Genetic algorithm and deep learning models compared for swell wave height prediction

IF 1.9 4区 地球科学 Q2 GEOCHEMISTRY & GEOPHYSICS
Mourani Sinha , Susmita Biswas , Swadhin Banerjee
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引用次数: 1

Abstract

A comparative study has been conducted between genetic algorithm (GA) and deep learning models to predict swell wave heights in the Bay of Bengal (BOB) region. To simulate the required parameter SWAN (Simulating Waves Nearshore) model is integrated with daily 25 km wind from 2009 to 2018 for July and December separately representing the southwest and northeast monsoons respectively. For the BOB region empirical orthogonal function (EOF) analysis is applied on the swell parameter to study the spatial and temporal patterns. GA is applied on the principal component of swell wave heights to generate a forecast explicit equation and thus a basin scale EOF-GA model is established. Next a grid (200 N, 900E) is chosen in the head bay region and the outcomes of the standalone GA model and the deep learning models are compared to predict the time series data of swell wave heights (SWS). It is observed that the performances of the deep learning model is better during the calm conditions in December than the rough seas in July. Another grid (150 N, 820E) is chosen along the east coast through which the severe cyclonic storm PHETHAI (13–18 December 2018) passed and the model accuracies are tested. The EOF-GA model serves as an effective computationally cheap basin scale forecast model. Thus, both the genetic algorithm and deep learning models can be developed and utilized for normal and extreme wave prediction having wide application in the ocean engineering domains.

遗传算法与深度学习模型在涌浪波高预测中的比较
对遗传算法(GA)和深度学习模型进行了比较研究,以预测孟加拉湾(BOB)地区的涌浪高度。为了模拟所需参数,SWAN(模拟近岸波浪)模型与2009年至2018年7月和12月的25公里日风相结合,分别代表西南季风和东北季风。对于BOB区域,将经验正交函数(EOF)分析应用于涌浪参数,以研究其空间和时间模式。将遗传算法应用于涌浪高度的主分量,生成预测显式方程,建立了流域尺度EOF-GA模型。接下来,在前海湾区域选择网格(200N,900E),并比较独立GA模型和深度学习模型的结果,以预测涌浪高度(SWS)的时间序列数据。据观察,深度学习模型在12月的平静条件下的性能要好于7月的波涛汹涌。在强气旋风暴PHETHAI(2018年12月13日至18日)经过的东海岸选择了另一个网格(150 N,820E),并对模型精度进行了测试。EOF-GA模型是一种有效的、计算成本低廉的流域尺度预测模型。因此,遗传算法和深度学习模型都可以开发并用于正常和极端波浪预测,在海洋工程领域有着广泛的应用。
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来源期刊
Dynamics of Atmospheres and Oceans
Dynamics of Atmospheres and Oceans 地学-地球化学与地球物理
CiteScore
3.10
自引率
5.90%
发文量
43
审稿时长
>12 weeks
期刊介绍: Dynamics of Atmospheres and Oceans is an international journal for research related to the dynamical and physical processes governing atmospheres, oceans and climate. Authors are invited to submit articles, short contributions or scholarly reviews in the following areas: •Dynamic meteorology •Physical oceanography •Geophysical fluid dynamics •Climate variability and climate change •Atmosphere-ocean-biosphere-cryosphere interactions •Prediction and predictability •Scale interactions Papers of theoretical, computational, experimental and observational investigations are invited, particularly those that explore the fundamental nature - or bring together the interdisciplinary and multidisciplinary aspects - of dynamical and physical processes at all scales. Papers that explore air-sea interactions and the coupling between atmospheres, oceans, and other components of the climate system are particularly welcome.
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