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Seismic Policy, Operations, and Research Uses for a Building Inventory in an Earthquake-Prone City

International Journal of Disaster Risk Science 2020年第6期11卷 709-718页

作者： Ken Elwood Olga Filippova Ilan Noy Jacob Pastor Paz School of Engineering The University of AucklandAuckland 1142New Zealand Business School The University of AucklandAuckland 1142New Zealand School of Economics Victoria University of WellingtonWellington 6140New Zealand

After the 2016 New Zealand Kaikoura Earthquake,the absence of information about the state of buildings in Wellington proved to be a source of significant policy *** did not know what damages to expect and therefore how to react,and policies needed to be formulated without a clear quantification of the ***,without detailed knowledge of the existing buildings,it was difficult to assess what the available legal and regulatory tools can achieve and choose among *** describe the creation of a building inventory database for Wellington initiated by the *** database aims to assist the generation of research on the risks,impacts,and viable solutions for reducing future seismic risk in Wellington’s central business district(cbd).The database includes structural,economic,and market information on virtually every significant building in the *** primary purposes are:to collate and provide the best available information about the expected seismic performance of the existing building stock;to assess the impact of possible multiple building failures due to a seismic event;to describe a viable cost-effective path for seismic retrofitting;and to inform the design of a regulatory structure that can facilitate this resilience-building agenda.

关键词： Building inventory Building standards Disaster risk assessment Earthquake-prone city Retrofit

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Classification of oil palm female inflorescences anthesis stages using machine learning approaches

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Information Processing in Agriculture 2021年第4期8卷 537-549页

作者： Mamehgol Yousefi D.B A.S.Mohd Rafie Samsuzana Abd Aziz Syaril Azrad Mohamed Mazmira Mohd Masri Ahmad Shahi O.F.Marzuki Department.of Aerospace Engineering Universiti Putra MalaysiaSerdangSelangor 43400Malaysia Smart Farming Technology Research Centre Dept.of Biological and Agricultural EngineeringUniversiti Putra MalaysiaSerdangSelangor 43400Malaysia MPOB Biological Research Division Persiaran InstitusiKawasan Institusi Bangi43000 KajangSelangorMalaysia auckland cbd Auckland 1010New Zealand Faculty of Agriculture and Food Sciences Univesiti Putra Malaysia Bintulu CampusJalan Nyabau97008 Bintulu SarawakMalaysia

Prediction of pollination stages in oil palm plantations is an important research area in precision *** palm is known as the most efficient commercial crop with the capacity to fulfill the growing global demand for vegetable oil ***,oil palm production dependence on pollination is experiencing issues with decreasing the actual ***,alternative methods in commercial plantations such as humanassisted pollination and recently Wireless Sensor Network(WSN)have been deployed despite their high economic costs due to labor *** palm assisted pollination requires precision,inspection,traceability,and validation processes in the ***,all these processes are performed by humans that can be associated with false assumptions,uncertainty,and pollination ***,Machine Learning(ML)approaches as a subset of the Artificial Intelligence(AI)domain provides efficient,cost-effective,and non-destructive solutions to determine these reproductive stages for future autonomous pollination *** goal was to reduce the variability of worker’s performance in oil palms,using ML algorithms to make expert decisions and reduce the risk related to a transient *** comparative empirical study examined and compared the performance of the Random Forest(RF)against k Nearest Neighbor(kNN)and Support Vector Machine(SVM)for classification of oil palm pre-anthesis and anthesis stages,dividing into four classes(1,2,3,and 4).These models were tested using thermal features(endogenous)individually and in combination with meteorological variables(exogenous).The performance of models is evaluated with specific measures of performance,such as overall user’s and producer’s accuracies and F-measure values derived from the confusion *** results showed that the RF model produced better results with regard to average Fmeasure(88.6%,71.83%),producer’s accuracies(88.70%,71.35%),and user’s accuracies(88.27%,72.36%)on test set

关键词： Oil palm Pollination Anthesis stage Machine Learning Classification

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