MLR and ANN models of significant wave height on the west coast of India

Asma, Senay; Sezer, Ahmet; Özdemir, Özer

doi:10.1016/j.cageo.2012.05.032

Cited by 44 publications

(10 citation statements)

References 32 publications

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“…Our preliminary modelling showed that even though it is possible to have ANN models with three outputs, the accuracy generally decreases due to the complexity of the network. This finding is in line with the existing knowledge about the performance of ANN networks (Asma et al, 2012;Kamranzad et al, 2011;Zamani et al, 2008).…”

Section: Study Area and Evaluation Of The Datasupporting

confidence: 91%

“…Statistical techniques have been developed recently. Artificial neural networks (ANNs) are one of the most commonly used approaches (Asma et al, 2012;Deo and Naidu, 1998;Deo and Jagdale, 2003;Deo et al, 2001;Jain and Deo, 2007;Londhe and Panchang, 2006;Makarynskyy, 2004;Mase et al, 2007;Ozger, 2010;Tsai et al, 2002;Tseng et al, 2007;Zamani et al, 2008). Regression-based models have also been used by many researchers such as Malmberg et al (2005), Ho and Yim (2006) and Roulston et al (2005).…”

Section: Introductionmentioning

confidence: 99%

“…Makarynskyy et al (2005) used two different neural network strategies to forecast significant wave heights and zero-up-crossing wave periods, and showed the suitability of the artificial neural technique for short-term forecasts of the wave parameters. Asma et al (2012) employed multiple linear regression (MLR) and ANN models for prediction of the significant wave height and found that ANN provides smaller MSE for prediction of this parameter. Reikard (2009) used data sets from four Pacific Ocean sites to evaluate the ability of time-series models for forecasting ocean wave energy.…”

Section: Introductionmentioning

confidence: 99%

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Wave energy forecasting using artificial neural networks in the Caspian Sea

Hadadpour

Etemad‐Shahidi

Kamranzad³

2014

Proceedings of the Institution of Civil Engineers - Maritime En

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Providing energy without unfavourable impact on the environment is an important issue that is considered by societies. This paper focuses on forecasting the wave energy over horizons of 1-12 h, in the southern part of the Caspian Sea. For this purpose, an artificial neural network was used to obtain the wave energy flux using two different methods. First, the components of wave energy flux, including the significant wave height and peak wave period were predicted separately and the wave energy flux was calculated by combining them; and second, the wave c peak enhancement factor r density of seawater i forecasting time horizon (from 1 to 12 h) t time Proceedings of the Institution of Civil Engineers

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Section: Study Area and Evaluation Of The Datasupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wave energy forecasting using artificial neural networks in the Caspian Sea

Hadadpour

Etemad‐Shahidi

Kamranzad³

2014

Proceedings of the Institution of Civil Engineers - Maritime En

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“…However, whether or not a particular parameter actually affects the wave during a typhoon cannot be determined, rendering it devoid of physical meaning or implication. Key research in this field has been reported in Asma et al [9], Li et al [10], Makarynskyy et al [11], Sánchez-Arcilla et al [12], Suzuki et al [13], and Young [14]. ANNs use the input of parameters that affect waves during a typhoon to generate outputs after performing complex statistical computations in hidden layers.…”

Section: Introductionmentioning

confidence: 99%

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

Wei

Hsieh

2018

Water

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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.

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“…Deka and Prahlada [19] developed a hybrid model of wavelets and an ANN to forecast significant wave heights for higher lead times up to 48 h on the west coast of India. Asma et al [20] used multiple linear regressions (LR) and ANNs to describe the significant wave height off Goa, located on the west coast of India. The aforementioned studies have been widely used in hindcasting and forecasting of wave parameters.…”

Section: Introductionmentioning

confidence: 99%

Nearshore Wave Predictions Using Data Mining Techniques during Typhoons: A Case Study near Taiwan’s Northeastern Coast

Wei

2017

Energies

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Seasonal typhoons provide energy into the wave field in summer and autumn in Taiwan. Typhoons lead to abundant wave energy near the coastal area and cause storm surges that can destroy offshore facilities. The potential for wave energy can be obtained from analyzing the wave height. To develop an effective model for predicting typhoon-induced wave height near coastal areas, this study employed various popular data mining models-namely k-nearest neighbors (kNN), linear regressions (LR), model trees (M5), multilayer perceptron (MLP) neural network, and support vector regression (SVR) algorithms-as forecasting techniques. The principal component analysis (PCA) was then performed to reduce the potential variables from the original data at the first stage of data preprocessing. The experimental site was the Longdong buoy off the northeastern coast of Taiwan. Data on typhoons that occurred during 2002-2011 and 2012-2013 were collected for training and testing, respectively. This study designed four PCA cases, namely EV1, TV90, TV95, and ORI: EV1 used eigenvalues higher than 1.0 as principal components; TV90 and TV95 used the total variance percentages of 90% and 95%, respectively; and ORI used the original data. The forecast horizons varying from 1 h to 6 h were evaluated. The results show that (1) in the PCA model' cases, when the number of attributes decreases, computing time decreases and prediction error increases; (2) regarding classified wave heights, M5 provides excellent outcomes at the small wavelet wavelet level; MLP has favorable outcomes at the large wavelet and small/moderate wave levels; meanwhile, SVR gives optimal outcomes at the long wave and high/very high wave levels; and (3) for performance of lead times, MLP and SVR achieve more favorable relative weighted performance without consideration of computational complexity; however, MLP and SVR might obtain lower performance when computational complexity is considered.

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MLR and ANN models of significant wave height on the west coast of India

Cited by 44 publications

References 32 publications

Wave energy forecasting using artificial neural networks in the Caspian Sea

Wave energy forecasting using artificial neural networks in the Caspian Sea

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

Nearshore Wave Predictions Using Data Mining Techniques during Typhoons: A Case Study near Taiwan’s Northeastern Coast

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