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Investigation of Forecast Accuracy and its Impact on the Efficiency of Data-Driven Forecast-Based Reservoir Operating Rules

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WATER 2021年第19期13卷 2737-2737页

作者： Mostaghimzadeh, Ehsan Ashrafi, Seyed Mohammad Adib, Arash Geem, Zong Woo Shahid Chamran Univ Ahvaz Dept Civil Engn Fac Civil Engn & Architecture Ahvaz *** Iran Gachon Univ Coll IT Convergence Seongnam 13120 South Korea

Today, variable flow pattern, which uses static rule curves, is considered one of the challenges of reservoir operation. One way to overcome this problem is to develop forecast-based rule curves. However, managers must have an estimate of the influence of forecast accuracy on operation performance due to the intrinsic limitations of forecast models. This study attempts to develop a forecast model and investigate the effects of the corresponding accuracy on the operation performance of two conventional rule curves. To develop a forecast model, two methods according to autocorrelation and wrapper-based feature selection models are introduced to deal with the wavelet components of inflow. Finally, the operation performances of two polynomial and hedging rule curves are investigated using forecasted and actual inflows. The results of applying the model to the Dez reservoir in Iran visualized that a 4% improvement in the correlation coefficient of the coupled forecast model could reduce the relative deficit of the polynomial rule curve by 8.1%. Moreover, with 2% and 10% improvement in the Willmott and Nash-Sutcliffe indices, the same 8.1% reduction in the relative deficit can be expected. Similar results are observed for hedging rules where increasing forecast accuracy decreased the relative deficit by 15.5%. In general, it was concluded that hedging rule curves are more sensitive to forecast accuracy than polynomial rule curves are.

关键词： reservoir operation hedging policy forecast accuracy stacking algorithm ensemble learning support vector regression

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Minimizing vegetation influence on soil salinity mapping with novel bare soil pixels from multi-temporal images

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GEODERMA 2023年 439卷

作者： Wang, Danyang Yang, Haichao Qian, Hao Gao, Lulu Li, Cheng Xin, Jingda Tan, Yayi Wang, Yunqi Li, Zhaofu Nanjing Agr Univ Coll Resources & Environm Sci Nanjing 210095 Peoples R China Chinese Acad Sci Inst Soil Sci Nanjing 210008 Peoples R China

Optical remote sensing satellites provide rapid access to regional topsoil salinization mapping. However, mapping topsoil salinization based on spectral reflectance is always affected by background material like vegetation cover, straw mulching and soil types. In light of these challenges, this study investigates the potential of image fusion, where images of original and bare soil pixels were combined, to minimize the impact of vegetation cover on topsoil salinity mapping. A case study was presented for the typical vegetation cover area using synchronized Sentinel-2 MSI image (named original image) and 255 ground-truth data collected in October 2020, aligning with periods of vegetation cover and salt return. Furthermore, to obtain novel bare soil pixels, multi-temporal Sentinel-2 MSI images were acquired during two distinct intervals: March to May and September to November, spanning the years from 2018 to 2021. The synthetic soil image (SYSI) was obtained by extracting bare soil pixels from multi-temporal images. Two images (original, SYSI) were fused with non-negative matrix factorization (NMF) method, named SYSIfused. Then, the stacking machine algorithm was used for soil salinity mapping under different soil types, with evaluating the impact of SYSIfused on the accuracy of soil salinity prediction. The results showed the SYSIfused outperformed the original image (the R-2 of the best models increased by 0.054-0.242, RMSE and MAE decreased by 0.049-0.780 and 0.012-0.546, respectively). Based on the SYSIfused, the order of the effect of soil types was coastal bog solonchaks > alluvial soil > cinnamon soil > coral saline soil > overall samples, and their roles in improving the R-2 of the model were 0.141, 0.085, 0.022, 0.012, respectively. Besides, stacking models with the SYSIfused provided the best prediction performances (R-2 = 0.742, RMSE = 0.377, MAE = 0.362). This study introduces the concept of merging original images with SYSI, resulting in a significant imp

关键词： Soil salinity Bare soil Vegetation cover Fusion stacking algorithm

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TCA: Telecom Credit Assessment Assisted by Edge Intelligence

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2022年第11期31卷

作者： Hu, Hao Du, Xu Qiu, Feier Wang, Shiwei China Southern Power Grid Hainan Digital Grid Res Inst 32Haifu Rd Haikou 570204 Hainan Peoples R China Postal Savings Bank China Chengdu Software R&D Ctr Chengdu 610094 Peoples R China Zhongnan Univ Econ & Law Sch Informat & Safety Engn 82Nanhu Ave Wuhan 430073 Peoples R China

With China's economic transformation into a high-quality development stage, the importance of credit system construction has become increasingly prominent. The problems existing in the current telecom credit system include: (1) insufficient coverage of credit features;(2) traditional credit assessment models are difficult to reflect user credit status objectively, comprehensively and timely;(3) user demand for credit management and credit services are ignored. Due to these deficiencies, a new multi-level credit system is necessary to meet the rapid development of market economy. Telecom operators have large amount of precious data, with the advantages of large-scale, high-precision and data-diversity, which can provide new ideas for the construction of credit system. This work focuses on the current problems and conducts research as follows: design a Telecom Credit Assessment Model based on Boosting and stacking ensemble techniques, called TCAMBS, to improve the evaluation accuracy, and to select the best model according to the experimental results. On the one hand, this work can promote the innovation of telecom credit assessment models and provide new ideas for the construction of the credit system. On the other hand, this work will also help telecom operations to improve the quality of telecom credit services.

关键词： Telecom credit system credit assessment boosting algorithm stacking algorithm

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The Processing Procedure for the Interpretation of Microseismic Signal Acquired from a Surface Array During Hydraulic Fracturing in Pomerania Region in Poland

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Procedia Computer Science 2017年 108卷 1722-1730页

作者： Michał Antoszkiewicz Mateusz Kmieć Paweł Szewczuk Marek Szkodo Robert Jankowski Faculty of Mechanical Engineering Gdańsk University of Technology Gdańsk Poland Faculty of Civil and Environmental Engineering Gdańsk University of Technology Gdańsk Poland

Hydraulic fracturing is a procedure of injecting high pressure fluid into the wellbore in order to break shell rock and facilitate gas flow. It is a very costly procedure and, if not conducted properly, it may lead to environmental pollution. To avoid costs associated with pumping fluid outside the perspective (gas rich) zone and improve one’s knowledge about the reservoir rock, microseismic monitoring can be applied. The method involves recording seismic waves, which are induced by fractured rock, by an array of sensors distributed in a wellbore nearby or on the surface. Combining geological and geophysical knowledge of region with signal processing computer techniques, one can locate induced fractures allowing for real-time process monitoring and rock properties evaluation. In Poland perspective shell formation is located very deep, i.e. about 4km from the surface. Additionally overlaying rock formations strongly attenuate and disperse seismic waves. Therefore, signal recorded by a surface array of sensors is very weak. Signal from a seismic event can be orders of magnitude lower than noise. To recover signal connected with fractured rock one needs to use numerical methods utilizing coherence of signals. An example of such a computer procedure is presented in this paper.

关键词： microseismic monitoring surface array stacking algorithm

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A Multi-Clustering algorithm to Solve Driving Cycle Prediction Problems Based on Unbalanced Data Sets: A Chinese Case Study

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SENSORS 2020年第9期20卷 2448-2448页

作者： Wu, Yuewei Zhang, Wutong Zhang, Long Qiao, Yuanyuan Yang, Jie Cheng, Cheng Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China

Vehicle evaluation parameters, which are increasingly of concern for governments and consumers, quantify performance indicators, such as vehicle performance, emissions, and driving experience to help guide consumers in purchasing cars. While past approaches for driving cycle prediction have been proven effective and used in many countries, these algorithms are difficult to use in China with its complex traffic environment and increasingly high frequency of traffic jams. Meanwhile, we found that the vehicle dataset used by the driving cycle prediction problem is usually unbalanced in real cases, which means that there are more medium and high speed samples and very few samples at low and ultra-high speeds. If the ordinary clustering algorithm is directly applied to the unbalanced data, it will have a huge impact on the performance to build driving cycle maps, and the parameters of the map will deviate considerable from actual ones. In order to address these issues, this paper propose a novel driving cycle map algorithm framework based on an ensemble learning method named multi-clustering algorithm, to improve the performance of traditional clustering algorithms on unbalanced data sets. It is noteworthy that our model framework can be easily extended to other complicated structure areas due to its flexible modular design and parameter configuration. Finally, we tested our method based on actual traffic data generated in Fujian Province in China. The results prove the multi-clustering algorithm has excellent performance on our dataset.

关键词： unbalanced data driving cycle multi-clustering algorithm stacking algorithm

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A DISTRIBUTED SYSTEM FAULT DIAGNOSIS SYSTEM BASED ON MACHINE LEARNING

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第2期25卷 1117-1123页

作者： Wang, Yixiao Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan 430070 Peoples R China

Now days distributed system becomes the mainstream system of information storage and processing. Compared with traditional systems, distributed systems are larger and more ***, the average probability of failure is higher and the difficulty, complexity of operation and maintenance are greatly increased. Therefore, it is necessary to use efficient methods to diagnose the system. Our aim is to use the trained model to diagnose the fault data of the distributed system, so we can obtain as high diagnostic accuracy as possible, and create a web side for users to use. The technique we proposed uses the integrated learning approach of stacking to model the superposition of the raw data. To realize this, we trained with a dataset of 10,000 pieces of data and assessed accuracy every once in a while. Our best training results are about 80.69% accurate and can be used on the web side. By training data sets and analyzing distributed system faults with stacking technology, a model with a test accuracy of 80.69% was obtained. Through this model and the web platform we built, the fault of distributed system can be diagnosed, and the diagnosis results are better than other models.

关键词： distributed system Machine learning stacking algorithm Fault diagnosis Forecast Deep learning

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Historical Interstation Pattern Referencing (HIPR): An Application to PcP Waves Recorded in the Antarctic for ULVZ Imaging

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JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 2021年第10期126卷

作者： Hansen, Samantha E. Garnero, Edward J. Rost, Sebastian Univ Alabama Tuscaloosa AL 35401 USA Arizona State Univ Tempe AZ USA Univ Leeds Leeds W Yorkshire England

Much of our knowledge on deep Earth structure is based on detailed analyses of seismic waveforms that often have small amplitude arrivals on seismograms;therefore, stacking is essential to obtain reliable signals above the noise level. We present a new iterative stacking scheme that incorporates Historical Interstation Pattern Referencing (HIPR) to improve data quality assessment. HIPR involves comparing travel-time and data quality measurements between every station for every recorded event to establish historical patterns, which are then compared to individual measurements. Weights are determined based on the individual interstation measurement differences and their similarity to historical averages, and these weights are then used in our stacking algorithm. This approach not only refines the stacks made from high-quality data but also allows some lower-quality events that may have been dismissed with more traditional stacking approaches to contribute to our study. Our HIPR-based stacking routine is illustrated through an application to core-reflected PcP phases recorded by the Transantarctic Mountains Northern Network to investigate ultra-low velocity zones (ULVZs). We focus on ULVZ structure to the east of New Zealand because this region is well-sampled by our data set and also coincides with the boundary of the Pacific Large Low Shear Velocity Province (LLSVP), thereby allowing us to further assess possible ULVZ-LLSVP relationships. The HIPR-refined stacks display strong ULVZ evidence, and associated synthetic modeling suggests that the ULVZs in this region are likely associated with compositionally distinct material that has perhaps been swept by mantle convection currents to accumulate along the LLSVP boundary.

关键词： stacking algorithm ultra-low velocity zones PcP

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Data on prediction of geological characteristics during shield tunnelling in mixed soil and rock ground

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DATA IN BRIEF 2022年 45卷 108726页

作者： Yan, Tao Shantou Univ Coll Engn Dept Civil & Environm Engn MOE Key Lab Intelligent Mfg Technol Shantou 515063 Guangdong Peoples R China Royal Melbourne Inst Technol RMIT Sch Engn Discipline Civil & Infrastructure Engn Melbourne Vic 3001 Australia Shantou Univ Coll Engn Shantou 515063 Guangdong Peoples R China

This data presented in this article pertain to measured data obtained from earth pressure balance (EPB) shield tunelling of Guangzhou-Foshan intercity railway project. The measured data consists of geological characteristics and the main shield parameters in each lining ring during shield tunnelling. The distribution of raw data was displayed, and the geological characteristics via field record were compared to the pre-diction results of improved stacking method. The value of the database is consideration of the relationship between shield operational parameters and geological characteristics encountered in the shield tunnelling area, including forma-tions with soft soil, majority of soft soil, and majority of hard rock. The raw data was standardized and processed to low dimensional data by principal component analysis, which can be better used in geological characteristics classification. The presented data are applied to identify the geological char-acteristics in the article titled "Prediction of geological char-acteristics from shield operational parameters by integrating grid search and K-fold cross validation into stacking classifi-cation algorithm".(c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： Shield operational parameters Geological characteristics Identification and cluster stacking algorithm

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