Exploring Technological Innovation in Wave Forecasting Using Machine Learning - A Literature Analysis

Aug 4, 2026

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Rapid technological progress is making innovation in wave forecasting increasingly important for managing the multifaceted effects of climate change on marine systems. This qualitative study employs a Systematic Literature Review (SLR) to analyze research from 2014 to 2024 in Scopus, DOAJ, and Google Scholar. Findings show that deep learning, ensemble learning, transfer learning, and data augmentation improve both prediction accuracy and efficiency. Models using CNNs and RNNs capture non-linear wave patterns, while ensemble methods strengthen forecast robustness under dynamic ocean conditions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Abstract

Introduction

Importance of wave forecasting across sectors

Technological advances in sensors, satellites, and numerical models

Role of machine learning in wave forecasting

Exploring Technological Innovation in Wave Forecasting Using Machine Learning: A Literature Analysis Muhammad Hapipi Edu Tamora Research Centre, West Lombok City, West Nusa Tenggara. Indonesia Corespodence: [hapipi.ntb@gmail.com](hapipi.ntb@gmail.com) Received: July 11, 2024 | Revised: July 29, 2024 | Accepted: August 30, 2024 [https://doi.org/10.31629/jmps.v1i2.6941](https://doi.org/10.31629/jmps.v1i2.6941) ABSTRACK In the face of rapid technological advancements, innovations in wave forecasting are increasingly essential for effectively addressing the complex impacts of climate change. This study aims to explore technological developments in wave forecasting that can manage the complexities related to climate change and enhance the accuracy and efficiency of predictions in dynamic marine environments. Employing a qualitative approach through a Systematic Literature Review (SLR) methodology, the research focuses on literature from databases such as Scopus, DOAJ, and Google Scholar, specifically targeting publications from 2014 to 2024. Recent findings reveal that advancements in machine learning technologies, including deep learning, ensemble learning, transfer learning, and data augmentation, have significantly improved the precision and efficiency of wave forecasting models. Techniques like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been particularly effective in capturing complex, non-linear patterns within wave data, enhancing the overall prediction accuracy. Ensemble learning methods have further contributed by increasing the stability and robustness of forecasts. Moreover, transfer learning and data augmentation play vital roles in adapting these models to rapidly changing environmental conditions, making them highly relevant in the context of climate change. These approaches are crucial for models to remain adaptable and responsive to dynamic oceanic conditions influenced by climate variability. The insights derived from this study are expected to provide valuable direction for the future development of machine learning-based wave forecasting models, emphasizing the need for innovative techniques that can accommodate the complexities and uncertainties brought about by climate change. Keyword: Technological Innovation, Wave Forecasting, Machine Learning, Prediction Models INTRODUCTION Wave forecasting plays a crucial role in various sectors, including maritime navigation, coastal safety, renewable energy, and disaster mitigation. In maritime navigation, accurate wave condition information is essential for determining safe and efficient sailing routes, thereby reducing the risk of accidents and optimizing travel time and costs (Aslam et al., 2020) . In the coastal safety sector, precise wave forecasting aids in flood and coastal erosion management, as well as in preparing evacuation and protection measures for coastal communities (Arkema et al., 2017) . Additionally, in the development of renewable energy, particularly wave energy, a deep understanding of wave patterns enables the optimization of device design and placement, thereby enhancing energy production efficiency (Garcia-Teruel & Forehand, 2021) . In disaster mitigation efforts, such as tsunamis and storms, accurate wave prediction is critical for providing early warnings and reducing the destructive impact of these events (Angove et al., 2019) . In the past decade, technology has undergone significant advancements to support wave forecasting, particularly through progress in sensors, satellites, and numerical models. Advanced sensors can now measure various ocean parameters in real-time, such as wave height, currents, sea surface temperature, and salinity, all of which are crucial data for developing predictive wave models (Isern-Fontanet et al., 2017) . Additionally, satellite technology has advanced with higher resolution and observation frequency, enabling more detailed and extensive monitoring of

FAQs

What is the main purpose of the literature analysis?

The study explores technological developments in wave forecasting that help handle climate-change complexities and improve prediction accuracy and efficiency in dynamic marine environments.

Which time range and databases were used in the Systematic Literature Review?

The SLR analyzes publications from 2014 to 2024, using databases including Scopus, DOAJ, and Google Scholar.

How do machine learning methods improve wave forecasting performance?

Deep learning and architectures such as CNNs and RNNs capture complex non-linear wave patterns, while ensemble learning increases stability and robustness. Transfer learning and data augmentation help adapt models to rapidly changing environmental conditions.

Aug 4, 2026

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