Spatial Evaluation of Earthquake Disaster Based On Correlation Characteristics And BP Neural Network

Rapid spatial evaluation of disaster after earthquake occurrence is required in the emergency rescue management, due to its significant support for decreasing casualties and property losses. The earthquake-hit population is taken as an example of earthquake disaster to construct the evaluation model using the data from the 2013 Ms7.0 Lushan earthquake. Ten influencing factors are classified into environmental factors and seismic factors. The correlation analysis reveals characteristics that there is a nonlinear relationship between the earthquake-hit population and various factors, and per capita GDP and PGA factor have a stronger correlation with earthquake-hit population. Moreover, the spatial variability of influencing factors would affect the distribution of earthquake-hit population. The earthquake-hit population is evaluated using BP neural network with optimizing training samples based on the spatial characteristics of per capita GDP and PGA factors. Different number of sample points are generated in areas with different value intervals of influencing factors, instead of the random distribution of sample points. The minimum value of RMSE (Root Mean Square Error) from testing set is 18 people/km2, showing good accuracy in the spatial evaluation of earthquake-hit population. Meanwhile, the optimizing samples considering spatial characteristics could improve the convergence speed and generalization capability comparing to random samples. The trained network was generalized to the 2017 Ms7.0 Jiuzhaigou earthquake to verify the prediction accuracy. The evaluation results indicate that BP neural network considering the correlation characteristics of factors has the capability to evaluate the seismic disaster information in space, providing more detailed information for emergency service and rescue operation.

Spatial Evaluation of Health Risk due to Inhalation of PM2.5 Pollutants in the Metropolitan Areas of Toluca Valley and Mexico Valley

Aims: In the present work, health risk from inhalation of PM2.5 pollutants in both areas was assessed spatially Place and Duration of Study: The metropolitan areas of the Toluca (MATV) and Mexico Valleys (MAMV), between September and November 2009. Methodology: A simultaneous sampling campaign was conducted in the Toluca and Mexico Valleys on alternate days from September-22 to November-29, 2009. From the samples collected, their gravimetric concentration was obtained, and S, Cl, K, Ca Ti, V, Mn, Fe, Ni, Cu, Zn, and Pb were determined using the particle-induced X-ray emission technique (PIXE). Results: The health risk by inhalation of PM2.5 with a higher result for the metropolitan area of the Toluca Valley (2.09 for adults, 6.25 for children from 6-12 years old, and 6.58 for children from 2-6 years old) in contrast with that of the metropolitan area of the Mexico Valley (1.67 for adults, 5.20 for children from 6-12 years old, and 5.28 for children from 2-6 years old). Conclusion: These results are perhaps due to the higher concentration of Cl and Mn for the MATV. Additionally, the air parcels from sampling site MAMV go to MATV and thus contributes to an increased health risk from inhalation of PM2.5. There are health risks for the inhalation of PM2.5 in the MATV and MAMV study areas. The risk only considers the elemental risk. There are no similar studies for this comparison between MATV and MAMV in the literature.

Earthquake-hit Population Spatial Evaluation Based on Correlation Characteristics and BP Neural Network

Rapid spatial evaluation of earthquake-hit population after earthquake occurrence is required in the disaster emergency rescue management, due to its significant support for decreasing casualties and property losses. The correlation between earthquake-hit population and influencing factors are analyzed using the data from the 2013 Ms7.0 Lushan earthquake. Ten influencing factors including elevation, slope angle, population density, per capita GDP, distance to fault, distance to river, NDVI, PGA, PGV and distance to epicenter, are classified into environmental factors and seismic factors. The correlation analysis reveals characteristics that there is a nonlinear relationship between the earthquake-hit population and various factors, and per capita GDP and PGA factor have a stronger correlation with earthquake-hit population. Moreover, the spatial variability of influencing factors would affect the distribution of earthquake-hit population. The earthquake-hit population is evaluated using BP neural network with optimizing training samples based on the spatial characteristics of per capita GDP and PGA factors. Different number of sample points are generated in areas with different value intervals of influencing factors, instead of the random distribution of sample points. The minimum value of RMSE (Root Mean Square Error) from testing set is 18 people/km2, showing good accuracy in the spatial evaluation of earthquake-hit population. Meanwhile, the optimizing samples considering spatial characteristics could improve the convergence speed and generalization capability comparing to random samples. The trained network was generalized to the 2017 Ms7.0 Jiuzhaigou earthquake to verify the prediction accuracy. The mean absolute error of earthquake-hit population evaluation results in different counties under the Jiuzhaigou earthquake were 18357 people and 26121 people for optimizing samples and random samples, respectively. The evaluation results indicate that BP neural network considering the correlation characteristics of factors has the capability to evaluate the earthquake-hit population in space, providing more detailed information for emergency service and rescue operation.

Spatial evaluation of the improvement of the municipal entity governance framework

Subject. This article deals with the issues of management of a municipal formation and implementation of national projects in the constituent entities of the Russian Federation. Objectives. The article aims to develop a spatial methodology to assess the improvement of the municipal formation management mechanism and test this methodology regarding the Orel Oblast municipalities as a case study. Methods. For the study, we used speculative and estimating methods. Results. The methods used to assess the modernization of the management system are focused on the study of the competitiveness and socio-economic development of municipalities and the region. The article proposes a methodology to evaluate the processes of modernization of the municipal formation management mechanism on the basis of the relationship with the areas of national projects implemented in the Orel Oblast. Conclusions. In terms of development level, most of the Orel Oblast municipalities do not meet the requirements stated in the national projects.

ESTUDO DA VARIABILIDADE PLUVIOMÉTRICA ESPAÇO-TEMPORAL NA MICRORREGIÃO DE SOUSA, PARAÍBA

<div class="WordSection1"><p>O Nordeste Brasileiro é uma das regiões mais problemáticas no que se refere à disponibilidade de água, portanto, se tem necessidade de atentar a práticas e métodos para desenvolvimento dessa região, visto que as condições climáticas, a hidrologia e sua vegetação são de extrema importância para compreender como esse ambiente se forma. O objetivo principal deste trabalho é verificar e avaliar a variabilidade climática da microrregião de Sousa, através da análise espaço-temporal mensal e anual da sua precipitação, identificando-se, assim, os períodos secos e chuvosos da área estudada com o auxílio do Índice de Anomalia de Chuva (IAC). Os dados pluviométricos utilizados na pesquisa correspondem às séries mensais de precipitação no período de 1994 a 2017 fornecidos pela AESA, para a avaliação temporal, espacial e para o cálculo do Índice de Anomalia de Chuva (IAC). A microrregião de Sousa apresenta dois períodos distintos, um período de cinco meses chuvosos e outro com sete meses secos. A distribuição espacial da precipitação da microrregião possui áreas bem distintas, onde a maior concentração de precipitação se localiza na parte sudoeste, em contrapartida, a região noroeste e em um ponto na parte central apresentaram valores baixos de precipitação.</p><p><strong>Palavras-chave:</strong> Períodos Secos, Períodos Chuvosos e Índice de Anomalia de Chuva.</p><p> </p><p align="center">STUDY OF SPACE-TIME PLUVIOMETRIC VARIABILITY IN THE SOUSA MICROREGION, PARAÍBA</p><p><strong>Abstract</strong></p><p>The Brazilian Northeast is one of the most problematic regions in terms of water availability. Therefore, it is necessary to consider the practices and methods for the development of this region, since climatic conditions, hydrology and vegetation are of extreme importance to understand how this environment is formed. The main objective of this work is to verify and evaluate the climatic variability of the Sousa microregion, through the monthly and annual space-time analysis of its precipitation, thus identifying the dry and rainy periods of the studied area with the aid of the Anomaly Index Rainfall (IAC). The rainfall data used in the research correspond to the monthly rainfall series from 1994 to 2017 provided by the EFSA for the temporal and spatial evaluation and for the calculation of the Rainfall Anomaly Index (IAC). The Sousa microregion has two distinct periods, one period of five rainy months and the other with seven dry months. This work has a relevance in the area of ecology, which through the results help to collaborate with the development of the microregion, which through its managers dominate the knowledge of the stations pointed to abundance and water scarcity for the anthropic activities.</p><p><strong>Key-words: </strong>Dry Periods, Rainy Periods, and Rain Anomaly Index.</p></div>