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Landslide susceptibility modelling based on AHC-OLID clustering algorithm

ADVANCES IN SPACE RESEARCH 2021年第1期68卷 301-316页

作者： Mao, Yimin Mwakapesa, Deborah S. Wang, Genglong Nanehkaran, Y. A. Zhang, Maosheng Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Jiangxi Peoples R China MLR Key Lab Geohazards Loess Area 438 E Youyi Rd Xian 710054 Shaanxi Peoples R China

The aim of this study is to design a novel machine learning model named Agglomerative Hierarchical clustering algorithm based on Overlapped Interval Divergence distance measure (AHC-OLID), for modelling and assessing landslide susceptibility. The AHC-OLID algorithm is proposed to combat the limitations of many clustering algorithms in modelling and assessing landslide susceptibility, includ-ing: pre-defining the number of clusters;sensitivity to the clusters properties and noisy data;as well as difficulty in processing rainfall data. The proposed algorithm addresses these issues by integrating the traditional Agglomerative Hierarchical clustering (AHC) and Overlapped Interval Divergence distance measure (OLID) methods into landslide susceptibility assessment to enhance its performance. It considers the clusters sizes as well the distances between them and tends to avoid taking into consideration small clusters which are very far from other clusters in the dataset. It is also insensitive to the variation in sizes, variances and shapes of the clusters, which makes the algorithm more advantageous. Besides, the AHC-OLID algorithm makes use of the OLID distance function to process the rainfall data, which takes two factors into consideration: distance between their centers as well as relative size of their overlapped area. Applying this new approach in Baota District, China, produced significant improvement in assessment of landslide susceptibility than previous models. Moreover, the Landslide susceptibility map constructed based on AHC-OLID algorithm can be a useful tool for landslide con-trolling strategies for proper land use and planning. (C) 2021 COSPAR. Published by Elsevier B.V. All rights reserved.

关键词： Landslide Landslide susceptibility Uncertain data clustering algorithm

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Distributed Soft clustering algorithm for IoT Based on Finite Time Average Consensus

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IEEE INTERNET OF THINGS JOURNAL 2021年第21期8卷 16096-16107页

作者： Yu, Hao Chen, Hui Zhao, Shengjie Shi, Qingjiang Tongji Univ Sch Software Engn Shanghai 201804 Peoples R China Tongji Univ Sch Math Sci Shanghai 200092 Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China

clustering is a common technique for statistical data analysis and it has been widely used in many fields. This article investigates data clustering over the Internet-of-Things (IoT) network. Facing the IoT network challenges, including data volume, communication latency, and information security, we here propose a distributed soft clustering algorithm for the IoT environments where each IoT node may have data from multiple clusters. Considering that the main task of soft clustering is to compute each cluster center in a weighted averaging fashion, our distributed clustering method resorts to an efficient finite-time average-consensus algorithm. Moreover, to make the distributed clustering algorithm more stable and be able to escape from some bad local optimum, we propose a distributed deterministic initialization method based on data variance partitioning. Experiments show that the proposed distributed soft clustering algorithm can offer the same performance as its centralized counterpart in terms of both convergence and clustering quality. Besides, unlike most clustering methods relying on probabilistic initialization, our algorithm could provide stable clustering quality which makes it more suitable for IoT networks. A real-world case study about the clustering analysis for distributed data sets collected by environmental monitoring stations is offered, which shows the potential of our algorithms in practical applications.

关键词： Average consensus clustering algorithm distributed algorithm fuzzy C-means Internet of Things (IoT) K-means

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EMASS: A Novel Energy, Safety and Mobility Aware-Based clustering algorithm for FANETs

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IEEE ACCESS 2021年 9卷 105506-105520页

作者： Aissa, Mohamed Abdelhafidh, Maroua Ben Mnaouer, Adel Univ Nizwa Nizwa 616 Oman Univ Sfax Digital Res Ctr Sfax SM RTS Lab Signals SystMs Sfax 3029 Tunisia Canadian Univ Dubai Dept Comp Engn & Computat Sci Dubai U Arab Emirates

The Unmanned Aerial Vehicles (UAVs), organized as a Flying Ad-hoc NETwork (FANET), are used to make effective remote monitoring in diverse applications. Due to their high mobility, their energy consumption is increasingly affected leading to reduced network stability and communication efficiency. The design of node clustering of a FANET needs to consider the number of UAVs in the vicinity (transmission range) in order to ensure an adaptive reliable routing. Novel clustering schemes have been employed to deal with the highly dynamic flying behavior of UAVs and to maintain network stability. In this context, a new clustering algorithm is proposed to address the fast mobility of UAVs and provide safe inter-UAV distance, stable communication and extended network lifetime. The main contributions of this paper are first to extend and improve important metrics used in two well-known algorithms in the literature namely: The Bio-Inspired clustering Scheme for FANETs (BICSF) and the Energy Aware Link-based clustering (EALC). Then, exploiting the improved metrics, an Energy and Mobility-aware Stable and Safe clustering (EMASS) algorithm, built upon new schemes useful for ensuring stability and safety in FANETs, is proposed. The simulation results showed that the EMASS algorithm outperformed the BICSF and the EALC algorithms in terms of better cluster stability, guaranteed safety, higher packet deliverability, improved energy saving and lower delays.

关键词： clustering algorithms Ad hoc networks Routing Reliability Routing protocols Stability criteria Network topology Unmanned aerial vehicle (UAV) clustering algorithm energy consumption stability mobility safe-inter-UAV distance Flying Ad-hoc NETwork (FANET)

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A geographical division clustering algorithm for multiple flying base stations

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INTERNET TECHNOLOGY LETTERS 2021年第4期4卷

作者： Lee, Jongyul Lyu, Xiao Friderikos, Vasilis Kings Coll London Dept Engn Ctr Telecommun Res CTR London WC2R 2LS England

Unmanned Aerial Vehicles (UAVs) can play a significant role as flying base station (FBSs) in assisting terrestrial base stations (BSs) to increase overall network capacity by providing localized transmission to a set of users. In that respect, FBSs can be deployed from a terrestrial macro BS which can act as the depot. In this letter, we propose two flavors of a Geographical Division (GD) clustering algorithm to assign FBSs located at the same terrestrial BS to a set of end users. Numerical investigations demonstrate that the proposed algorithm outperforms the two most widely used and best known clustering algorithms for this specific problem, namelyK-means and Hierarchical clustering algorithms.

关键词： 5G clustering algorithm flying base stations UAV unmanned aerial vehicles wireless networks

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Surface-wave dispersion curves extraction method from ambient noise based on U-net plus plus and density clustering algorithm

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JOURNAL OF APPLIED GEOPHYSICS 2023年第1期213卷

作者： Hu, Wei Zhang, Hao Sang, Wenjing Anna, Sowizdzal Yuan, Shichuan Yuan, Sanyi China Univ Petr State Key Lab Petr Resources & Prospecting Beijing Peoples R China Chinese Acad Geol Sci Inst Geomech Beijing Peoples R China AGH Univ Sci & Technol Krakow Poland Southern Univ Sci & Technol Dept Earth & Space Sci Shenzhen Peoples R China

It is crucial to accurately and rapidly extract dispersion curves of surface waves from ambient noise recordings for inverting the subsurface shear (S) wave velocity structures. Conventional manual picking methods are labor-intensive and affected by subjective factors related to seismologists. The existing deep learning methods can automatically and efficiently extract dispersion curves from dispersion spectrograms with a single branch. However, extracting dispersion curves from phase-velocity dispersion spectrograms with multiple branches re-mains challenging due to complex label-making processes and extensive label-preparation times. Notably, phase velocities typically display a positive correlation with periods and are slightly higher than group velocities. Given the significance of this empirical relationship, we present an intelligent surface-wave dispersion curves extraction method based on U-net++ and density clustering algorithm. Initially, guided by domain knowledge that dispersion curves are smooth, a global searching method is employed to automatically label group-velocity dispersion curves from group-velocity dispersion spectrograms. Subsequently, the group-velocity dispersion curves undergo transformation into probability images of the curves using a Gaussian function. The U-net++ then nonlinearly converts group-velocity dispersion spectrograms into probability images of group-velocity dispersion curves within a high-dimensional space. Following this, the Density-Based Spatial clustering of Ap-plications with Noise (DBSCAN) algorithm is applied to obtain multi-mode phase-velocity dispersion curves from phase-velocity dispersion spectrograms. We then remove invalid dispersion values in the multi-mode phase-velocity dispersion curves based on the empirical relationship between phase and group velocities, ultimately obtaining the phase-velocity dispersion curve corresponding to the group-velocity dispersion curve. Our pro-posed method has been tested using

关键词： Surface waves Dispersion curve extraction Convolutional neural network clustering algorithm Ambient noise tomography

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An Ensemble Model for PM2.5 Concentration Prediction Based on Feature Selection and Two-Layer clustering algorithm

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ATMOSPHERE 2023年第10期14卷 1482-1482页

作者： Wu, Xiaoxuan Wen, Qiang Zhu, Jun Hefei Univ Sch Artificial Intelligence & Big Data Hefei 230601 Peoples R China Anhui Jianzhu Univ Key Lab Intelligent Bldg & Bldg Energy Efficiency Hefei 230601 Peoples R China

Determining accurate PM2.5 pollution concentrations and understanding their dynamic patterns are crucial for scientifically informed air pollution control strategies. Traditional reliance on linear correlation coefficients for ascertaining PM2.5-related factors only uncovers superficial relationships. Moreover, the invariance of conventional prediction models restricts their accuracy. To enhance the precision of PM2.5 concentration prediction, this study introduces a novel integrated model that leverages feature selection and a clustering algorithm. Comprising three components-feature selection, clustering, and integrated prediction-the model first employs the non-dominated sorting genetic algorithm (NSGA-III) to identify the most impactful features affecting PM2.5 concentration within air pollutants and meteorological factors. This step offers more valuable feature data for subsequent modules. The model then adopts a two-layer clustering method (SOM+K-means) to analyze the multifaceted irregularity within the dataset. Finally, the model establishes the Extreme Learning Machine (ELM) weak learner for each classification, integrating multiple weak learners using the AdaBoost algorithm to obtain a comprehensive prediction model. Through feature correlation enhancement, data irregularity exploration, and model adaptability improvement, the proposed model significantly enhances the overall prediction performance. Data sourced from 12 Beijing-based monitoring sites in 2016 were utilized for an empirical study, and the model's results were compared with five other predictive models. The outcomes demonstrate that the proposed model significantly heightens prediction accuracy, offering useful insights and potential for broadened application to multifactor correlation concentration prediction methodologies for other pollutants.

关键词： PM2.5 concentration feature selection clustering algorithm Adaboost integration model

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Fast clustering algorithm of commodity association big data sparse network

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2021年第4期12卷 667-674页

作者： Pan, Hailan Yang, Xiaohuan Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China Shanghai Polytech Univ Res Ctr Resource Recycling Sci & Engn Shanghai Peoples R China Shanghai Polytech Univ Sch Econ & Management Shanghai Peoples R China Cosco Shipping Technol CO Ltd Shanghai Peoples R China

How to dig out the business perspectives and market rules behind commodity transaction data, explore the relationship between commodities, so as to more scientifically and rationally classify and promote commodity categories and improve commodity sales performance for e-commerce companies has become a recent research hotspot. To this end, this paper proposes to use clustering algorithm to explore the hidden laws of commodity-related big data. This article first consults a large amount of information through the literature survey method, systematically summarizes the relevant theoretical knowledge of the association rule method and clustering algorithm and gives a detailed introduction to its application in the commodity association big data mining. The research in this area has laid a sufficient theoretical foundation;after that, the Apriori algorithm in the association rules and the K-means algorithm in the clustering algorithm were used to carry out the fast clustering algorithm experiment of the commodity-related big data sparse network and the commodity transaction data was introduced in detail. The process of association analysis and cluster analysis;then taking China's well-known e-commerce platform Jingdong Mall as an example, by investigating the commodity transaction records of Jingdong Mall in the 4th week of July, the association and cluster analysis of its commodity transaction data were found. Among them, mobile phones and Bluetooth earphone, laptops and Bluetooth earphone, laptops and hard disks have the highest correlation and their confidence thresholds have reached 25%, 35 and 40% respectively. Finally, when the clustering results were tested, they were also found in the store. Strengthening the push and shopping guide of highly relevant product combinations on the website pages will increase the sales of products.

关键词： clustering algorithm Association rules Sparse network Data mining Electronic commerce

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A Multi-Frame Superposition Detection Method for Dim-Weak Point Targets Based on Optimized clustering algorithm

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REMOTE SENSING 2023年第8期15卷 1991-1991页

作者： Xu, Chenyu Zhao, Erwei Zheng, Wei Yang, Zhen Peng, Xiaodong Niu, Wenlong Chinese Acad Sci Natl Space Sci Ctr Key Lab Elect & Informat Technol Space Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100049 Peoples R China

Long-range surveillance and early warning of space targets are significant factors in space security. Under remote observation conditions, the energy performance of the target is weak and subject to environmental and imaging process contamination. Most detection methods are aimed at targets with a high signal-to-noise ratio (SNR) or local contrast, and the detection performance for dim-weak small targets is poor;therefore, the target signal is often enhanced by energy accumulation. However, owing to the interference caused by the instability of the imaging system, energy accumulation errors occur in the target, resulting in the dispersion of the target energy, making detection a challenge. To solve the above problem, this study proposed a multi-frame superposition detection method for dim-weak point targets based on an optimized clustering algorithm by combining the clustering method with the inherent features of the target and using the difference between the target and noise energy distribution for detection. First, we simulated the multi-frame imaging process of the target post-disturbance and established an optical imaging system model of the dim-weak target. Subsequently, we used data dimension reduction and outlier removal to extract the target potential area. Finally, the data were sent to the clustering model for calculation and judgment. Given that the accuracy rate reaches 87.1% when the SNR is 1 dB, the experimental results show that the detection method proposed in this paper can effectively detect dim-weak targets with low SNR. In addition, there is a significant improvement in the detection performance of the receiver characteristic curve compared with other algorithms in the real scene, which further proves the superiority of the method in this paper.

关键词： dim-weak target detection multi-frame superposition clustering algorithm outlier removal low SNR

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A novel spatio-temporal clustering algorithm with applications on COVID-19 data from the United States

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2023年第1期188卷

作者： Deb, Soudeep Karmakar, Sayar Indian Inst Management Bangalore Decis Sci Area Bannerghatta Main Rd Bengaluru 560076 Karnataka India Univ Florida Dept Stat 230 Newell Dr Gainesville FL 32611 USA

A new clustering algorithm for spatio-temporal data is developed. The proposed method leverages a weighted combination of a spatial haversine distance matrix and a spectral density based temporal distance matrix between the locations. Concepts of partition around medoids algorithm and the gap statistic are utilized to develop the algorithm and to determine the optimal number of clusters. Such a non-parametric algorithm is novel as it incorporates both spatial and temporal distances of the units and it can work for time-series of possibly different lengths. Theoretical guarantee of consistency of the proposed method is provided. An elaborate simulation study is also given to demonstrate the efficacy of the algorithm. As an interesting real life application, the proposed algorithm is implemented to analyze the spatio-temporal dynamics of the time series of coronavirus (COVID-19) incidence rates observed at county-level in the United States of America. The results are demonstrated on datasets of different sizes: the entire country, the Midwest region and the state of California. Special emphasis is given on the last two cases to display how the clustering results offer interesting insights into the epidemic progression in these areas. Particularly, it sheds light on whether state-mandated restrictions impacted the entire state similarly or if there are interesting local behaviors in terms of the COVID-19 spread. & COPY;2023 Elsevier B.V. All rights reserved.

关键词： clustering algorithm Coronavirus Gap statistic PAM Spatio-temporal Spectral density

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Urban flood risk assessment based on DBSCAN and K-means clustering algorithm

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GEOMATICS NATURAL HAZARDS & RISK 2023年第1期14卷

作者： Li, Jianwei Zheng, Anna Guo, Wei Bandyopadhyay, Nairwita Zhang, Yanji Wang, Qianfeng Fuzhou Univ Coll Phys & Informat Engn Fuzhou Peoples R China Fujian Meteorol Bur Meteorol Serv Ctr Fuzhou Peoples R China Univ Kalyani Haringhata Mahavidyalaya Kalyani India Fuzhou Univ Coll Environm & Safety Engn Fuzhou Peoples R China

Urban flood risk assessment plays a crucial role in disaster risk reduction and preparedness. It is essential to mitigate flood risks and establish a comprehensive analysis of factors influencing flood risk, as well as classify risk levels, in order to provide a clear model for risk assessment. This article aims to propose an efficient assessment method that can classify urban flood risk levels and assist cities in flood risk management, particularly in identifying high-risk areas. The study area chosen for this method is the municipal district of Fuzhou City, located in Fujian Province, China. The proposed method utilizes the Urban Flood Risk Assessment Index, which is developed based on the risk-vulnerability framework. It integrates the combinatorial empowerment method, Density-Based Spatial clustering of Applications with Noise (DBSCAN), and the K-means algorithm to cluster the quantitative risk factors, enabling a comprehensive analysis of the risk results. The findings demonstrate that areas characterized by intense extreme rainfall, lower elevation, gradual slope, high runoff coefficient, and high population density tend to exhibit higher flood risk. Moreover, the dominant factors contributing to high risk in different regions vary. The results obtained from this method align well with the distribution of historical flood points, indicating the effectiveness of the risk map prepared using this approach. In comparison to the results obtained from the single clustering method and the TOPSIS method used in traditional risk assessment, the proposed method can successfully identify high-risk urban flood areas. Consequently, this method offers a valuable scientific basis for urban flood prevention and control planning.

关键词： Urban flood clustering algorithm regional evaluation risk mapping risk management

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