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Land subsidence hazard assessment based on novel hybrid approach: BWM, weighted overlay index (WOI), and support vector machine (SVM)

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NATURAL HAZARDS 2023年第3期115卷 1997-2030页

作者： Mehrnoor, Shabnam Robati, Maryam Zarkesh, Mir Masoud Kheirkhah Farsad, Forough Baikpour, Shahram Islamic Azad Univ Fac Nat Resources & Environm Dept Environm Sci Sci & Res Branch Tehran Iran Islamic Azad Univ Soil Conservat & Watershed Management Res Inst Sci & Res Branch Agr Res Educ & Extens Org Tehran Iran Islamic Azad Univ Med Geol Res Ctr Sci & Res Branch Tehran Iran

Land subsidence is a morphological phenomenon, which causes negative environmental and economic consequences for human societies. Therefore, identifying the areas prone to subsidence can be one of the necessary measures for reducing the potential risks. This study aims to evaluate the efficiency of the support vector machine (SVM) algorithm and weighted overlay index (WOI) models in zoning the rate of land subsidence hazard in Hashtgerd plain, Iran. First, the 19 criteria include groundwater depletion, groundwater extraction, aquifer thickness, alluvium thickness, aquifer recharge, well density, drainage density, groundwater depth, lithology, bedrock depth, average annual precipitation, average annual temperature, climate type, agricultural use, urban use, industrial use, distance from rivers and streams, distance from roads, distance from faults were considered. Then, the layers were weighed based on the Best-Worst Method (BWM). The results of BWM indicated that the factors of groundwater extraction (0.219), lithology (0.157), and groundwater depletion (0.079) have a greater effect on the potential for subsidence hazard. Moreover, the results of validation by performing ROC curve showed that the accuracy of RBF-SVM, LN-SVM, SIG-SVM, PL-SVM, and WOI were 95.7, 94.3, 94.9, 93.2, and 90%, respectively. Based on the ROC results, all of the models for preparing the subsidence hazard map in Hashtgerd plain exhibit excellent accuracy. Therefore, all of the models used here can predict the areas vulnerable to subsidence properly. In this study, the five land subsidence hazard maps were used as new input factors and integrated using fuzzy gamma-ensemble methods to make better hazard maps. The results of the ensemble model indicated that 19.3% of Hashtgerd plain is in the zone of high to very high sensitivity. The results of this study can help planners in managing and reducing the possible hazards of subsidence.

关键词： Land subsidence support vector machine algorithm Weighted overlay index Best-worst method Hashtgerd plain

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Detecting the penetration of malicious behavior in big data using hybrid algorithms

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第SUPPL 1期18卷 919-933页

作者： Wang, Yue Shi, Yan HeBei Chem & Pharmaceut Coll Shijiazhuang 050026 Hebei Peoples R China

Information security must be maintained because the amount of data in the world today is growing exponentially. The issues related to security are growing as big data usage increases. Finding ways to identify intrusions into networks and information systems is one of the major issues in this subject. It is imperative and important to enhance intrusion detection skills in order to address malevolent behavior in large data. This paper presents a scalable approach to harmful data detection. Three variables have been considered in this strategy and model: scalability, user review, and temporal progress. High volumes of data can be processed using this technology. Time is split into time periods for data training in this system, and each time interval uses users' review information to train the data. Large volumes of data require sophisticated strategies to handle, and scalability in storage allows for faster processing and fewer computations. This approach is a kind of hardware-software hybrid solution for malware detection. A fresh approach to feature extraction has also been applied. In the suggested method, the bacteria algorithm in conjunction with the immune system algorithm has been utilized for the prediction operation, and the modified support vector machine algorithm and optical density have been utilized for classification. Based on the findings, the suggested combination algorithm outperforms other comparable techniques with a 21% detection rate, a 62% false alarm rate, a 15% accuracy rate, and a 73% training duration.

关键词： Intrusion detection Malicious behavior support vector machine algorithm Big data Bacterial system algorithm

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Landslide hazard zonation using advanced machine learning algorithms based on statistical models (Case study: Sarvabad, Iran)

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EARTH SCIENCE INFORMATICS 2025年第2期18卷 1-19页

作者： Rastkhadiv, Himan Motamedvaziri, Baharak Javadi, Seyed Akbar Ahmadi, Hassan Islamic Azad Univ Dept Nat Engn Sci & Res Branch Tehran Iran Univ Tehran Dept Reclamat Arid & Mt Reg Karaj Iran

Effectively reducing damages and making decisions on land development policies can be achieved identifying areas that are at high risk. Thus, the aim of the current research is to explore the risk of landslides zonation in Sarvabad by use of advanced machine learning algorithms based on statistical models. At the present research, landslide hazard zonation was conducted by use of hybrid algorithms, including Frequency Ratio-Random Forest (FR-RF), Frequency Ratio-support vector machine (FR-SVM), Weight of Evidence-Random Forest (WoE-RF), and Weight of Evidence-support vector machine (Woe-SVM). First, the point shape files of 166 landslides in Sarvabad, prepared by the Natural Resource Departmens of Kurdistan State, considered as the inventory of landslides map. The data from the landslide points was split into training sets comprising 70% and validation sets comprising 30%. To achieve to conduct the landslides hazard zone, a sum of 16 items were used that are as follows: slope, aspect, elevation, distance to the stream, distance to the road, river density, distance to the fault, fault density, roadway density, precipitation, land utilization, Normalized Difference Vegetation Index (NDVI), lithology, earthquake, Stream Power Index (SPI), and topographic wetness index (TWI). Finally, the performance of the models was evaluated using the ROC curve. Among the FR-RF, WoE-RF, FR-SVM, and WoE-SVM models, the FR-RF model accounted for the most high performance. In the end, it can be concluded that obtaining an accurate and reasonable spatial prediction map can help managers and urban planners identify zones susceptible to landslide occurrence so that they can manage the potential crises of landslide-prone zones.

关键词： Landslide Random forest algorithm support vector machine algorithm Sarvabad

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Forest Fire Risk Assessment and Mapping Using Remote Sensing and GIS Techniques: A Case Study in Nghe An Province, Vietnam

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IZVESTIYA ATMOSPHERIC AND OCEANIC PHYSICS 2024年第9期60卷 982-991页

作者： Doan, Th. N. Ph. Trinh, L. H. Zablotskii, V. R. Nguyen, V. T. Tran, X. T. Pham, Th. Th. H. Le, Th. Th. H. Le, V. Ph. Hanoi Univ Min & Geol Geomat Earth Sci Res Grp Hanoi Vietnam Le Quy Don Tech Univ Hanoi Vietnam Moscow State Univ Geodesy & Cartog Moscow 105064 Russia

This paper presents the results of modeling the risk of forest fires in the west of Nghe An Province (north-central Vietnam) using remote sensing and GIS data. We built models for the occurrence of forest fires using machine learning methods, including Random Forest (RF), Suppor vector machine (SVM), and Classification and Regression Trees (CART). The models took into account nine factors influencing the risk of forest fires, including vegetation cover (the normalized difference vegetation index (NDVI)), surface evapotranspiration, elevation, slope, aspect, wind speed, ground surface temperature, average monthly precipitation, and population density. Various parameters are tested in the RF, SVM, and CART algorithms to select the algorithm with the highest accuracy in forest fire risk prediction. The results show that the RF algorithm with the value of the "numberOfTrees" parameter equal to 100 has the highest accuracy in predicting the risk of forest fires in the study area.

关键词： forest fire risk remote sensing GIS support vector machine algorithm Nghe An province

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Identification of key brain networks and functional connectivities of successful aging:A surface-based resting-state functional magnetic resonance study

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World Journal of Psychiatry 2025年第3期15卷 216-226页

作者： Jiao-Jiao Sun Li Zhang Ru-Hong Sun Xue-Zheng Gao Chun-Xia Fang Zhen-He Zhou Department of Psychiatry The Affiliated Mental Health Center of Jiangnan UniversityWuxi 214151Jiangsu ProvinceChina Department of Psychiatry Yangzhou Wutaishan Hospital of Jiangsu ProvinceTeaching Hospital of Yangzhou UniversityYangzhou 225000Jiangsu ProvinceChina Department of Psychiatry Huai’an Third People’s HospitalHuai’an 223300Jiangsu ProvinceChina

BACKGROUND Successful aging(SA)refers to the ability to maintain high levels of physical,cognitive,psychological,and social engagement in old age,with high cognitive function being the key to achieving *** To explore the potential characteristics of the brain network and functional connectivity(FC)of *** Twenty-six SA individuals and 47 usual aging individuals were recruited from community-dwelling elderly,which were taken the magnetic resonance imaging scan and the global cognitive function assessment by Mini Mental State Examination(MMSE).The resting state-functional magnetic resonance imaging data were preprocessed by DPABISurf,and the brain functional network was conducted by *** support vector machine model was constructed with altered functional connectivities to evaluate the identification value of *** The results found that the 6 inter-network FCs of 5 brain networks were significantly altered and related to MMSE *** FC of the right orbital part of the middle frontal gyrus and right angular gyrus was mostly increased and positively related to MMSE score,and the FC of the right supramarginal gyrus and right temporal pole:Middle temporal gyrus was the only one decreased and negatively related to MMSE *** 17 significantly altered FCs of SA were taken into the support vector machine model,and the area under the curve was *** The identification of key brain networks and FC of SA could help us better understand the brain mechanism and further explore neuroimaging biomarkers of SA.

关键词： Successful aging Resting-state functional magnetic resonance imaging Surface-based brain network analysis Functional connectivity support vector machine algorithm

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Power System Transient Stability Assessment Model Construction Using Improved SVM

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IEEE ACCESS 2023年 11卷 131866-131876页

作者： Chen, Qianhui Shangqiu Inst Technol Sch Mech Engn Shangqiu 476000 Peoples R China

As computer technology and intelligence progress, many fields begin to develop towards intelligence. Power system is an important component of the whole power grid operation and the work of electronic components and electronic devices, so how to evaluate its stability is an important issue to ensure the stable operation of power system. In this study, a power system evaluation model using improved support vector machine algorithm is proposed for the transient state evaluation system of power system. Firstly, the characteristic vector is extracted from the transient steady-state data of power system, and then the traditional support vector machine algorithm is improved by adding Mahalanobis distance. Finally, the algorithm accuracy and precision are compared. It is found that the highest accuracy of the improved algorithm was 96.79%, which was 1.70% higher than that of the lowest algorithm. At the same time, the false alarm rate was lower, which was 0.42% lower than that of the highest algorithm. The accuracy of the improved support vector machine algorithm was 95.62%. Results show a higher accuracy and precision of the improved vector machine algorithm, and it is better in the processing ability and evaluation ability of power system data.

关键词： Power system stability Transient analysis support vector machines Generators Feature extraction Thermal stability Circuit faults Quality assessment Accuracy Power system transient state support vector machine algorithm assessment capability accuracy

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Research on the Load_breast_cancer data set under Multiple machine Learning algorithms 24

Research on the Load_breast_cancer data set under Multiple M...

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International Conference on Modeling, Natural Language Processing and machine Learning (CMNM)

作者： Song, Meirui Wu, Shixiao Huang, Rujuan Wuhan Business Univ Wuhan Peoples R China

ISBN: (纸本)9798400709760

breast cancer ranks first in female malignant tumors. Early detection and diagnosis is the key to treatment. This paper uses the open-source load_break_cancer breast cancer data set, mainly uses random forest, support vector machine, logical regression, Gauss naive Bayesian algorithm, BP neural network algorithm, k-neighborhood algorithm and XGBoost algorithm to classify and predict the breast cancer data set, conducts a lot of training and testing on the data set under a variety of machine learning algorithms, analyzes the learning curve in the training process, analyzes the training and testing results, and analyzes the performance of the algorithm processing data, which is of great significance for breast cancer diagnosis and treatment.

关键词： Random Forest algorithm support vector machine algorithm Logistic Regression algorithm Gaussian Naive Bayes algorithm BP Neural Network algorithm k-Neighbor algorithm XGBoost algorithm

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machine learning approach in mortality rate prediction for hemodialysis patients

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2022年第1期25卷 111-122页

作者： Radovic, Nevena Prelevic, Vladimir Erceg, Milena Antunovic, Tanja Univ Montenegro Elect Engn Dept Podgorica Montenegro Clin Ctr Montenegro Clin Nephrol Podgorica Montenegro Clin Ctr Montenegro Ctr Lab Diagnost Podgorica Montenegro

Kernel support vector machine algorithm and K-means clustering algorithm are used to determine the expected mortality rate for hemodialysis patients. The national nephrology database of Montenegro has been used to conduct this research. Mortality rate prediction is realized with accuracy up to 94.12% and up to 96.77%, when a complete database is observed and when a reduced database (that contains data for the three most common basic diseases) is observed, respectively. Additionally, it is shown that just a few parameters, most of which are collected during the sole patient examination, are enough for satisfying results.

关键词： Mortality rate prediction support vector machine algorithm K-means clustering algorithm chronic kidney disease

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Return Strategy and machine Learning Optimization of Tennis Sports Robot for Human Motion Recognition

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FRONTIERS IN NEUROROBOTICS 2022年 16卷 857595页

作者： Wang, Yuxuan Yang, Xiaoming Wang, Lili Hong, Zheng Zou, Wenjun Nanchang JiaoTong Inst Sports Inst Nanchang Peoples R China Univ Perpetual Help Syst Dalta Grad Sch Pinas Philippines Univ Putra Malaysia Fac Educ Studies Kuala Lumpur Malaysia East China Univ Technol Coll Phys Educ Nanchang Peoples R China Nanchang Univ Sch Software Nanchang Peoples R China

At present, there are many kinds of intelligent training equipment in tennis sports, but they all need human control. If a single tennis player uses the robot to return the ball, it will save some human resources. This study aims to improve the recognition rate of tennis sports robots in the return action and the return strategy. The human-oriented motion recognition of the tennis sports robot is taken as the starting point to recognize and analyze the return action of the tennis sports robot. The OpenPose traversal dataset is used to recognize and extract human motion features of tennis sports robots under different classifications. According to the return characteristics of the tennis sports robot, the method of tennis return strategy based on the support vector machine (SVM) is established, and the SVM algorithm in machine learning is optimized. Finally, the return strategy of tennis sports robots under eight return actions is analyzed and studied. The results reveal that the tennis sports robot based on the SVM-Optimization (SVM-O) algorithm has the highest return recognition rate, and the average return recognition rate is 88.61%. The error rates of the backswing, forward swing, and volatilization are high in the return strategy of tennis sports robots. The preparation action, backswing, and volatilization can achieve more objective results in the analysis of the return strategy, which is more than 90%. With the increase of iteration times, the effect of the model simulation experiment based on SVM-O is the best. It suggests that the algorithm proposed has a reliable accuracy of the return strategy of tennis sports robots, which meets the research requirements. Human motion recognition is integrated with the return motion of tennis sports robots. The application of the SVM-O algorithm to the return action recognition of tennis sports robots has good practicability in the return action recognition of tennis sports robot and solves the problem that the optimizati

关键词： OpenPose traversal dataset human motion characteristics machine learning support vector machine algorithm human motion recognition

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Fault Recognition Method for Isolating Switch Infrared Images Based on HOG Feature 6

Fault Recognition Method for Isolating Switch Infrared Image...

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6th International Conference on HVDC, HVDC 2024

作者： Guo, Shuangquan Lu, Jun Wang, Jinxin Dong, Zhaoyang State Grid Xinjiang Electric Power Co. Ltd. Electric Power Science Research Institute Urumqi China

ISBN: (纸本)9798350387759

In order to achieve fault recognition for isolating switch infrared images, this paper utilizes an improved SLIC algorithm to segment and label the fault areas of the isolating switch based on color space conversion, effectively improving the accuracy of image segmentation. Leveraging the histogram of oriented gradient (HOG) feature extraction and support vector machine (SVM) algorithm, the paper classifies and categorizes the infrared images of isolating switches to distinguish whether they are operating in normal states. For isolating switches in normal states, the relative temperature difference method is employed to judge their fault states, where a higher relative temperature difference indicates a more severe fault. Experimental results demonstrate that under optimized HOG feature parameters, the highest accuracy in image device recognition can be achieved. By diagnosing faults using infrared images, the severity of defects in isolating switches can be determined, and maintenance recommendations can be provided. The proposed model exhibits high accuracy and reliability. © 2024 IEEE.

关键词： fault diagnosis HOG characteristics isolating switch support vector machine algorithm

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