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This work aims to enhance wave parameter forecasting and wave power estimation using a PCHIP-based machine learning framework.

May 16, 2026Results in Engineering0 citationsOpen Access

A PCHIP based temporal refinement in ML for wave forecasting and wave power estimation

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JVJishnu VijayVellore Institute of Technology University SVS. Vaishnavi VPV. PrabhakarVellore Institute of Technology University

Puntos clave

This work aims to enhance wave parameter forecasting and wave power estimation using a PCHIP-based machine learning framework.
Developed hybrid models using PCHIP approach with LSTM and RF for wave data analysis.
Analyzed seven datasets from the Bay of Bengal during various cyclonic conditions.
Validated models against buoy measurements for error reduction and predictive performance.
Achieved notable error reduction of about 70% in RMSE, MAE, and MAPE compared to traditional models.
Scatter plots demonstrated superior predictive performance of PCHIP-LSTM and PCHIP-RF models.
Models performed effectively under scarce data availability, ensuring accuracy during extreme weather events.

Resumen

• An upgradation of machine learning framework using PCHIP approach has been proposed for improving the wave parameters prediction. • PCHIP based LSTM and RF models were applied to buoy data from the Bay of Bengal region to predict wave parameters and estimate wave power. • Validation shows a notable error reduction of about 70% to 90% for cyclonic, pre cyclonic and post cyclonic periods. • The proposed work is found to be effective under limited data availability during normal and extreme events. This study proposes two hybrid machine learning models employing piecewise cubic hermite interpolation polynomial (PCHIP) technique : PCHIP-LSTM and PCHIP-RF, to improve the wave parameter forecasting. Seven datasets from the Bay of Bengal (13 ∘ 27′09.3″ N and 84 ∘ 08′20.4″ E), consisting cyclonic, pre-cyclonic, and post-cyclonic conditions during short-term periods between 2021 and 2023, were analysed to evaluate significant wave height (SWH) and average wave period (APD) using the proposed models. PCHIP technique is utilised to enhance the temporal resolution of data by iteratively splitting the time durations and accumulating interpolated values from the original datasets. Scatter plot results for SWH and APD shows good predictive performance of the proposed models over the traditional models. Results obtained through these models did not disturb the originality of the datasets and agree well with that of the buoy measurements and reanalysis data. Validation against buoy measurements shows a notable error reduction of about 70% in RMSE, MAE and MAPE. Friedman test and Posthoc pairwise comparison ensures the statistical significance of the proposed models thereby serves as a validation. A key advantage of the proposed approach is its effectiveness under limited data availability where continuous measurements are often sparse or interrupted during severe weather. This work not only mitigates the limitations of traditional ML models for studying small datasets but also enhances the wave power estimation in all oceanic conditions.

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Vijay et al. (Fri,) studied this question.

synapsesocial.com/papers/6a080ab3a487c87a6a40cad5 https://doi.org/https://doi.org/10.1016/j.rineng.2026.110996

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