scholarly journals Hybrid systems using residual modeling for sea surface temperature forecasting

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Paulo S. G. de Mattos Neto ◽  
George D. C. Cavalcanti ◽  
Domingos S. de O. Santos Júnior ◽  
Eraylson G. Silva
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Hybrid Systems ◽  
Short Term Memory ◽  
Absolute Error ◽  
Sea Surface ◽  
Percentage Error ◽  
Support Vector ◽  
Temperature Forecasting ◽  
Complex Phenomena

AbstractThe sea surface temperature (SST) is an environmental indicator closely related to climate, weather, and atmospheric events worldwide. Its forecasting is essential for supporting the decision of governments and environmental organizations. Literature has shown that single machine learning (ML) models are generally more accurate than traditional statistical models for SST time series modeling. However, the parameters tuning of these ML models is a challenging task, mainly when complex phenomena, such as SST forecasting, are addressed. Issues related to misspecification, overfitting, or underfitting of the ML models can lead to underperforming forecasts. This work proposes using hybrid systems (HS) that combine (ML) models using residual forecasting as an alternative to enhance the performance of SST forecasting. In this context, two types of combinations are evaluated using two ML models: support vector regression (SVR) and long short-term memory (LSTM). The experimental evaluation was performed on three datasets from different regions of the Atlantic Ocean using three well-known measures: mean square error (MSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). The best HS based on SVR improved the MSE value for each analyzed series by $$82.26\%$$ 82.26 % , $$98.93\%$$ 98.93 % , and $$65.03\%$$ 65.03 % compared to its respective single model. The HS employing the LSTM improved $$92.15\%$$ 92.15 % , $$98.69\%$$ 98.69 % , and $$32.41\%$$ 32.41 % concerning the single LSTM model. Compared to literature approaches, at least one version of HS attained higher accuracy than statistical and ML models in all study cases. In particular, the nonlinear combination of the ML models obtained the best performance among the proposed HS versions.

scholarly journals Monthly and Quarterly Sea Surface Temperature Prediction Based on Gated Recurrent Unit Neural Network

2020 ◽  
Vol 8 (4) ◽  
pp. 249 ◽  
Author(s):  
Zhen Zhang ◽  
Xinliang Pan ◽  
Tao Jiang ◽  
Baikai Sui ◽  
Chenxi Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Short Term Memory ◽  
Absolute Error ◽  
Sea Surface ◽  
Neural Network Algorithm ◽  
Stability And Accuracy ◽  
Gated Recurrent Unit

The sea surface temperature (SST) is an important parameter of the energy balance on the Earth’s surface. SST prediction is crucial to marine production, marine protection, and climate prediction. However, the current SST prediction model still has low precision and poor stability. In this study, a medium and long-term SST prediction model is designed on the basis of the gated recurrent unit (GRU) neural network algorithm. This model captures the SST time regularity by using the GRU layer and outputs the predicted results through the fully connected layer. The Bohai Sea, which is characterized by a large annual temperature difference, is selected as the study area, and the SSTs on different time scales (monthly and quarterly) are used to verify the practicability and stability of the model. The results show that the designed SST prediction model can efficiently fit the results of the real sea surface temperature, and the correlation coefficient is above 0.98. Regardless of whether monthly or quarterly data are used, the proposed network model performs better than long short-term memory in terms of stability and accuracy when the length of the prediction increases. The root mean square error and mean absolute error of the predicted SST are mostly within 0–2.5 °C.

scholarly journals A Novel Method for Sea Surface Temperature Prediction Based on Deep Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xuan Yu ◽  
Suixiang Shi ◽  
Lingyu Xu ◽  
Yaya Liu ◽  
Qingsheng Miao ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Root Mean Square ◽  
Spatial Information ◽  
Sea Surface ◽  
The Yellow Sea ◽  
Percentage Error ◽  
Good Prediction ◽  
Mean Square

Sea surface temperature (SST) forecasting is the task of predicting future values of a given sequence using historical SST data, which is beneficial for observing and studying hydroclimatic variability. Most previous studies ignore the spatial information in SST prediction and the forecasting models have limitations to process the large-scale SST data. A novel model of SST prediction integrated Deep Gated Recurrent Unit and Convolutional Neural Network (DGCnetwork) is proposed in this paper. The DGCnetwork has a compact structure and focuses on learning deep long-term dependencies in SST time series. Temporal information and spatial information are all included in our procedure. Differential Evolution algorithm is applied in order to configure DGCnetwork’s optimum architecture. Optimum Interpolation Sea Surface Temperature (OISST) data is selected to conduct experiments in this paper, which has good temporal homogeneity and feature resolution. The experiments demonstrate that the DGCnetwork significantly obtains excellent forecasting result, predicting SST by different lengths flexibly and accurately. On the East China Sea dataset and the Yellow Sea dataset, the accuracy of the prediction results is above 98% on the whole and all mean absolute error (MAE) values are lower than 0.33°C. Compared with the other models, root mean square error (RMSE), root mean square percentage error (RMSPE), and mean absolute percentage Error (MAPE) of the proposed approach reduce at least 0.1154, 0.2594, and 0.3938. The experiments of SST time series show that the DGCnetwork model maintains good prediction results, better performance, and stronger stability, which has reached the most advanced level internationally.

scholarly journals Koreksi Bias Statistik Pada Data Prediksi Suhu Permukaan Air Laut Di Wilayah Indian Ocean Dipole Barat Dan Timur

2021 ◽  
Vol 3 (1) ◽  
pp. 9-17
Author(s):  
Mohamad Khoirun Najib ◽  
Sri Nurdiati
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mapping Method ◽  
Sea Surface ◽  
Actual Data ◽  
Weather Forecasts ◽  
Temperature Forecasting ◽  
Corrected Data ◽  
The Indian Ocean

The IOD can be measured using the Dipole Mode Index (DMI) which is calculated based on the sea surface temperature in the Indian Ocean. Therefore, DMI can be predicted using sea surface temperature forecasting data, such as data provided by the European Center for Medium-Range Weather Forecasts (ECMWF). However, the data still has a bias as compared to the actual data, so to get a more accurate prediction, corrected data is needed. Therefore, the aim of this study is to predict DMI based on sea surface temperature forecasting data that has been corrected for bias using the quantile mapping method, a method that connects the distribution of forecasting and actual data. The results showed that the DMI prediction using corrected data was more accurate than the DMI prediction using ECMWF data. DMI predictions using corrected data have high accuracy to predict IOD events in October-April.

scholarly journals Seasonal prediction of winter extreme precipitation over Canada by support vector regression

2011 ◽  
Vol 15 (1) ◽  
pp. 65-74 ◽  
Author(s):  
Z. Zeng ◽  
W. W. Hsieh ◽  
A. Shabbar ◽  
W. R. Burrows
Keyword(s):  
Linear Regression ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Support Vector Regression ◽  
Winter Season ◽  
Sea Surface ◽  
The Arctic ◽  
Support Vector ◽  
Climate Indices ◽  
The Pacific

Abstract. For forecasting the maximum 5-day accumulated precipitation over the winter season at lead times of 3, 6, 9 and 12 months over Canada from 1950 to 2007, two nonlinear and two linear regression models were used, where the models were support vector regression (SVR) (nonlinear and linear versions), nonlinear Bayesian neural network (BNN) and multiple linear regression (MLR). The 118 stations were grouped into six geographic regions by K-means clustering. For each region, the leading principal components of the winter maximum 5-d accumulated precipitation anomalies were the predictands. Potential predictors included quasi-global sea surface temperature anomalies and 500 hPa geopotential height anomalies over the Northern Hemisphere, as well as six climate indices (the Niño-3.4 region sea surface temperature, the North Atlantic Oscillation, the Pacific-North American teleconnection, the Pacific Decadal Oscillation, the Scandinavia pattern, and the East Atlantic pattern). The results showed that in general the two robust SVR models tended to have better forecast skills than the two non-robust models (MLR and BNN), and the nonlinear SVR model tended to forecast slightly better than the linear SVR model. Among the six regions, the Prairies region displayed the highest forecast skills, and the Arctic region the second highest. The strongest nonlinearity was manifested over the Prairies and the weakest nonlinearity over the Arctic.

scholarly journals Seasonal prediction of winter extreme precipitation over Canada by support vector regression

2010 ◽  
Vol 7 (3) ◽  
pp. 3521-3550 ◽  
Author(s):  
Z. Zeng ◽  
W. W. Hsieh ◽  
A. Shabbar ◽  
W. R. Burrows
Keyword(s):  
Linear Regression ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Support Vector Regression ◽  
Extreme Precipitation ◽  
Sea Surface ◽  
The Arctic ◽  
Support Vector ◽  
Climate Indices ◽  
The Pacific

Abstract. For forecasting the maximum 5-d accumulated precipitation over the winter season at lead times of 3, 6, 9 and 12 months over Canada from 1950 to 2007, two nonlinear and two linear regression models were used, where the models were support vector regression (SVR) (nonlinear and linear versions), nonlinear Bayesian neural network (BNN) and multiple linear regression (MLR). The 118 stations were grouped into six geographic regions by K-means clustering. For each region, the leading principal components of the winter extreme precipitation were the predictands. Potential predictors included quasi-global sea surface temperature anomalies and 500 hPa geopotential height anomalies over the Northern Hemisphere, as well as six climate indices (the Niño-3.4 region sea surface temperature, the North Atlantic Oscillation, the Pacific-North American teleconnection, the Pacific Decadal Oscillation, the Scandinavia pattern, and the East Atlantic pattern). The results showed that in general the two robust SVR models tended to have better forecast skills than the two non-robust models (MLR and BNN), and the nonlinear SVR model tended to forecast slightly better than the linear SVR model. Among the six regions, the Eastern Prairies region displayed the highest forecast skills, and the Arctic region the second highest. The strongest nonlinearity was manifested over the Eastern Prairies and the weakest nonlinearity over the Arctic.

Sign in / Sign up

Export Citation Format

Share Document