Natural disasters cause considerable property damage and loss of life as well as destruction of ecosystem and natural resources. In the context of global change, extreme events are expected to increase in both frequency and intensity. To prevent natural disasters and mitigate the loss, we need to act quickly and effectively. It would be conducive to achieve sustainable economic development, reduce disaster vulnerabilities and risks, and build resilience through implementing effective measures of disaster prevention, preparedness, response, and recovery. 相似文献
Two models, artificial neural network (ANN) and multiple linear regression (MLR), were developed to estimate typical grassland aboveground dry biomass in Xilingol River Basin, Inner Mongolia, China. The normalized difference vegetation index (NDVI) and topographic variables (elevation, aspect, and slope) were combined with atmospherically corrected reflectance from the Landsat ETM+ reflective bands as the candidate input variables for building both models. Seven variables (NDVI, aspect, and bands 1, 3, 4, 5 and 7) were selected by the ANN model (implemented in Statistica 6.0 neural network module), while six (elevation, NDVI, and bands 1, 3, 5 and 7) were picked to fit the MLR function after a stepwise analysis was executed between the candidate input variables and the above ground dry biomass. Both models achieved reasonable results with RMSEs ranging from 39.88% to 50.08%. The ANN model provided a more accurate estimation (RMSEr = 39.88% for the training set, and RMSEr = 42.36% for the testing set) than MLR (RMSEr = 49.51% for the training, and RMSEr = 53.20% for the testing). The final above ground dry biomass maps of the research area were produced based on the ANN and MLR models, generating the estimated mean values of 121 and 147 g/m2, respectively. 相似文献
Numerous studies had focused on the association between air pollution and health outcomes in recent years. However, little evidence is available on associations between air pollutants and premature rupture of membranes (PROM). Therefore, we performed time-series analysis to evaluate the association between PROM and air pollution. The daily average concentrations of PM2.5, SO2 and NO2 were 54.58 μg/m3, 13.06 μg/m3 and 46.09 μg/m3, respectively, and daily maximum 8-h average O3 concentration was 95.67 μg/m3. The strongest effects of SO2, NO2 and O3 were found in lag4, lag06 and lag09, and an increase of 10 μg/m3 in SO2, NO2 and O3 was corresponding to increase in incidence of PROM of 8.74% (95% CI 2.12–15.79%), 3.09% (95% CI 0.64–5.59%) and 1.68% (95% CI 0.28–3.09%), respectively. There were no significant effects of PM2.5 on PROM. Season-specific analyses found that the effects of PM2.5, SO2 and O3 on PROM were more obvious in cold season, but the statistically significant effect of NO2 was observed in warm season. We also found the modifying effects by maternal age on PROM, and we found that the effects of SO2 and NO2 on PROM were higher among younger mothers (<?35 years) than advanced age mothers (≥?35 years); however,?≥?35 years group were more vulnerable to O3 than?<?35 years group. This study indicates that air pollution exposure is an important risk factor for PROM and we wish this study could provide evidence to local government to take rigid approaches to control emissions of air pollutants.
