1 included information from the adjacent buoy and Scenario 2 did not. An artificial neural network was used to establish the wave height prediction model. The research results demonstrated that (1) Scenario 1 achieved superior performance with respect to absolute errors, relative errors, and efficiency coefficient (CE) compared with Scenario 2; (2) the CE of Longdong (0.802) was higher than that of Guishandao (0.565); and (3) various types of typhoon paths were observed by examining each typhoon. The present study successfully determined the feasibility of using information from the adjacent buoy to predict waves. In addition, the effects of various factors such as the path of a typhoon on the prediction accuracy of both buoys were also discussed. Keywords: wave height; typhoon; buoy; neural network; prediction 1. Introduction Taiwan is located at the border of the tropical and subtropical regions of the Pacific Ocean, where typhoons often occur during summer and autumn. Most typhoons gradually form above the sea surface southeast of Taiwan and produce torrential rains and strong winds that provide considerable water resources but also cause countrywide disasters [1]. When typhoons approach Taiwan from the Pacific Ocean, the northeastern sea and coastal area (Figure1) are the first to be threatened. Because violent storms strike the sea surface with extreme wind speed that persists for a long duration, the resultant waves exhibit long wave periods and great wave heights [2]. Moreover, the topography of seabed may cause wave height to increase to a severely dangerous extent near the shore; for example, seawater intrusion may occur in lowland areas and anchored fishing boats may be capsized or dismantled because of the sudden rise of the waves or a fierce wave impact. The waters near Taiwan are prone to enormous waves generated by strong winds. Such waves contain a massive amount of energy that can be transmitted to the coast of Taiwan from extremely far away, for which reason they are commonly called long waves. They can be transmitted over hundreds or thousands of kilometers, which is why in summer and autumn surface waves often Water 2018, 10, 1800; doi:10.3390/w10121800 www.mdpi.com/journal/water Water 2018, 10, 1800 2 of 20 Water 2018, 10, 1800 2 of 20 far away, for which reason they are commonly called long waves. They can be transmitted over hundreds or thousands of kilometers, which is why in summer and autumn surface waves often suddenly occur at the east coast of Taiwan Taiwan before a typhoon comes. Usually, Usually, several big waves occur in a row, afterafter whichwhich thethe seasea surface becomes calm for a while, and then giant waves suddenly occur again. Such a pattern gives rise to the name “rogue waves”, and locals colloquially call them “mad dog waves” [[3].3]. Long waves move extremely fast and carry considerableconsiderable momentum; long waves generated by typhoons often carry a large quantity of sea water to the coast where people often get taken under by the waves because they are caught unprepared. According to the data of the Central Weather BureauBureau (CWB)(CWB) of Taiwan, atat leastleast 340 incidentsincidents of rogue waves were recorded between 2000 and 2018, causing injury or death to 568 persons. Figure 1. NortheasternNortheastern coast coast of of Taiwan Taiwan and the location of the relevant test stations. Because long waves generated by typhoons often occur in the northeastern sea of Taiwan and cause rogue waves that threaten the safety of people, it is critical to develop the ability to accurately predict wavewave heightsheights duringduring aa typhoon. typhoon. The The CWB CWB has has set set up up the the Longdong Longdong and and Guishandao Guishandao buoys buoys in inthe the northeastern northeastern sea areasea (Figurearea (Figure1) for long-term 1) for long-term monitoring monitoring and the collection and the of keycollection oceanographic of key oceanographicdata. The buoys data. record The up-to-date buoys record information up-to-date concerning information waves concerning in this area waves that can in this then area be subjected that can thento further be subjected analysis to and further used toanalysis make predictions;and used to moreover, make predictions; the information moreover, can informthe information public safety can preparationsinform publicthat safety prevent preparations adverse that outcomes. prevent However,adverse outcomes. performance However, records performance of the buoys records indicate of thethat buoys there areindicate often nothat wave there data are available often no owing wave to data data available loss or other owing malfunctions. to data loss The or present other malfunctions.study thus hypothesized The present thatstudy if wavethus hypothesized heights around that a particularif wave heights buoy around could be a predictedparticular usingbuoy coulddata from be predicted the adjacent using buoy data when from the the target adjacent buoy malfunctioned, buoy when the the target problems buoy associated malfunctioned, with buoy the problemsmalfunctioning associated could with be overcomebuoy malfunctioning and key information could be overcome regarding and the waveskey information in the target regarding area could the wavesstill be in collected the target [4]. area The could aim of still this be study collected was to[4]. evaluate The aim the of this feasibility study ofwas predicting to evaluate waves the feasibility using the ofdata predicting collected waves by the using adjacent the data buoy. collected Moreover, by th thise adjacent study assessed buoy. Moreover, the prediction this study accuracy assessed of buoy the predictiondata in light accuracy of the of influence buoy data of in various light of factors the infl suchuence as of the various path factors of typhoons. such as Thisthe path study of typhoons. used two Thisscenarios study to used develop two thescenarios prediction to develop model, the one predicti that usedon datamodel, from one the that adjacent used data buoy from and the one adjacent that did buoynot. The and study one alsothat evaluateddid not. The other study factors also that evalua mightted affect other wave factors height that predictions might affect such wave as the height path predictionsof typhoons, such the as location the path of of buoys, typhoons, and typhoonthe location intensity. of buoys, Artificial and typhoon neural intensity. networks Artificial (ANNs) neural were networksused to develop (ANNs) an were efficient used and to develop accurate an wave efficient prediction and accurate model. wave prediction model. Computation speed has increased considerably because of the rapid development of computer hardware andand software. software. Researchers Researchers have have thus thus been been able able to develop to develop numerous numerous wave predictionwave prediction models modelsfor typhoon for typhoon periods usingperiods wave using theories wave and theori complexes and computation. complex computation. Commonly usedCommonly computation used computationmodels include models numerical include models, numerical regression models, analysis, regression and analysis, ANNs. and Numerical ANNs. Numerical models use models wave usegoverning wave governing equations equations with correction with correction items generated items generated from local from differences local differences to conduct to complexconduct complexcomputations. computations. Because Because this method this method employs employs grid computing, grid computing, the conversion the conversion from afrom global a global scale scaleto a scaleto a scale suitable suitable for thefor the target target coast coast area, area, which which involves involves complicated complicated computations computations andand grid Water 2018, 10, 1800 3 of 20 selection, is often extremely time-consuming [5–8]. Regression analysis uses typhoon or weather parameters that affect wind waves during typhoons to establish a simple statistical regression. Then, the coefficient and intercept of each parameter on the regression line are identified to simulate the waves during a typhoon. However, whether or

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