Proper waste management models using recent technologies like computer vision,machine learning(ML),and deep learning(DL)are needed to effectively handle the massive quantity of increasing waste.Therefore,waste classif...Proper waste management models using recent technologies like computer vision,machine learning(ML),and deep learning(DL)are needed to effectively handle the massive quantity of increasing waste.Therefore,waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management process.This study concentrates on the design of hazardous waste detection and classification using ensemble learning(HWDC-EL)technique to reduce toxicity and improve human health.The goal of the HWDC-EL technique is to detect the multiple classes of wastes,particularly hazardous and non-hazardous wastes.The HWDC-EL technique involves the ensemble of three feature extractors using Model Averaging technique namely discrete local binary patterns(DLBP),EfficientNet,and DenseNet121.In addition,the flower pollination algorithm(FPA)based hyperparameter optimizers are used to optimally adjust the parameters involved in the EfficientNet and DenseNet121 models.Moreover,a weighted voting-based ensemble classifier is derived using three machine learning algorithms namely support vector machine(SVM),extreme learning machine(ELM),and gradient boosting tree(GBT).The performance of the HWDC-EL technique is tested using a benchmark Garbage dataset and it obtains a maximum accuracy of 98.85%.展开更多
Agricultural environmental pollution emergencies have become a hot research topic because of the high incidence and influence depth.This paper introduces classification and features of agricultural environmental pollu...Agricultural environmental pollution emergencies have become a hot research topic because of the high incidence and influence depth.This paper introduces classification and features of agricultural environmental pollution emergencies:by pollutant type,it falls into organic pollution emergencies and inorganic pollution emergencies;by the approach of entering agricultural environment,it falls into water resource agricultural environmental pollution emergencies and non-water resource agricultural environmental pollution emergencies.Hazards of agricultural environmental pollution emergencies are analyzed from 4 perspectives:personal security,indirect loss,ecological environment and social stability.In view of the hazards,countermeasures are given to deal with the pollution emergencies as(i)establishing a risk evaluation mechanism for agricultural environment;(ii)enhancing the capacity of handling agricultural environmental pollution emergencies;(iii)introducing new management concepts for environmental emergencies,and cultivating keen emergency management consciousness.展开更多
Various types of geological hazards exist in the South China Sea. In dynamics sense, they can be categorized into 5 principal genetic types related to effects of hydraulic dynamics, gaseous activity, soil mechanics, g...Various types of geological hazards exist in the South China Sea. In dynamics sense, they can be categorized into 5 principal genetic types related to effects of hydraulic dynamics, gaseous activity, soil mechanics, gravity and tectonism, respectively. Integrated analyses indicate that the geological hazards associated with volcanoes, earthquakes and fractures are mainly distributed in tectonically active regions, whereas those resulting from mudflows, landslides and diapirs are usually concentrated in the region of slope, that shallow gas, high pressure gas pockets and soft intercalations are major potential geological hazards in the inner shelf, and that strong hydraulic dynamics, especially storm tide, is one of the major causes of geological hazards in the littoral areas. The geological hazards that occurred in the South China Sea are also characterized by periodicity, succession and, to a certain extent, unpredictability in addition to regionalization.展开更多
湘北常澧山地-丘陵地区地理地质环境复杂,滑坡地质灾害点多、面广、零散、频发,是造成人员伤亡和经济损失最主要的地质灾害类型。InSAR、光学遥感、LiDAR、GIS多源遥感综合技术,是目前可行性高、精度良好的滑坡地灾隐患识别和监测技术方...湘北常澧山地-丘陵地区地理地质环境复杂,滑坡地质灾害点多、面广、零散、频发,是造成人员伤亡和经济损失最主要的地质灾害类型。InSAR、光学遥感、LiDAR、GIS多源遥感综合技术,是目前可行性高、精度良好的滑坡地灾隐患识别和监测技术方法,能够满足宏观大范围、时效性等要求。该文基于InSAR形变速率数据、多光谱影像和DEM数据对湖南常澧地区的滑坡地灾隐患进行了识别和提取:首先用2种决策树分类方法对多光谱图像进行了土地利用分类,以便于观察研究区的用地类别及分布情况;然后运用DEM数据提取了高程、坡度、坡向、起伏度和曲率等5项地形地貌因子对研究区进行了滑坡危险性评价;再基于SBAS-InSAR技术对研究区进行地表时序微形变测量;最后在GIS系统内综合危险性评价结果和形变速率对研究区滑坡隐患进行提取和圈定,并基于CART决策树分类结果和研究区水系分布情况,对研究区内除圈定的滑坡隐患点以外的形变速率大于-0.01 m/a的区域进行了危险性推断。本次研究在植被覆盖区和裸露区识别出了数处隐蔽性高、规模小的滑坡隐患,并圈定了滑坡隐患的空间分布范围,面积0.126 km 2,证明了技术方法的有效性,具有一定的实践应用价值。展开更多
If progress is to be made toward improving geohazard management and emergency decision-making,then lessons need to be learned from past geohazard information.A geologic hazard report provides a useful and reliable sou...If progress is to be made toward improving geohazard management and emergency decision-making,then lessons need to be learned from past geohazard information.A geologic hazard report provides a useful and reliable source of information about the occurrence of an event,along with detailed information about the condition or factors of the geohazard.Analyzing such reports,however,can be a challenging process because these texts are often presented in unstructured long text formats,and contain rich specialized and detailed information.Automatically text classification is commonly used to mine disaster text data in open domains(e.g.,news and microblogs).But it has limitations to performing contextual long-distance dependencies and is insensitive to discourse order.These deficiencies are most obviously exposed in long text fields.Therefore,this paper uses the bidirectional encoder representations from Transformers(BERT),to model long text.Then,utilizing a softmax layer to automatically extract text features and classify geohazards without manual features.The latent Dirichlet allocation(LDA)model is used to examine the interdependencies that exist between causal variables to visualize geohazards.The proposed method is useful in enabling the machine-assisted interpretation of text-based geohazards.Moreover,it can help users visualize causes,processes,and other geohazards and assist decision-makers in emergency responses.展开更多
基金the Deanship of Scientific Research at King Khalid University for funding this work underGrant Number(RGP 2/209/42)PrincessNourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R136)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4210118DSR27).
文摘Proper waste management models using recent technologies like computer vision,machine learning(ML),and deep learning(DL)are needed to effectively handle the massive quantity of increasing waste.Therefore,waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management process.This study concentrates on the design of hazardous waste detection and classification using ensemble learning(HWDC-EL)technique to reduce toxicity and improve human health.The goal of the HWDC-EL technique is to detect the multiple classes of wastes,particularly hazardous and non-hazardous wastes.The HWDC-EL technique involves the ensemble of three feature extractors using Model Averaging technique namely discrete local binary patterns(DLBP),EfficientNet,and DenseNet121.In addition,the flower pollination algorithm(FPA)based hyperparameter optimizers are used to optimally adjust the parameters involved in the EfficientNet and DenseNet121 models.Moreover,a weighted voting-based ensemble classifier is derived using three machine learning algorithms namely support vector machine(SVM),extreme learning machine(ELM),and gradient boosting tree(GBT).The performance of the HWDC-EL technique is tested using a benchmark Garbage dataset and it obtains a maximum accuracy of 98.85%.
基金Supported by Humanities and Social Science Fund of Henan Provincial Department of Education(2013-QN-027)Doctoral Fund of Henan Polytechnic University(B2012-008)
文摘Agricultural environmental pollution emergencies have become a hot research topic because of the high incidence and influence depth.This paper introduces classification and features of agricultural environmental pollution emergencies:by pollutant type,it falls into organic pollution emergencies and inorganic pollution emergencies;by the approach of entering agricultural environment,it falls into water resource agricultural environmental pollution emergencies and non-water resource agricultural environmental pollution emergencies.Hazards of agricultural environmental pollution emergencies are analyzed from 4 perspectives:personal security,indirect loss,ecological environment and social stability.In view of the hazards,countermeasures are given to deal with the pollution emergencies as(i)establishing a risk evaluation mechanism for agricultural environment;(ii)enhancing the capacity of handling agricultural environmental pollution emergencies;(iii)introducing new management concepts for environmental emergencies,and cultivating keen emergency management consciousness.
文摘Various types of geological hazards exist in the South China Sea. In dynamics sense, they can be categorized into 5 principal genetic types related to effects of hydraulic dynamics, gaseous activity, soil mechanics, gravity and tectonism, respectively. Integrated analyses indicate that the geological hazards associated with volcanoes, earthquakes and fractures are mainly distributed in tectonically active regions, whereas those resulting from mudflows, landslides and diapirs are usually concentrated in the region of slope, that shallow gas, high pressure gas pockets and soft intercalations are major potential geological hazards in the inner shelf, and that strong hydraulic dynamics, especially storm tide, is one of the major causes of geological hazards in the littoral areas. The geological hazards that occurred in the South China Sea are also characterized by periodicity, succession and, to a certain extent, unpredictability in addition to regionalization.
文摘湘北常澧山地-丘陵地区地理地质环境复杂,滑坡地质灾害点多、面广、零散、频发,是造成人员伤亡和经济损失最主要的地质灾害类型。InSAR、光学遥感、LiDAR、GIS多源遥感综合技术,是目前可行性高、精度良好的滑坡地灾隐患识别和监测技术方法,能够满足宏观大范围、时效性等要求。该文基于InSAR形变速率数据、多光谱影像和DEM数据对湖南常澧地区的滑坡地灾隐患进行了识别和提取:首先用2种决策树分类方法对多光谱图像进行了土地利用分类,以便于观察研究区的用地类别及分布情况;然后运用DEM数据提取了高程、坡度、坡向、起伏度和曲率等5项地形地貌因子对研究区进行了滑坡危险性评价;再基于SBAS-InSAR技术对研究区进行地表时序微形变测量;最后在GIS系统内综合危险性评价结果和形变速率对研究区滑坡隐患进行提取和圈定,并基于CART决策树分类结果和研究区水系分布情况,对研究区内除圈定的滑坡隐患点以外的形变速率大于-0.01 m/a的区域进行了危险性推断。本次研究在植被覆盖区和裸露区识别出了数处隐蔽性高、规模小的滑坡隐患,并圈定了滑坡隐患的空间分布范围,面积0.126 km 2,证明了技术方法的有效性,具有一定的实践应用价值。
基金supported by the Natural Science Foundation of China(No.42301492)the National Key Research and Development Program(No.2022YFB3904200)+4 种基金the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources(No.KF-2022-07-014)the Natural Science Foundation of Hubei Province of China(No.2022CFB640)the Open Fund of Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering(No.2022SDSJ04)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(No.GLAB 2023ZR01)the Fundamental Research Funds for the Central Universities.
文摘If progress is to be made toward improving geohazard management and emergency decision-making,then lessons need to be learned from past geohazard information.A geologic hazard report provides a useful and reliable source of information about the occurrence of an event,along with detailed information about the condition or factors of the geohazard.Analyzing such reports,however,can be a challenging process because these texts are often presented in unstructured long text formats,and contain rich specialized and detailed information.Automatically text classification is commonly used to mine disaster text data in open domains(e.g.,news and microblogs).But it has limitations to performing contextual long-distance dependencies and is insensitive to discourse order.These deficiencies are most obviously exposed in long text fields.Therefore,this paper uses the bidirectional encoder representations from Transformers(BERT),to model long text.Then,utilizing a softmax layer to automatically extract text features and classify geohazards without manual features.The latent Dirichlet allocation(LDA)model is used to examine the interdependencies that exist between causal variables to visualize geohazards.The proposed method is useful in enabling the machine-assisted interpretation of text-based geohazards.Moreover,it can help users visualize causes,processes,and other geohazards and assist decision-makers in emergency responses.
