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Chen W.,University of Science and Technology of China | Chai H.,China University of Mining and Technology | Sun X.,University of Science and Technology of China | Wang Q.,China University of Mining and Technology | And 2 more authors.
Arabian Journal of Geosciences | Year: 2016

The aim of this study is to generate reliable susceptibility maps using frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) models based on geographic information system (GIS) for the Qianyang County of Baoji City, China. At first, landslide locations were identified by earlier reports, aerial photographs, and field surveys, and a total of 81 landslides were mapped from various sources. Then, the landslide inventory was randomly split into a training dataset 70 % (56 landslides) for training the models, and the remaining 30 % (25 landslides) was used for validation purpose. In this case study, 13 landslide-conditioning factors were exploited to detect the most susceptible areas. These factors are slope angle, slope aspect, curvature, plan curvature, profile curvature, altitude, distance to faults, distance to rivers, distance to roads, Sediment Transport Index (STI), Stream Power Index (SPI), Topographic Wetness Index (TWI), and lithology. Subsequently, landslide-susceptible areas were mapped using the FR, SI, and WoE models based on landslide-conditioning factors. Finally, the accuracy of the landslide susceptibility maps produced from the three models was verified by using areas under the curve (AUC). The AUC plot estimation results showed that the susceptibility map using FR model has the highest training accuracy of 83.62 %, followed by the SI model (83.45 %), and the WoE model (82.51 %). Similarly, the AUC plot showed that the prediction accuracy of the three models was 79.40 % for FR model, 79.35 % for SI model, and 78.53 % for WoE model, respectively. According to the validation results of the AUC evaluation, the map produced by FR model exhibits the most satisfactory properties. © 2016, Saudi Society for Geosciences. Source

Hong H.,Jiangxi Provincial Meteorological Observatory | Pourghasemi H.R.,Shiraz University | Pourtaghi Z.S.,University of Yazd
Geomorphology | Year: 2016

Landslides are an important natural hazard that causes a great amount of damage around the world every year, especially during the rainy season. The Lianhua area is located in the middle of China's southern mountainous area, west of Jiangxi Province, and is known to be an area prone to landslides. The aim of this study was to evaluate and compare landslide susceptibility maps produced using the random forest (RF) data mining technique with those produced by bivariate (evidential belief function and frequency ratio) and multivariate (logistic regression) statistical models for Lianhua County, China. First, a landslide inventory map was prepared using aerial photograph interpretation, satellite images, and extensive field surveys. In total, 163 landslide events were recognized in the study area, with 114 landslides (70%) used for training and 49 landslides (30%) used for validation. Next, the landslide conditioning factors-including the slope angle, altitude, slope aspect, topographic wetness index (TWI), slope-length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, annual precipitation, land use, normalized difference vegetation index (NDVI), and lithology-were derived from the spatial database. Finally, the landslide susceptibility maps of Lianhua County were generated in ArcGIS 10.1 based on the random forest (RF), evidential belief function (EBF), frequency ratio (FR), and logistic regression (LR) approaches and were validated using a receiver operating characteristic (ROC) curve. The ROC plot assessment results showed that for landslide susceptibility maps produced using the EBF, FR, LR, and RF models, the area under the curve (AUC) values were 0.8122, 0.8134, 0.7751, and 0.7172, respectively. Therefore, we can conclude that all four models have an AUC of more than 0.70 and can be used in landslide susceptibility mapping in the study area; meanwhile, the EBF and FR models had the best performance for Lianhua County, China. Thus, the resultant susceptibility maps will be useful for land use planning and hazard mitigation aims. © 2016 Elsevier B.V. Source

Ding Q.,Zhejiang Communications Services Company Ltd Consulting Branch | Chen W.,University of Science and Technology of China | Hong H.,Jiangxi Provincial Meteorological Observatory
Geocarto International | Year: 2016

The landslide hazard occurred in Taibai County has the characteristics of the typical landslides in mountain hinterland. The slopes mainly consist of residual sediments and locate along the highway. Most of them are in the less stable state and in high risk during rainfall in flood season especially. The main purpose of this paper is to produce landslide susceptibility maps for Taibai County (China). In the first stage, a landslide inventory map and the input layers of the landslide conditioning factors were prepared in the geographic information system supported by field investigations and remote sensing data. The landslides conditioning factors considered for the study area were slope angle, altitude, slope aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, normalized difference vegetation index, lithological unit, rainfall and land use. Subsequently, the thematic data layers of conditioning factors were integrated by frequency ratio (FR), weights of evidence (WOE) and evidential belief function (EBF) models. As a result, landslide susceptibility maps were obtained. In order to compare the predictive ability of these three models, a validation procedure was conducted. The curves of cumulative area percentage of ordered index values vs. the cumulative percentage of landslide numbers were plotted and the values of area under the curve (AUC) were calculated. The predictive ability was characterized by the AUC values and it indicates that all these models considered have relatively similar and high accuracies. The success rate of FR, WOE and EBF models was 0.9161, 0.9132 and 0.9129, while the prediction rate of the three models was 0.9061, 0.9052 and 0.9007, respectively. Considering the accuracy and simplicity comprehensively, the FR model is the optimum method. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose. © 2016 Informa UK Limited, trading as Taylor & Francis Group Source

Zhang D.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Zhang D.,University of Chinese Academy of Sciences | Liu X.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Hong H.,Jiangxi Provincial Meteorological Observatory
Stochastic Environmental Research and Risk Assessment | Year: 2013

Reference evapotranspiration (ET 0) is a key parameter in hydrological and meteorological studies. In this study, the FAO Penman-Monteith equation was used to estimate ET 0, and the change in ET 0 was investigated in China from 1960 to 2011. The results show that a change point around the year 1993 was detected for the annual ET 0 series by the Cramer's test. For the national average, annual ET 0 decreased significantly (P < 0.001) by -14.35 mm/decade from 1960 to 1992, while ET 0 increased significantly (P < 0.05) by 22.40 mm/decade from 1993 to 2011. A differential equation method was used to attribute the change in ET 0 to climate variables. The attribution results indicate that ET 0 was most sensitive to change in vapor pressure, followed by solar radiation, air temperature and wind speed. However, the effective impact of change in climate variable on ET 0 was the product of the sensitivity and the change rate of climate variable. During 1960-1992, the decrease in solar radiation was the main reason of the decrease in ET 0 in humid region, while decrease in wind speed was the dominant factor of decreases in ET 0 in arid region and semi-arid/semi-humid region of China. Decrease in solar radiation and/or wind speed offset the effect of increasing air temperature on ET 0, and together led to the decrease in ET 0 from 1960 to 1992. Since 1993, the rapidly increasing air temperature was the dominant factor to the change in ET 0 in all the three regions of China, which led to the increase in ET 0. Furthermore, the future change in ET 0 was calculated under IPCC SRES A1B and B1 scenarios with projections from three GCMs. The results showed that increasing air temperature would dominate the change in ET 0 and ET 0 would increase by 2.13-10.77, 4.42-16.21 and 8.67-21.27 % during 2020s, 2050s and 2080s compared with the average annual ET 0 during 1960-1990, respectively. The increases in ET 0 would lead to the increase in agriculture water consumption in the 21st century and may aggravate the water shortage in China. © 2013 Springer-Verlag Berlin Heidelberg. Source

Zhang D.,CAS Nanjing Institute of Geography and Limnology | Hong H.,Jiangxi Provincial Meteorological Observatory | Zhang Q.,CAS Nanjing Institute of Geography and Limnology | Li X.,CAS Nanjing Institute of Geography and Limnology
Theoretical and Applied Climatology | Year: 2014

Water resources in the Yangtze River, the longest river in China, are of great importance for the water security of China. In this study, 146 years (1865-2010) of streamflow data were used to investigate the changes in streamflow of the Yangtze River. The Mann-Kendall test and wavelet coherence analysis were used to test the change points in annual streamflow. The streamflow data, combined with the meteorological dataset of the Climatic Research Unit (CRU) from 1901 to 2010, showed that the Yangtze River streamflow changes occurred in four major periods over the past century: 1901-1930, 1931-1960, 1961-1990, and 1991-2010. The average annual streamflow for the four periods was 497.26, 499.11, 476.25, and 471.93 mm, respectively. The period from 1901 to 1930 was considered the baseline period for estimating the streamflow changes during the other three periods. We found that the streamflow increase during 1931-1960 was mainly influenced by climatic variation, while the streamflow decrease during 1961-1990 was mainly attributed to human activities because of tremendous population growth and rapid economic development. During the period 1991-2010, both climatic variation and human activities led to a decrease in streamflow, and human activities were still the main driving factor for the streamflow decrease. However, the contribution proportion of human activities to the streamflow decrease during 1991-2010 was much smaller than that during 1961-1990. The estimation results indicated that human activities have become the dominant driving factors of the streamflow changes in the Yangtze River Basin since 1961. Human activities, such as booming socio-economic development and fast population growth, have brought new challenges for water resources management in the Yangtze River Basin. © 2014 Springer-Verlag Wien. Source

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