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An ANN-Based Short-Term temperature forecast Model for Mass Concrete Cooling Control

Tsinghua Science and Technology 2023年第3期28卷 511-524页

作者： Hide Author's Information Ming Li Peng Lin Daoxiang Chen Zichang Li Ke Liu Yaosheng Tan Department of Hydraulic Engineering Tsinghua UniversityBeijing 100084China Sichuan Energy Internet Research Institute Tsinghua UniversityChengdu 610213China China Three Gorges Construction Engineering Corporation Chengdu 610000China

Concrete temperature control during dam construction(e.g.,concrete placement and curing)is important for cracking prevention.In this study,a short-term temperature forecast model for mass concrete cooling control is developed using artificial neural networks(ANN).The development workflow for the forecast model consists of data integration,data preprocessing,model construction,and model application.More than 80000 monitoring samples are collected by the developed intelligent cooling control system in the Baihetan Arch Dam,which is the largest hydropower project in the world under construction.Machine learning algorithms,including ANN,support vector machines,long short-term memory networks,and decision tree structures,are compared in temperature prediction,and the ANN is determined to be the best for the forecast model.Furthermore,an ANN framework with two hidden layers is determined to forecast concrete temperature at intervals of one day.The root mean square error of the forecast precision is 0.15∘C on average.The application on concrete blocks verifies that the developed ANN-based forecast model can be used for intelligent cooling control during mass concrete construction.

关键词： artificial neural networks(ANN) predictive modeling temperature forecast mass concrete cooling control

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ConvLSTM Based temperature forecast Modification Model for North China

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Journal of Tropical Meteorology 2022年第4期28卷 405-412页

作者：耿焕同胡中岩王天雷 School of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044 China Jiangsu Open University Nanjing 210036 China

The correction of model forecast is an important step in evaluating weather forecast results.In recent years,post-processing models based on deep learning have become prominent.In this paper,a deep learning model named EDConvLSTM based on encoder-decoder structure and ConvLSTM is developed,which appears to be able to effectively correct numerical weather forecasts.Compared with traditional post-processing methods and convolutional neural networks,ED-ConvLSTM has strong collaborative extraction ability to effectively extract the temporal and spatial features of numerical weather forecasts and fit the complex nonlinear relationship between forecast field and observation field.In this paper,the post-processing method of ED-ConvLSTM for 2 m temperature prediction is tested using The International Grand Global Ensemble dataset and ERA5-Land data from the European Centre for Medium-Range Weather forecasts(ECMWF).Root mean square error and temperature prediction accuracy are used as evaluation indexes to compare ED-ConvLSTM with the method of model output statistics,convolutional neural network postprocessing methods,and the original prediction by the ECMWF.The results show that the correction effect of EDConvLSTM is better than that of the other two postprocessing methods in terms of the two indexes,especially in the long forecast time.

关键词： temperature forecast post-processing numerical weather prediction encoder-decoder model ConvLSTM

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IMPACT OF SUMMER WARMING ON DYNAMICS-STATISTICS-COMBINED METHOD TO PREDICT THE SUMMER temperature IN CHINA

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Journal of Tropical Meteorology 2017年第4期23卷 440-449页

作者：苏海晶乔少博杨杰王晓娟 College of Atmospheric Sciences Lanzhou UniversityLanzhou 730000 China College of Atmospheric Sciences Nanjing University of Information Science and Technology Nanjing 210044 China Climate Center of Jiangsu Provincial Meteorologica Bureau Nanjing 210000 China College of Physics and Electronic Project Changshu Institute of TechnologyChangshuJiangsu 215500 China

Based on NCEP/NCAR daily reanalysis data, climate trend rate and other methods are used to quantitatively analyze the change trend of China's summer observed temperature in 1983—2012. Moreover, a dynamics-statistics-combined seasonal forecast method with optimal multi-factor portfolio is applied to analyze the impact of this trend on summer temperature forecast. The results show that: in the three decades, the summer temperature shows a clear upward trend under the condition of global warming, especially over South China, East China, Northeast China and Xinjiang Region, and the trend rate of national average summer temperature was 0.27℃ per decade. However, it is found that the current business model forecast(Coupled Global Climate Model) of National Climate Centre is unable to forecast summer warming trends in China, so that the post-processing forecast effect of dynamics-statistics-combined method is relatively poor. In this study, observed temperatures are processed first by removing linear fitting trend, and then adding it after forecast to offset the deficiency of model forecast indirectly. After test, ACC average value in the latest decade was 0.44 through dynamics-statistics-combined independent sample return forecast. The temporal correlation(TCC) between forecast and observed temperature was significantly improved compared with direct forecast results in most regions, and effectively improved the skill of the dynamics-statistics-combined forecast method in seasonal temperature forecast.

关键词： dynamics-statistics-combined global warming temperature forecast model error correction

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