Symbols are considered as the language of a map;hence,accurate understanding of the meaning of symbols is crucial when obtaining geographical information from a map： the symbolisation of spatial data is of key import...Symbols are considered as the language of a map;hence,accurate understanding of the meaning of symbols is crucial when obtaining geographical information from a map： the symbolisation of spatial data is of key importance in cartography.A geographical information system（GIS） provides a convenient mapping platform and powerful functions for spatial data symbolisation,while the presence of various mapping standards impedes the understanding of maps and sharing of map information.On the other hand,the available GIS platforms find it difficult to deal with automatic conversion between maps and different mapping standards.To resolve this problem,an approach for symbol recognition and automatic conversion is proposed,and a conversion system based on the approach and the Arc GIS Engine platform is developed to realise automatic conversion between maps produced based on different mapping standards.To test these conversion effects of the proposed system,the petroleum sector is chosen as the research field and the mutual conversion of a map in practical work among the three mapping standards（i.e.the Chinese Petroleum,Shell and USGS standards） governing this field is taken as a casestudy.The results show that the conversion system has a high conversion accuracy and strong applicability.展开更多
Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural hazard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting d...Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural hazard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting debris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and useful in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time series of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collected in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.展开更多
基金supported by the National Major Specific Project of Oil and Gas during the Twelfth Five-Year Plan (2011ZX05028-004)the Major Science and Technology Program of Petro China (2012D-4602-05)the National Natural Science Foundation of China (61501064)
文摘Symbols are considered as the language of a map;hence,accurate understanding of the meaning of symbols is crucial when obtaining geographical information from a map： the symbolisation of spatial data is of key importance in cartography.A geographical information system（GIS） provides a convenient mapping platform and powerful functions for spatial data symbolisation,while the presence of various mapping standards impedes the understanding of maps and sharing of map information.On the other hand,the available GIS platforms find it difficult to deal with automatic conversion between maps and different mapping standards.To resolve this problem,an approach for symbol recognition and automatic conversion is proposed,and a conversion system based on the approach and the Arc GIS Engine platform is developed to realise automatic conversion between maps produced based on different mapping standards.To test these conversion effects of the proposed system,the petroleum sector is chosen as the research field and the mutual conversion of a map in practical work among the three mapping standards（i.e.the Chinese Petroleum,Shell and USGS standards） governing this field is taken as a casestudy.The results show that the conversion system has a high conversion accuracy and strong applicability.
文摘Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural hazard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting debris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and useful in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time series of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collected in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.
