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Urban ground subsidence monitoring and prediction using time-series InSAR and machine learning approaches: a case study of Tianjin, China

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ENVIRONMENTAL EARTH SCIENCES 2024年第16期83卷 473页

作者： Zhang, Jinlai Kou, Pinglang Tao, Yuxiang Jin, Zhao Huang, Yijian Cui, Jinhu Liang, Wenli Liu, Rui Chongqing Univ Posts & Telecommun Chongqing Engn Res Ctr Spatial Big Data Intelligen Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Tourism Multisource Data Percept & Decis Minist Culture & Tourism TMDPDMCT Chongqing 400065 Peoples R China Chinese Acad Sci Inst Earth Environm State Key Lab Loess & Quaternary Geol Xian 710061 Peoples R China Xi An Jiao Tong Univ Inst Global Environm Change Xian 710049 Peoples R China Chongqing Normal Univ Sch Geog & Tourism Chongqing Key Lab GIS Applicat Chongqing Peoples R China

Urban ground subsidence, a major geo-hazard threatening sustainable urban development, has been increasingly reported worldwide, yet comprehensive investigations integrating multi-temporal ground deformation monitoring and predictive modeling are still lacking. This study aims to characterize the spatial-temporal evolution of ground subsidence in Tianjin's Jinnan District from 2016 to 2023 using 193 Sentinel-1 A ascending images and the advanced Interferometric Synthetic Aperture Radar (InSAR) techniques of Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR). The maximum cumulative subsidence reached - 326.92 mm, with an average subsidence rate of -0.39 mm/year concentrated in industrial, commercial, and residential areas with high population density. Further analysis revealed that subway construction, human engineering activities, and rainfall were the primary drivers of ground subsidence in this region. Simultaneously, this study compared the predictive capabilities of five machine learning methods, including Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), Extremely Randomized Tree (ERT), and Long Short-Term Memory (lstm) neural network, for future ground subsidence. The lstm-based prediction model exhibited the highest accuracy, with a root mean square error of 2.11 mm. Subdomain predictions generally outperformed the overall prediction, highlighting the benefits of reducing spatial heterogeneity. These findings provide insights into the mechanisms and patterns of urban ground subsidence, facilitating sustainable urban planning and infrastructure development.

关键词： Urban subsidence Time-series InSAR Risk assessment Machine learning lstm algorithm Tianjin

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Two-layer optimal charging strategy for electric vehicles in old residential areas

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第6期31卷 p1-20页

作者： Xia, Mingchao Liao, Tonghe Chen, Qifang Beijing Jiaotong Univ Sch Elect Engn Beijing 100044 Peoples R China

Electric vehicle (EV) departure time is an important variable in current coordinated charging studies. The predicted value of EV departure time is more reliable than the user-set departure time. In this study, a long short-term memory prediction model is used to accurately predict the departure time, and a two-layer optimal charging strategy is proposed. Total user satisfaction is set as the objective function, with the constraint of a safe distribution network operation. The proposed strategy is tested using a set of real EV travel data in an old residential area, and its performance is comprehensively compared with two alternative charging strategies, namely, uncontrolled charging and two-layer optimal charging with a set departure time. The proposed strategy outperforms the rival strategies by improving total user satisfaction, while ensuring safe distribution network operation in the old residential areas.

关键词： electric vehicles lstm algorithm old residential area optimal charging strategy

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Development and application of machine learning-based prediction model for distillation column

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2021年第5期36卷 1970-1997页

作者： Kwon, Hyukwon Oh, Kwang Cheol Choi, Yeongryeol Chung, Yongchul G. Kim, Junghwan Korea Inst Ind Technol Green Mat & Proc R&D Grp 55 Jongga Ro Ulsan 44413 South Korea Pusan Natl Univ Sch Chem & Biomol Engn Busan South Korea Yonsei Univ Dept Chem & Biomol Engn Seoul South Korea

Distillation is an energy-consuming process in the chemical industry. Optimizing operating conditions can reduce the amount of energy consumed and improve the efficiency of chemical processes. Herein, we developed a machine learning-based prediction model for a distillation process and applied the developed model to process optimization. The energy consumed in the distillation process is mainly used to control the temperature of the distillation column. We developed a model that predicted temperature according to the following procedure: (1) data collection;(2) characteristic extraction from the collected data to reduce learning time;(3) min-max normalization to improve prediction performance;and (4) a case study conducted to select the artificial neural network algorithm, optimization method, and batch size, which are the most appropriate elements for predicting production stage temperature. The result of the case study revealed that the most appropriate model was observed with a root mean squared error of 0.0791 and a coefficient of determination of 0.924 when the long short-term memory algorithm, Adam optimization method, and batch size of 128 were applied. We calculated the amount of steam consumption required to consistently maintain the production stage temperature by utilizing the developed model. The calculation result indicated that the amount of steam consumption was expected to be reduced by approximately 14%, from an average flow rate of 2763-2374 kg/h. This study proposed a control method applying a machine learning-based prediction model in the distillation process and confirmed that operation energy could be reduced through efficient operation.

关键词： Adam optimization distillation process lstm algorithm machine learning prediction model

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An AI-Based Medical Chatbot Model for Infectious Disease Prediction

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IEEE ACCESS 2022年 10卷 128469-128483页

作者： Chakraborty, Sanjay Paul, Hrithik Ghatak, Sayani Pandey, Saroj Kumar Kumar, Ankit Singh, Kamred Udham Shah, Mohd Asif Techno Int Newtown Comp Sci & Engn Dept Kolkata 700156 India JIS Univ Comp Sci & Engn Dept Kolkata 700109 India GLA Univ Dept Comp Engn & Applicat Mathura 281406 India Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan Taiwan Kebri Dehar Univ Dept Econ Kebri Dehar P7R6 MRG Ethiopia

The purpose of this paper is to show concisely how we can promote chatbots in the medical sector and cure infectious diseases. We can create awareness through the users and the users can get proper medical solutions to prevent disease. We created a preliminary training model and a study report to improve human interaction in databases in 2021. Through natural language processing, we describe the human behaviors and characteristics of the chatbot. In this paper, we propose an AI Chatbot interaction and prediction model using a deep feedforward multilayer perceptron. Our analysis discovered a gap in knowledge about theoretical guidelines and practical recommendations for creating AI chatbots for lifestyle improvement programs. A brief comparison of our proposed model concerning the time complexity and accuracy of testing is also discussed in this paper. In our work, the loss is a minimum of 0.1232 and the highest accuracy is 94.32%. This study describes the functionalities and possible applications of medical chatbots and explores the accompanying challenges posed by the use of these emerging technologies during such health crises mainly posed by pandemics. We believe that our findings will help researchers get a better understanding of the layout and applications of these revolutionary technologies, which will be required for continuous improvement in medical chatbot functionality and will be useful in avoiding COVID-19.

关键词： Chatbots COVID-19 Artificial intelligence Medical services Machine learning Computer science Analytical models chatbot lstm algorithm machine learning natural language processing query processing

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Cracking risk analysis of face slabs in concrete face rockfill dams during the operation period

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STRUCTURES 2022年 40卷 621-632页

作者： Liu, Yongtao Zheng, Dongjian Cao, Enhua Wu, Xin Chen, Zhouyan Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Peoples R China Hohai Univ Natl Engn Res Ctr Water Resources Efficient Utili Nanjing 210098 Peoples R China

Due to changes in the environmental load and deformation of rockfill bodies, cracks likely form and develop in the face slab of concrete face rockfill dams (CFRDs), affecting the working behaviour of face slabs and the safe operation of dams. At present, the cracking risk in face slabs has not been studied. Based on field test data and the finite element method, this paper developed a new method of analysing the cracking risk of face slabs. Considering that the uncertainty of construction quality leads to the randomization of rockfill material param-eters, the probability function and coefficient of variation in the parameters of rockfill materials were determined based on field test data, and then, parameter samples were obtained by Latin hypercube sampling (LHS). The stress of the face slab was calculated by the Duncan-Chang E-B model and Burgers model with the parameter samples. To reduce the calculation workload of the finite element method based on traditional Monte Carlo samples, LHS and the long-and short-term memory (lstm) algorithm were applied to obtain the response surface function. The lstm algorithm was used to train the mapping relationship between the material pa-rameters of the samples and the stress of the face slab. Combining the threshold value of the concrete allowable strength with the response surface method, the implicit cracking limit state function of the face slab was approximated, the cracking risk of the face slab was calculated, and the cracking risk curve of the typical area of the concrete face slab with time was given. In this case, mean absolute error (MAE) is 0.006 and the root mean square error (RMSE) is 0.004. The error of this method is very small, meeting the accuracy requirement. The case showed that the results were consistent with the inspection of the face slab and that the method was feasible and effective.

关键词： Cracking risk of face slabs Dynamic response surface method Randomness of material parameters lstm algorithm CFRD

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Data-Driven Traffic Accident Analysis and Prediction Using Machine Learning Models: A Case Study of Philadelphia City 7

Data-Driven Traffic Accident Analysis and Prediction Using M...

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7th International Conference on Traffic Engineering and Transportation System (ICTETS)

作者： Lyu, Chengxuan Penn State Univ Sch Elect Engn & Comp Sci 201 Old Main University Pk PA 16802 USA

ISBN: (纸本)9781510674479

Traffic safety problem has been highly concerned by people all over the world. Predicting potential traffic accidents can help to improve traffic facilities or emergency system, and give people alerts to potential dangers. As a random event, the occurrence of traffic accidents has obvious seasonal and spatial characteristics. The accuracy of traffic accident prediction can be improved by combining the seasonal and spatial characteristics of traffic accidents. In the study, we selected Philadelphia car accident databases during 2008 to 2012 on Opendata Philly. On the basis of data grouping preprocessing, we tested three machine learning algorithms to predict the count of accidents. The results indicate that lstm(Long Short-Term Memory) algorithm has the best performance relatively, and XGBoost (eXtreme Gradient Boosting) model does not perform better than ARIMA(Autoregressive Integrated Moving Average model. In addition, we identify the important features of traffic accidents in Philadelphia city: the count of accidents on the same road segment, traffic control device, automobile type, roadway surface condition, weather condition. The datasets in this study has obvious spatial characteristics also, and the occurrence of accidents is closely related to the accident segment.

关键词： Traffic accidents Data analysis Machine learning ARIMA algorithm XGBoost algorithm lstm algorithm

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Application of lstm Neural Network for Urban Road Diseases Trend Forecasting

Application of LSTM Neural Network for Urban Road Diseases T...

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IEEE International Conference on Big Data (Big Data)

作者： Zhang, Linglan Meng, Weina Chen, Ailin Mei, Mei Liu, Ying USTC Sch Management Hefei 230026 Anhui Peoples R China UCAS Sch Econ & Management Beijing 100190 Peoples R China Stanford Univ Ctr Sustainable Dev & Global Competitiveness Stanford CA 94305 USA CAMS Inst Med Informat Med Lib Beijing 100005 Peoples R China PUMC Beijing 100005 Peoples R China

ISBN: (纸本)9781538650356

Road disease detection is an important aspect of road management. Data shows that nearly all regions have invested a lot of money and manpower in road management such as road disease detection and road repair. In order to economize on the use of funds, industry and academia have been exploring many new road exploration techniques. The gradual implementation of these technologies has accumulated a large amount of road disease data. This data give us the impression that road management can reduce the investment in road disease detection through reasonable prediction of road diseases. With this large amount of data, we try to use the Long Short Term Memory (lstm) algorithm in Neural Network as well as time series model to predict the amount of road diseases in the future. lstm is a special kind of cyclic neural network with long-term dependence on learning. It is able to store information for quite a long time and overcome the problem of gradients that disappear. By using the time series method and the lstm method on the training and test sets to test the existing data on 421 roads in Beijing, China, we find that the results of the lstm model are better than that of the time series method under most circumstances.

关键词： road disease prediction time series lstm algorithm

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Learning Inertial Measurement Error Compensation in GPS Signal Shielded Using lstm 22

Learning Inertial Measurement Error Compensation in GPS Sign...

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22nd IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Fall)

作者： Wu, Yu-Fan Huang, Guo-Shing Kao, Ming-Cheng Natl Chin Yi Univ Technol Inst Elect Engn Taichung Taiwan Hsiuping Univ Sci & Technol Dept Elect Engn Taichung Taiwan

ISBN: (纸本)9781665404037

GPS (Global Positioning System) is an indispensable technology in vehicle positioning and navigation. Now the GPS positioning technology is very mature and is developing towards high-precision and high-reliability technology. However, the stability of GPS needs to be improved This paper uses RTK (Real Time Kinematic) real-time dynamic differential positioning technology that can improve GPS accuracy, as well as basic simple inertial navigation components such as gyroscopes, accelerometers, and magnetic compasses as GPS. Compensation during interruption improves the reliability of GPS positioning. However, the error of the long-term inertial navigation system accumulates over time, which seriously affects the navigation accuracy, and the accuracy of the simple sensor output is not high. Therefore, this paper proposes a neural network-like learning scheme that uses lstm to achieve high-precision and reliable positioning. We use cars to collect the XY position coordinate data of the original vehicle around the urban area without difference and with difference positioning. Use MATLAB offline operation to calculate lambda (longitude), Phi (latitude) and use the data measured by integrated inertial elements to assist navigation in the road section that is shielded by GPS signals. And use lstm deep learning to correct its errors, and then compare with and without differential positioning methods to get a more optimized path map to achieve the compensation effect.

关键词： RTK GPS positioning navigation inertial components error compensation lstm algorithm

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Gesture Recognition in Smartwatches Using lstm for Interaction in Low-Cost Virtual Environments 26

Gesture Recognition in Smartwatches Using LSTM for Interacti...

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26th Symposium on Virtual and Augmented Reality (SVR)

作者： Silva, Leonardo Soares, Fabrizzio Felix, Juliana Cardoso, Luciana Aranha, Renan Vinicius Nascimento, Thamer Horbylon Inst Fed Goiano Goiania Go Brazil Univ Fed Goias Goiania Go Brazil Univ Fed Mato Grosso Cuiaba Brazil

ISBN: (纸本)9798400709791

This work presents a methodology to enhance interaction in VR environments using accessible devices such as Google Cardboard and smartwatches. The main contribution is the implementation of a recurrent neural network model of the lstm type, designed to recognize gestures captured by smartwatches. This enables more natural and fluid interaction with the virtual environment, significantly elevating the level of immersion and responsiveness perceived by users. Preliminary results demonstrate that the lstm model achieves robust performance in accurately identifying gestures, which is essential for providing an immersive and engaging experience. We hope that this approach expands the possibilities for interaction in virtual environments and represents a significant advancement in the field of wearable computing applied to Virtual Reality.

关键词： Gesture Recognition Smartwatches Virtual Reality lstm algorithm

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An integrated wind power prediction method based on heterogeneous clustering and DAlstm

An integrated wind power prediction method based on heteroge...

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IEEE-Power-and-Energy-Society General Meeting (PESGM)

作者： Wang, Shuo Kong, Xiangyu Wang, Ning Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin Peoples R China

ISBN: (纸本)9781665464413

Currently, the power grid is greatly impacted by wind power's intermittent and fluctuating problems. If wind power forecasting can be accurately implemented, it will be crucial for the power grid to develop coordinated power dispatch. It is found that the traditional methods have problems such as insufficient data mining and low accuracy of prediction algorithms. Therefore, an integrated wind power prediction method based on heterogeneous clustering and DAlstm is proposed. Firstly, an integrated clustering framework with multiple heterogeneous algorithms is constructed, and wind power sequences under different fluctuation states are obtained. Secondly, the lstm prediction model is established, and the feature space and temporal attention mechanism are introduced to dynamically mine the patterns of wind power sequences and input features. Finally, a wind farm in China is used as an example to demonstrate the validity and accuracy of the method.

关键词： Clustering Integration Dual-stage attention lstm algorithm Data -driven

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