An SOM network model was developed for retrievals of the oceanic total precipitable water (PW) from the SSM/I, by using the SSM/I and radiosonde observation data set provided by the NASDA (Japan). The model was first ...An SOM network model was developed for retrievals of the oceanic total precipitable water (PW) from the SSM/I, by using the SSM/I and radiosonde observation data set provided by the NASDA (Japan). The model was first trained by 5/6 of the data, and the other data were used to test the retrieval ability of the model. The retrieval results showed that the SOM network model was better than the routine operational algorithm.展开更多
Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annu...Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annuallyinthe urban area of Beijing, the capital of China. Based on a selforganizing map(SOM) artificial neural network(ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product(GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANNis suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors,producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. The points that were assigned risk grades of IV or Vwere located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.展开更多
The Self-Organizing Map (SOM) is an unsupervised neural network algorithm that projects high-dimensional data onto a two-dimensional map. The projection preserves the topology of the data so that similar data items wi...The Self-Organizing Map (SOM) is an unsupervised neural network algorithm that projects high-dimensional data onto a two-dimensional map. The projection preserves the topology of the data so that similar data items will be mapped to nearby locations on the map. One of the SOM neural network’s applications is clustering of animals due their features. In this paper we produce an experiment to analyze the SOM in clustering different species of animals.展开更多
Development of coastal ecotourism has been a focus of Shandong government, and the suitability evaluation of regional coastal ecotourism is crucial for the reasonable plan and sustainable development of Shandong coast...Development of coastal ecotourism has been a focus of Shandong government, and the suitability evaluation of regional coastal ecotourism is crucial for the reasonable plan and sustainable development of Shandong coastal ecotourism. By using MATLAB language to establish a SOM neural network model, this paper evaluates the coastal ecotourism suitability of four regions, Qingdao, Yantai, Weihai and Rizhao of Shandong Province and divides 33 subordinate regions of those four cities into four categories, i.e. regions poorly suitable for ecotourism resources, regions highly suitable for coastal ecotourism, regions secondly suitable for coastal ecotourism, regions ordinarily suitable for coastal ecotourism. Related suggestions on development of regional coastal ecotourism have been given in the final conclusions.展开更多
文摘An SOM network model was developed for retrievals of the oceanic total precipitable water (PW) from the SSM/I, by using the SSM/I and radiosonde observation data set provided by the NASDA (Japan). The model was first trained by 5/6 of the data, and the other data were used to test the retrieval ability of the model. The retrieval results showed that the SOM network model was better than the routine operational algorithm.
基金supported by the National Key R&D Program of China (GrantN o.2016YFC0401407)National Natural Science Foundation of China (Grant Nos. 51479003 and 51279006)
文摘Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters haveoccurred almost annuallyinthe urban area of Beijing, the capital of China. Based on a selforganizing map(SOM) artificial neural network(ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product(GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANNis suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors,producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. The points that were assigned risk grades of IV or Vwere located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.
文摘The Self-Organizing Map (SOM) is an unsupervised neural network algorithm that projects high-dimensional data onto a two-dimensional map. The projection preserves the topology of the data so that similar data items will be mapped to nearby locations on the map. One of the SOM neural network’s applications is clustering of animals due their features. In this paper we produce an experiment to analyze the SOM in clustering different species of animals.
文摘Development of coastal ecotourism has been a focus of Shandong government, and the suitability evaluation of regional coastal ecotourism is crucial for the reasonable plan and sustainable development of Shandong coastal ecotourism. By using MATLAB language to establish a SOM neural network model, this paper evaluates the coastal ecotourism suitability of four regions, Qingdao, Yantai, Weihai and Rizhao of Shandong Province and divides 33 subordinate regions of those four cities into four categories, i.e. regions poorly suitable for ecotourism resources, regions highly suitable for coastal ecotourism, regions secondly suitable for coastal ecotourism, regions ordinarily suitable for coastal ecotourism. Related suggestions on development of regional coastal ecotourism have been given in the final conclusions.
