Predicting shoreline changes using deep learning techniques with Bayesian optimisation

IF 4.5 2区 工程技术 Q1 ENGINEERING, CIVIL
Tharindu Manamperi , Alma Rahat , Doug Pender , Demetra Cristaudo , Rob Lamb , Harshinie Karunarathna
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引用次数: 0

Abstract

Accurate prediction of shoreline change is vital for effective coastal planning and management, especially under increasing climate variabilities. This study explores the applicability of deep learning (DL) techniques, particularly Long Short-Term Memory (LSTM) and Convolutional Neural Network-LSTM (CNN-LSTM) models, for shoreline forecasting at monthly to inter-annual timescales, under two modelling approaches—direct input (DI) and autoregressive (AR). All models demonstrated the ability to reproduce temporal shoreline variability, while the autoregressive DL models were performing better.
Further, a noise impact assessment revealed that seasonal decomposition and noise filtering significantly enhanced the model performance. In particular, the models using 52-week data decomposition and residual noise reduction improved the model performance. The reduction of data noises also resulted in narrower ensemble prediction envelopes, indicating that ensemble candidate models behave with low diversity. The temporal data resolution analysis showed that lower data resolutions reduce the predictive performance of the model and at least fortnightly data are required to satisfactorily capture the trend of variability of the shoreline position at this beach.
The use of ensemble predictions, derived from a selected subset of model trials based on their collective performance, proved beneficial by capturing diverse temporal behaviours, thereby offering a quasi-probabilistic forecast with minimal computational cost. Overall, the study underscores the potential of DL models, particularly with autoregressive architectures, for reliable and transferable shoreline change prediction. It also emphasizes the importance of data quality, resolution, and preprocessing in improving model robustness, laying the groundwork for future research into use of DL in multi-scale shoreline predictions.
利用贝叶斯优化的深度学习技术预测海岸线变化
海岸线变化的准确预测对于有效的沿海规划和管理至关重要,特别是在气候变化日益增加的情况下。本研究探讨了深度学习(DL)技术,特别是长短期记忆(LSTM)和卷积神经网络-LSTM (CNN-LSTM)模型,在直接输入(DI)和自回归(AR)两种建模方法下,对月至年际时间尺度的海岸线预测的适用性。所有模型都显示出再现时间海岸线变化的能力,而自回归DL模型表现得更好。此外,噪声影响评估表明,季节分解和噪声滤波显著提高了模型的性能。特别是采用52周数据分解和残差降噪的模型,提高了模型的性能。数据噪声的降低也导致集合预测包络更窄,表明集合候选模型表现出较低的多样性。时间数据分辨率分析表明,较低的数据分辨率降低了模型的预测性能,至少需要两周的数据才能令人满意地捕捉到该海滩岸线位置的变化趋势。集合预测的使用是基于模型试验的集体表现,通过捕获不同的时间行为,从而以最小的计算成本提供准概率预测,证明是有益的。总的来说,该研究强调了DL模型的潜力,特别是具有自回归架构的模型,用于可靠和可转移的海岸线变化预测。它还强调了数据质量、分辨率和预处理在提高模型鲁棒性方面的重要性,为未来在多尺度海岸线预测中使用深度学习的研究奠定了基础。
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来源期刊
Coastal Engineering
Coastal Engineering 工程技术-工程:大洋
CiteScore
9.20
自引率
13.60%
发文量
0
审稿时长
3.5 months
期刊介绍: Coastal Engineering is an international medium for coastal engineers and scientists. Combining practical applications with modern technological and scientific approaches, such as mathematical and numerical modelling, laboratory and field observations and experiments, it publishes fundamental studies as well as case studies on the following aspects of coastal, harbour and offshore engineering: waves, currents and sediment transport; coastal, estuarine and offshore morphology; technical and functional design of coastal and harbour structures; morphological and environmental impact of coastal, harbour and offshore structures.
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