Using the Residual Network Module to Correct the Sub-Seasonal High Temperature Forecast

The high temperature forecast of the sub-season is a severe challenge. Currently, the residual structure has achieved good results in the field of computer vision attributed to the excellent feature extraction ability. However, it has not been introduced in the domain of sub-seasonal forecasting. Here, we develop multi-module daily deterministic and probabilistic forecast models by the residual structure and finally establish a complete set of sub-seasonal high temperature forecasting system in the eastern part of China. The experimental results indicate that our method is effective and outperforms the European hindcast results in all aspects: absolute error, anomaly correlation coefficient, and other indicators are optimized by 8–50%, and the equitable threat score is improved by up to 400%. We conclude that the residual network has a sharper insight into the high temperature in sub-seasonal high temperature forecasting compared to traditional methods and convolutional networks, thus enabling more effective early warnings of extreme high temperature weather.

Hybrid systems using residual modeling for sea surface temperature forecasting

The 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.

Minimum temperature forecasting by stochastic techniques : An evidence of the heat island effect

This paper deals mainly with the forecasting of minimum temperatures (Tm) from an stochastic viewpoint. Some appropriate theoretical considerations lead to a choice of those variables significant connected to Tm. Modelling has been carried out for two nearby observatories, one in the centre of Madrid, the other one in the border (Barajas airport). The obtained models allow to show that the significant variables are the same for both locations, but performance in the peripheral area is of a rather inferior quality. It is shown, taking into account the characteristics of both places, that the difference between them can be allotted to the heat-island effect in the centre of Madrid.

Method of Air Temperature Forecast in Mechanized Longwall Workings in the Conditions of Vietnamese Mines

In the most recent years, the Vietnam National Coal - Mineral Industries Holding CorporationLimited (VINACOMIN) has been dynamically developing mechanization technologies in undergroundcoal mines. The climatic conditions of Vietnam, as well as increasing the depth of the coal seams and theproduction capacity, contribute to an air temperature increasing in mining excavations. The articlepresents statistical equations enabling air temperature forecasting at the outlet of mechanized longwallworkings. The results of numerical calculations, obtained from the solutions of the adopted mathematicaldescriptions, were compared with the measurement results and the statistical significance of the obtaineddeviations was determined. The performed analysis allowed to assess the practical usefulness of theadopted model for the air temperature forecasting in the workings of mechanized underground mines inVietnam. The presented method can be used as a tool for mining services in the fight against the climatethreat in underground excavations.

A New Hybrid Ensemble Deep Learning Model for Train Axle Temperature Short Term Forecasting

The axle temperature is an index factor of the train operating conditions. The axle temperature forecasting technology is very meaningful in condition monitoring and fault diagnosis to realize early warning and to prevent accidents. In this study, a data-driven hybrid approach consisting of three steps is utilized for the prediction of locomotive axle temperatures. In stage I, the Complementary empirical mode decomposition (CEEMD) method is applied for preprocessing of datasets. In stage II, the Bi-directional long short-term memory (BILSTM) will be conducted for the prediction of subseries. In stage III, the Particle swarm optimization and gravitational search algorithm (PSOGSA) can optimize and ensemble the weights of the objective function, and combine them to achieve the final forecasting. Each part of the combined structure contributes its functions to achieve better prediction accuracy than single models, the verification processes of which are conducted in the three measured datasets for forecasting experiments. The comparative experiments are chosen to test the performance of the proposed model. A sensitive analysis of the hybrid model is also conducted to test its robustness and stability. The results prove that the proposed model can obtain the best prediction results with fewer errors between the comparative models and effectively represent the changing trend in axle temperature.