Coastal Wave Height Prediction using Recurrent Neural Networks (RNNs) in the South Caspian Sea

There are successful experiences with the application of ANN and ensemble-based data assimilation methods in the field of flood forecasting and estuary flow. In the present work, the combination of dynamic Artificial Neural Network and Ensemble Kalman Filter (EnKF) is applied on wind-wave data. ANN is used for the time propagation mechanism that governs the time evolution of the system state. The system state consists of the significant wave height that is affected by wind speed and wind direction. The relevant inputs are selected by analysing the Average Mutual Information. By help of the observations, the EnKF will correct the output of the ANN to find the best estimate of the wave height. A combination of ANN with EnKF acts as an output correction scheme. To deal with the time-delayed states, the extended state vector is taken and the dynamic equation of the extended state vector is used in EnKF. Application of the proposed scheme is examined by using five-month hourly buoy measurement at the Caspian Sea and several model runs with different assimilation–forecast cycles.The coefficient of performance and root mean square error are used to access performance of the method.

Download Full-text

Nearshore two-step typhoon wind-wave prediction using deep recurrent neural networks

Journal of Hydroinformatics ◽

10.2166/hydro.2019.084 ◽

2019 ◽

Vol 22 (2) ◽

pp. 346-367 ◽

Cited By ~ 3

Author(s):

Chih-Chiang Wei ◽

Ju-Yueh Cheng

Keyword(s):

Neural Networks ◽

Wave Height ◽

Recurrent Neural Networks ◽

Wind Wave ◽

Weather Forecasting ◽

Wind Waves ◽

Heavy Rain ◽

Strong Wind ◽

Climate Conditions ◽

Wave Prediction

Abstract Because Taiwan is located within the subtropical high and on the primary path of western Pacific typhoons, the interaction of these two factors easily causes extreme climate conditions, with strong wind carrying heavy rain and huge wind waves. To obtain precise wind-wave data for weather forecasting and thus minimize the threat posed by wind waves, this study proposes a two-step wind-wave prediction (TSWP) model to predict wind speed and wave height. The TSWP model is further divided into TSWP1, which uses data attributes at the current moment as input values and TSWP2, which uses observations from a lead time and predicts data attributes from input data. The classical one-step wave height prediction (OSWP) approach, which directly predicts wave height, was used as a benchmark to test TSWP. Deep recurrent neural networks (DRNNs) can be used to construct TSWP and OSWP approach-based models in wave height predictions. To compare with the accuracy achieved using DRNNs, linear regression, multilayer perceptron (MLP) networks, and deep neural networks (DNNs) were tested as benchmarks. The Guishandao Buoy Station located off the northeastern shore of Taiwan was used for a case study. The results were as follows: (1) compared with the shallower MLP network, the DNN and DRNN demonstrated a lower prediction error. (2) Compared with OSWP, TSWP1 and TSWP2 provided more accurate results. Therefore, the TSWP approach using a DRNN algorithm can effectively predict wind-wave heights.

Download Full-text

Wave Height and Peak Wave Period Prediction Using Recurrent Neural Networks

2020 International Conference on Computational Intelligence (ICCI) ◽

10.1109/icci51257.2020.9247805 ◽

2020 ◽

Author(s):

Kazuki Osawa ◽

Hiroki Yamaguchi ◽

Muhammad Umair ◽

Manzoor Ahmed Hashmani ◽

Keiichi Horio

Keyword(s):

Neural Networks ◽

Wave Height ◽

Recurrent Neural Networks ◽

Wave Period

Download Full-text

Analysis of feed forward and recurrent neural networks in predicting the significant wave height at the moored buoys in Bay of Bengal

2017 International Conference on Communication and Signal Processing (ICCSP) ◽

10.1109/iccsp.2017.8286717 ◽

2017 ◽

Cited By ~ 2

Author(s):

P. Abhigna ◽

S. Jerritta ◽

R. Srinivasan ◽

V. Rajendran

Keyword(s):

Neural Networks ◽

Wave Height ◽

Bay Of Bengal ◽

Recurrent Neural Networks ◽

Significant Wave Height ◽

Feed Forward ◽

Significant Wave

Download Full-text

Forecasting of Typhoon-Induced Wind-Wave by Using Convolutional Deep Learning on Fused Data of Remote Sensing and Ground Measurements

Sensors ◽

10.3390/s21155234 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5234

Author(s):

Chih-Chiang Wei ◽

Hao-Chun Chang

Keyword(s):

Neural Networks ◽

Wind Speed ◽

Wave Height ◽

Prediction Models ◽

Prediction Performance ◽

Series Data ◽

Northwestern Pacific ◽

Wind Speed Prediction ◽

Speed Prediction ◽

Height Prediction

Taiwan is an island, and its economic activities are primarily dependent on maritime transport and international trade. However, Taiwan is also located in the region of typhoon development in the Northwestern Pacific Basin. Thus, it frequently receives strong winds and large waves brought by typhoons, which pose a considerable threat to port operations. To determine the real-time status of winds and waves brought by typhoons near the coasts of major ports in Taiwan, this study developed models for predicting the wind speed and wave height near the coasts of ports during typhoon periods. The forecasting horizons range from 1 to 6 h. In this study, the gated recurrent unit (GRU) neural networks and convolutional neural networks (CNNs) were combined and adopted to formulate the typhoon-induced wind and wave height prediction models. This work designed two wind speed prediction models (WIND-1 and WIND-2) and four wave height prediction models (WAVE-1 to WAVE-4), which are based on the WIND-1 and WIND-2 model outcomes. The Longdong and Liuqiu Buoys were the experiment locations. The observatory data from the ground stations and buoys, as well as radar reflectivity images, were adopted. The results indicated that, first, WIND-2 has a superior wind speed prediction performance to WIND-1, where WIND-2 can be used to identify the temporal and spatial changes in wind speeds using ground station data and reflectivity images. Second, WAVE-4 has the optimal wave height prediction performance, followed by WAVE-3, WAVE-2, and WAVE-1. The results of WAVE-4 revealed using the designed models with in-situ and reflectivity data directly yielded optimal predictions of the wind-based wave heights. Overall, the results indicated that the presented combination models were able to extract the spatial image features using multiple convolutional and pooling layers and provide useful information from time-series data using the GRU memory cell units. Overall, the presented models could exhibit promising results.

Download Full-text

Coevolutionary Recurrent Neural Networks for Prediction of Rapid Intensification in Wind Intensity of Tropical Cyclones in the South Pacific Region

Neural Information Processing - Lecture Notes in Computer Science ◽

10.1007/978-3-319-26555-1_6 ◽

2015 ◽

pp. 43-52 ◽

Cited By ~ 1

Author(s):

Rohitash Chandra ◽

Kavina S. Dayal

Keyword(s):

Neural Networks ◽

Tropical Cyclones ◽

Recurrent Neural Networks ◽

South Pacific ◽

Rapid Intensification ◽

Pacific Region ◽

The South ◽

Wind Intensity

Download Full-text

Optimization of Artificial Neural Networks Based Models for Wave Height Prediction

E3S Web of Conferences ◽

10.1051/e3sconf/202017303007 ◽

2020 ◽

Vol 173 ◽

pp. 03007

Author(s):

Gheorghe Stăvărache ◽

Sorin Ciortan ◽

Eugen Rusu

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Wave Height ◽

Neural Model ◽

Neural Models ◽

Mathematical Formulas ◽

Height Prediction ◽

Energy Conversion System ◽

Artificial Neural ◽

And Training

For an efficient wave energy extraction, the evolution of some specific parameters must be known. These parameters, like significant wave height and period, are mainly determined by the wind speed and influenced by some sea environment characteristics. Their evolution in time is one of the basic information necessary for designing of an accurate energy conversion system. In many scientific works the benefits of artificial neural networks based modeling are presented. These models allow the prediction and optimization of the wave parameters starting from experimentally acquired data. Due to specific calculus method of the artificial neural networks, in order to obtain accurate results, a very important step is the appropriate neural model design. If the model is optimal correlated with the data processed, the results obtained can be more significant than those coming from the mathematical formulas. The main neural models parameters that must be taken into account for an optimal design are model structure, transfer function and training algorithm. This paper presents an investigation of the results obtained with different models, proving that for a specific dataset a specific neural model offers the best results. Several models are analyzed, for a dataset corresponding to specific point in Black Sea and a comparison of results is presented.

Download Full-text