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Related Experiment Video

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Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

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Published on: June 24, 2019

Coati optimization algorithm based Deep Convolutional Forest method for prediction of atmospheric and oceanic

Sundeep Raj^1,2, Rajendra Kumar Bharti³, K C Tripathi⁴

¹CSE, VMSB Uttarakhand Technical University, Deharadun, India. sundeepraj1@gmail.com.

Scientific Reports

|September 28, 2024

Summary

This summary is machine-generated.

A new Coati Optimization Algorithm-based Deep Convolutional Forest (COA-DCF) method improves ocean surface temperature anomaly forecasts. This approach enhances prediction accuracy for extreme weather events by analyzing key ocean and soil variables.

Keywords:

COA-DCF Feedback connection MAE Optimization RMSE Sea surface temperature

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Area of Science:

Oceanography
Climate Science
Artificial Intelligence

Background:

Ocean temperature significantly influences global climate and extreme weather events like droughts and floods.
Current numerical models for sea surface temperature (SST) forecasting have limitations in localized accuracy.
Accurate real-time SST predictions are crucial for understanding and mitigating climate change impacts.

Purpose of the Study:

To develop an advanced method for improving the accuracy of ocean surface temperature anomaly forecasts.
To enhance prediction capabilities in high-precision areas where traditional models fall short.
To leverage deep learning and optimization algorithms for more reliable climate predictions.

Main Methods:

Proposed the Coati Optimization Algorithm-based Deep Convolutional Forest (COA-DCF) method.
Utilized a Deep Convolutional Forest (DCF) classifier trained with the COA optimization algorithm.
Incorporated historical data (1-10 days) of SST, Sea Surface Height (SSH), soil moisture, and wind speed for forecasting.

Main Results:

The COA-DCF method demonstrated improved accuracy in forecasting ocean surface temperature anomalies.
Achieved low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values.
Obtained a high Pearson's correlation coefficient (r) of 0.493, 0.487, and 0.4733, indicating strong predictive performance.

Conclusions:

The COA-DCF method offers a significant advancement in ocean temperature anomaly prediction.
This approach enhances the performance of deep learning models for climate-related forecasting.
Improved localized SST forecasts can aid in better prediction and management of extreme weather events.