Short-Term Regional Temperature Prediction Based on Deep Spatial and Temporal Networks

Wu, Shun; Fu, Fengchen; Wang, Lei; Yang, Minhang; Dong, Song; He, Yongqing; Zhang, Qingqing; Guo, Rong

doi:10.3390/atmos13121948

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…Therefore, the Henk de Kluijver traffic noise model is more effective in calcu-of IDW interpolation. Not like IDW, the correlation of observed points is considered in Kriging interpolation (Wu et al, 2022). The semi-variance of observed points is mapped in the 2D coordinate system, and the model is fitted for the semi-variance.…”

Section: Road Traffic Noise Visualisationmentioning

confidence: 99%

Performance Assessment of Spatial Interpolations for Traffic Noise Mapping on Undulating and Level Terrain

Wickramathilaka,

Ujang,

Azri

et al. 2024

Geodesy and Cartography

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Traffic noise mapping frequently employs Kriging, Inverse Distance Weighted (IDW), and Triangular Irregular Networks (TIN) spatial interpolations. This study uses the Henk de Kluijver noise model to evaluate the performance of spatial interpolations. Effective traffic noise mapping requires that noise observation points (Nops) be designed as 2 m grids. The upper and lower slopes function as noise barriers to reduce sound levels. Therefore, assessment of accuracy is essential for visualising noise levels in undulating and level terrain. In addition, this work gives an accurate comparison of traffic noise interpolation in undulating areas. The elements of spatial interpolations, such as the weighting factor, variogram, radius, and number of points influence the interpolation accuracy. The Kriging with a Gaussian variogram, where the radius is 5 m and the number of points is 12 demonstrates the highest level of precision. However, there is no direct relationship between accuracy validation and cross-validation. In cross-validation, however, the accuracy of the Gaussian variogram with a 7 m radius and 18 points is more accurate. In addition, this study demonstrates that Kriging is superior for extrapolating noise levels in undulating regions. Accurate visualising traffic noise levels requires a prior understanding of spatial interpolations.

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Section: Road Traffic Noise Visualisationmentioning

confidence: 99%

Performance Assessment of Spatial Interpolations for Traffic Noise Mapping on Undulating and Level Terrain

Wickramathilaka,

Ujang,

Azri

et al. 2024

Geodesy and Cartography

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“…The ST-Net model (deep spatial and temporal network) was used to predict the future 1 hour air temperature at Baihetan Hydropower Station, China. The experimental results showed that the R 2 , RMSE, and MAE of ST-Net were 0.98, 0.63, and 0.45, respectively (Wu et al, 2022). The ANN was trained by 90% of the monthly land surface air temperatures from ERA5 and validated with the remaining 10%.…”

Section: Projection Of Future Climate Change Between 2030 and 2040mentioning

confidence: 99%

Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models

Guo,

He,

Wang

2023

Front. For. Glob. Change

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IntroductionAtmospheric temperature affects the growth and development of plants and has an important impact on the sustainable development of forest ecological systems. Predicting atmospheric temperature is crucial for forest management planning.MethodsArtificial neural network (ANN) and deep learning models such as gate recurrent unit (GRU), long short-term memory (LSTM), convolutional neural network (CNN), CNN-GRU, and CNN-LSTM, were utilized to predict the change of monthly average and extreme atmospheric temperatures in Zhengzhou City. Average and extreme atmospheric temperature data from 1951 to 2022 were divided into training data sets (1951–2000) and prediction data sets (2001–2022), and 22 months of data were used as the model input to predict the average and extreme temperatures in the next month.Results and DiscussionThe number of neurons in the hidden layer was 14. Six different learning algorithms, along with 13 various learning functions, were trained and compared. The ANN model and deep learning models were evaluated in terms of correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), and good results were obtained. Bayesian regularization (trainbr) in the ANN model was the best performing algorithm in predicting average, minimum and maximum atmospheric temperatures compared to other algorithms in terms of R (0.9952, 0.9899, and 0.9721), and showed the lowest error values for RMSE (0.9432, 1.4034, and 2.0505), and MAE (0.7204, 1.0787, and 1.6224). The CNN-LSTM model showed the best performance. This CNN-LSTM method had good generalization ability and could be used to forecast average and extreme atmospheric temperature in other areas. Future climate changes were projected using the CNN-LSTM model. The average atmospheric temperature, minimum atmospheric temperature, and maximum atmospheric temperature in 2030 were predicted to be 17.23 °C, −5.06 °C, and 42.44 °C, whereas those in 2040 were predicted to be 17.36 °C, −3.74 °C, and 42.68 °C, respectively. These results suggest that the climate is projected to continue warming in the future.

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“…The numerical methods utilize physics and mathematics by formulating the equations to forecast [5]. They focus on many parameters and complex equation operations and need several engineering calculations to extract evolution trends [6]. In addition to their complexity, these methods demand great computational time and effort [7], and their accuracy is highly dependent on the spatial domain [6,8,9].…”

Section: Introductionmentioning

confidence: 99%

“…They focus on many parameters and complex equation operations and need several engineering calculations to extract evolution trends [6]. In addition to their complexity, these methods demand great computational time and effort [7], and their accuracy is highly dependent on the spatial domain [6,8,9]. The latest studies have increasingly applied data-driven approaches.…”

Section: Introductionmentioning

confidence: 99%

Time-Series Hourly Sea Surface Temperature Prediction Using Deep Neural Network Models

Farhangi

Sadeghi‐Niaraki

Bazargani

et al. 2023

JMSE

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Sea surface temperature (SST) is crucial in ocean research and marine activities. It makes predicting SST of paramount importance. While SST is highly affected by different oceanic, atmospheric, and climatic parameters, few papers have investigated time-series SST prediction based on multiple features. This paper utilized multi features of air pressure, water temperature, wind direction, and wind speed for time-series hourly SST prediction using deep neural networks of convolutional neural network (CNN), long short-term memory (LSTM), and CNN–LSTM. Models were trained and validated by different epochs, and feature importance was evaluated by the leave-one-feature-out method. Air pressure and water temperature were significantly more important than wind direction and wind speed. Accordingly, feature selection is an essential step for time-series SST prediction. Findings also revealed that all models performed well with low prediction errors, and increasing the epochs did not necessarily improve the modeling. While all models were similarly practical, CNN was considered the most suitable as its training speed was several times faster than the other two models. With all this, the low variance of time-series data helped models make accurate predictions, and the proposed method may have higher errors while working with more variant features.

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Short-Term Regional Temperature Prediction Based on Deep Spatial and Temporal Networks

Cited by 8 publications

References 19 publications

Performance Assessment of Spatial Interpolations for Traffic Noise Mapping on Undulating and Level Terrain

Performance Assessment of Spatial Interpolations for Traffic Noise Mapping on Undulating and Level Terrain

Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models

Time-Series Hourly Sea Surface Temperature Prediction Using Deep Neural Network Models

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