Raj Sundeep, Bharti Rajendra Kumar, Tripathi K C
CSE, VMSB Uttarakhand Technical University, Deharadun, India.
SSET, Sharda University, Greater Noida, India.
Sci Rep. 2024 Sep 27;14(1):22160. doi: 10.1038/s41598-024-73811-z.
Droughts and floods are examples of extreme weather events that can result from changes in ocean temperature. Ocean temperature is a key component of the global open sea system. Currently, real-time sea surface temperature (SST) forecasts are generated by numerical models based on physics principles and influenced by boundary and initial conditions. These models generally perform better over large areas than at specific locations. To address this and improve prediction accuracy, particularly in high-precision areas, the Coati Optimization Algorithm-based Deep Convolutional Forest (COA-DCF) method is proposed. This optimization approach is utilized to train the Deep Convolutional Forest (DCF) classifier, which then applies the prediction strategy. The COA-DCF method forecasts ocean surface temperature anomalies by considering key variables such as SST, Sea Surface Height (SSH), soil moisture, and wind speed, using historical data ranging from 1 to 10 days across six different locations. The proposed method achieves improved accuracy with low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values, and a high Pearson's correlation coefficient (r) of 0.493, 0.487, and 0.4733, respectively, thereby enhancing the overall performance of the deep learning model.
干旱和洪水是可能由海洋温度变化导致的极端天气事件的例子。海洋温度是全球公海系统的一个关键组成部分。目前，基于物理原理并受边界条件和初始条件影响的数值模型生成实时海表面温度（SST）预报。这些模型在大面积区域通常比在特定位置表现得更好。为了解决这个问题并提高预测精度，特别是在高精度区域，提出了基于南美浣熊优化算法的深度卷积森林（COA-DCF）方法。这种优化方法用于训练深度卷积森林（DCF）分类器，然后应用预测策略。COA-DCF方法通过考虑诸如SST、海面高度（SSH）、土壤湿度和风速等关键变量，利用六个不同位置1至10天的历史数据来预测海洋表面温度异常。所提出的方法实现了更高的精度，均方根误差（RMSE）和平均绝对误差（MAE）值较低，皮尔逊相关系数（r）分别高达0.493、0.487和0.4733，从而提高了深度学习模型的整体性能。
