RESUMEN
Aims: To examine the utility of open data for flood mapping of the Bangkok Metropolitan Region and Chao Phraya River basin. The region is particularly vulnerable to flooding, having experienced recurrent major flooding events, including the some of the most extensive and prolonged in 2011. Study Design: Novel methodologies were innovated utilising open spatial data and open source geographical software to generate flood extent/hazard maps of the Bangkok Metropolitan Region and Chao Phraya River basin. Key geospatial data were sourced from the Thai Geo-Informatics and Space Technology Development Agency and NASA’s Shuttle Radar Topography Mission. Methodology: Given limited resources for conducting detailed hydrological-hydraulic analyses, two alternative approaches were examined for flood extent/hazard mapping of the basin and city. The first method made use of publicly available historical flood data to produce an up-to-date composite flood extent/hazard map. The second approach, using the latter output as a reference source, examined the utility of a modified topographic index for delineating flood-prone areas, as integrated into the r.hazard.flood module of the open source GRASS GIS application. Results: Compilation of multi-year historical data enabled generation of a relatively finescale (~100m spatial resolution) flood extent/hazard map for the basin and city. The optimal tau threshold for delineating flood exposed cells from the modified topographic index was linearly related to the sub-basin mean slope. The four most northerly subbasins of the Chao Phraya basin, those with higher mean slopes, gave lowest total errors, ranging from 17.5 to 35.9 percent. Conclusions: Open data in the form of multi-year spatial flood layers were effectively combined to generate a relatively fine-scale flood extent/hazard map for the Chao Phraya River basin and Bangkok Metropolitan Region, and the modified topographic index showed promise as an alternative means for identifying flood exposed areas.
RESUMEN
Mapas pedológicos são fontes de informações primordiais para planejamento e manejo do uso do solo, porém apresentam altos custos de produção. A fim de produzir mapas de solos a partir de mapas existentes, neste trabalho, foram comparados métodos de classificação em estágio único (Regressões Logísticas Múltiplas Multinomiais e Bayes) e em estágios múltiplos (Classification and Regression Trees (CART), J48 e Logistic Model Trees (LMT)) com a utilização de sistemas de informações geográficas e de variáveis geomorfométricas para produção de mapas pedológicos com legenda original e simplificada. A base de dados foi gerenciada em aplicativo computacional ArcGis, em que as variáveis e o mapa original foram relacionados por meio de amostras de treinamento para os algoritmos. Os resultados dos algoritmos obtidos no software Weka foram implementados no ArcGis, para a confecção dos mapas. Foram geradas matrizes de erros para análise de acurácias dos mapas. As variáveis geomorfométricas de declividade, perfil e plano de curvatura, elevação e índice de umidade topográfica são aquelas que melhor explicam a distribuição espacial das classes de solo. Os métodos de classificação em estágio múltiplo apresentaram sensíveis melhoras nas acurácias globais, porém significativas melhoras nos índices Kappa. A utilização de legenda simplificada aumentou significativamente as acurácias do produtor e do usuário.
Soil maps are sources of important information for land planning and management, but are expensive to produce. This paper proposes testing and comparing single stage classification methods (Multiple Multinomial Logistic Regression and Bayes) and multiple stage classification methods (Classification and Regression Trees (CART), J48 and Logistic Model Trees (LMT)) using geographic information system and terrain parameters for producing soil maps with both original and simplified legend. The database was managed in ArcGis computer application in which the variables and the original map were related through training of the algorithms. The results from statistical software Weka were implemented in ArcGis environment to generate digital soil maps. The terrain parameters that best explained soil distribution were slope, profile and planar curvature, elevation, and topographic wetness index. The multiple stage classification methods showed small improvements in overall accuracies and large improvements in the Kappa index. Simplification of the original legend significantly increased the producer and user accuracies, however produced small improvements in overall accuracies and Kappa index.