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The Effect of Climate Variables on Dengue Burden in Indonesia: A Case Study from Medan City

Journal of Geoscience and Environment Protection 2019年第10期7卷 80-94页

作者： martiwi diah setiawati Institute for Future Initiatives The University of Tokyo Tokyo Japan

Dengue is the world most serious arboviral diseases with regard to the number of people infected. In 2012, WHO informed that Indonesia is the second largest with dengue cases among the endemic countries. The most prevalent province in Indonesia of dengue cases outside java island was North Sumatra where Medan city was recorded as the highest cases within the province. Urbanization, demographic change and warming temperatures were related with the recent expansion of the primary vector of dengue;Aedes aegypti and Aedes albopictus. In this paper, we examined the relation between climate factors and dengue cases in the study area. The association of them was performed through Generalized Additive Models (GAM), considering the number of dengue patients as response variable and climatic factor such as precipitation, minimum temperature, maximum temperature, average temperature and relative humidity as predictor variables. In addition, using this model vulnerability map was constructed. The result stated that climate variation influenced the number of dengue haemorrhagic fever (DHF) patients as 66.1% with precipitation variable was more important followed by maximum temperature. Furthermore, the highest risk of dengue was located in the main city of Medan.

关键词： Dengue GAM Climate Variables Vulnerability

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Evaluation of GSMaP Daily Rainfall Satellite Data for Flood Monitoring: Case Study—Kyushu Japan

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Journal of Geoscience and Environment Protection 2016年第12期4卷 101-117页

作者： martiwi diah setiawati Fusanori Miura Integrated Research System for Sustainability Science (IR3S) The University of Tokyo Bunkyo Japan Graduate School of Science and Engineering Yamaguchi University Ube Japan

In this paper, the Global Satellite Mapping of Precipitation Moving Vector with Kalman filter (GSMaP_MVK) was evaluated and corrected at daily time scales with a spatial resolution of 0.1°;latitude/longitude. The reference data came from thirty-four rain gauges on Kyushu Island, Japan. This study focused on the GSMaP_MVK’s ability to detect heavy rainfall patterns that may lead to flooding. Statistical analysis was used to evaluate the GSMaP_MVK data both quantitatively and qualitatively. The statistical analysis included the relative bias (B), the mean error (E), the Nash-Sutcliffe coefficient (CNS), the Root Mean Square Error (RMSE) and the correlation coefficient (r). In addition, Generalized Additive Models (GAMs) were used to conduct GSMaP_MVK data correction. The results of these analyses indicate that GSMaP_MVK data have lower values than observed data and may be significantly underestimated during heavy rainfall. By applying GAM to bias correction, GSMaP_MVK’s ability to detect heavy rainfall was improved. In addition, GAM for bias correction could effectively be applied for significant underestimates of GSMaP_ MVK (i.e., bias of more than 55%). GAM is a new approach to predict rainfall amount for flood and landslide monitoring of satellite base precipitation, especially in areas where rain gauge data are limited.

关键词： Evaluation GSMaP_MVK Flood Monitoring Kyushu

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