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Droughts are increasing in frequency and intensity as the global climate changes. Risk assessment for agricultural drought disasters is an important approach for disaster prevention and mitigation. Based on the formation mechanism of natural disaster risk, the index system model of agricultural drought risk assessment in North China was constructed, and the robustness of the established risk assessment model was tested by using the internal test method. Then based on the characteristics of agricultural drought risk formation in North China, the paper proposes corresponding risk prevention recommendations. The conclusions were as follows: when adjusting the indices and methods in the risk assessment model, the range of changes in the agricultural drought risk ranking for each city was mostly 1-2.5 places, and the model was relatively robust. With the exception of Beijing and Tianjin, which had the highest disaster prevention and mitigation capacity, central and northern China had the highest agricultural drought risk, which was previously linked to the high risk and vulnerability of these regions. The agricultural drought risk in northern Henan and southern Shandong is low, mainly due to the low risk. The 34 cities in North China can be divided into 6 agricultural drought risk clusters, which are not dispersed in space and cross-city distribution, and have good aggregation. Cluster 4, located in the northernmost mountainous areas, has the highest risk of drought, which is associated with high environmental vulnerability and low capacity for disaster prevention and mitigation, so agricultural areas should be reduced and agricultural input should be increased. Clusters 2, 5 and 6, located in the central and southern regions, have higher grain acreage and yields due to drought, so agricultural drought risk can be further reduced by increasing agricultural input and improving irrigation guarantee rate. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Wildland Fire Science is the property of Journal of Wildland Fire Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Fly Ash Comprehensive Utilization is the property of Hebei Fly Ash Comprehensive Utilization Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Agrometeorology is the property of Editorial Board of Chinese Journal of Agrometeorology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Water Conservancy Science & Techonlogy & Economy is the property of Water Conservancy Science & Technology & Economy Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Volcanic hazard evaluation and zonation, as the effective countermeasures, plays an important role in volcanic emergency response and hazard mitigation. Taking Changbaishang volcano as an example, this paper reviews on the study history of volcanic hazard zonation in China. The major types of volcanic hazards in China, which are potentially related with the active volcanoes, involve tephra fallout, pyroclastic flow, lava flow and lahar. The volcanic zoning maps of Changbaishan volcano compiled in 2000 and 2012 respectively show the progresses in volcanic hazard assessment and hazard zonation of China. It is proposed that the probabilistic method such as event tree should be applied in compiling the volcanic hazard map of China in future, which represents the most-up-to-date technical trend in volcanic hazard zonation in the world. [ABSTRACT FROM AUTHOR]

Objective: Large-scale human engineering activities induce and aggravate the landslide disaster, which seriously threatens the engineering safety and environmental safety. Landslide susceptibility assessment is the key technique of landslide monitoring and early warning. In view of the limited data sources of traditional landslide monitoring Methods: and the lack of effective Methods: for mining the spatial distribution of landslide hazards and their inducing factors, the main Objective: of this study was to investigate the prediction performances of synthetic minority oversampling technique (SMOTE) and convolutional neural networks (CNN) for landslide susceptibility assessment in the Wanzhou area of the Three Gorges, China. Methods:: The landslide susceptibility assessment Methods: used in this paper are SMOTE and CNN. SMOTE is an improved random oversampling algorithm, which was used to better the imbalance of input samples. CNN is a widely used deep learning network and differs from other neural networks mainly in convolution operation. CNN was used to analyze the nonlinear relationship between landslides and its influencing factors. Firstly, 22 influencing factors were extracted from multi-source data such as topographic, geological, and remote sensing data, and multicollinearity test was performed on these factors. Then, the grid unit with a spatial resolution of 25 m was selected as the mapping unit. 30% of the initial sample dataset was selected randomly for testing of the CNN, and the remaining 70% was expanded by using SMOTE to obtain 1:1 landslide and non-landslide dataset. 90% of the proportionally balanced dataset was randomly selected as the training set and the remaining 10% as the validation set. Thirdly, the obtained training sam - ple set is input to the CNN and six optimal model parameters were set to ensure the stability and efficiency of the model. Finally, the trained CNN was used to generate landslide susceptibility zoning map, and the receiver operating characteristic curves, testing and validating set were used for evaluating the accuracy and Results of the proposed model.Results The proposed SMOTE-CNN model with the highest predictive accuracy and generalization ability was trained and then used to calculate the landslide susceptibility indices. The index values of the grid cells vary from zero to one and correspond to landslide susceptibilities from low to high. The landslide susceptibility maps were created using these values. For easy visual interpretation, the susceptibility values were classified into five classes (very low,low,moderate,high,and very high) using Jenks natural breaks. Approximately, 59.57 % of the landslides lie in the very-high-susceptibility region, 18.74% of the landslides lie in the high-susceptibility region, 10.73% of the landslides lie in the moderatesusceptibility region, and 10.96 % of the landslides lie in the low- and very-low-susceptibility regions. The experimental Results demonstrate that the proposed model provides the best predictive accuracy. The model can effectively assess landslide susceptibility and provides a novel method for landslide prediction.Conclusions: The SMOTE and CNN were applied to quantitatively mapping the landslide susceptibility in Wanzhou area of the Three Gorges, China. The experimental Results: show that SMOTE and CNN have good stability, and the Results were consistent with field investigations and can provide a reference for landslide prevention and reduction in the Three Gorge, China. In addition, CNN has the characteristics of strong adaptability and is good at mining local features of data and extracting global training features and classification. However, the physical meaning of CNN model is not clear, and it takes a long time to process a large amount of data, so it relies on GPU acceleration. Therefore, it is necessary to carry out further research from the aspects of super-parameter optimization combination, model structure adjustment, applicability analysis etc., so as to improve the accuracy and robustness of the model. [ABSTRACT FROM AUTHOR]

Using Data from 2010 China General Social Survey, this paper presents a comparative analysis on the urban-rural differences in environmental concern in mainland China. Firstly, it could be concluded from the literature review that the Residence Hypothesis-that urban residents are more environmentally concerned than the rural-had once been extensively supported in the western countries, and there are mainly three theoretical explanations for this hypothesis: differential-exposure theory, differential-occupation theory, and differential-experience theory. Secondly, the measurement model for Chinese residents' environmental concern consists of five facets-New Ecological Paradigm (NEP), the degree of attention paid to environmental issues, the assessment of environmental hazards, the willingness to pay for environmental protection, and daily environmental behaviors- and 21 indicators in total; the results of second-order confirmatory factor analysis indicate that both urban and rural residents share a coherent belief system in perceiving environmental issues. Thirdly, the results of ANOVA analysis indicate that there are indeed significant differences in the level of urban-rural residentsf environmental concern, with urban residents show more environmental concern in many aspects. Fourthly,the results of Structural Equation Modeling indicate that only differential-exposure theory can explain a part of urban-rural differences while the other two are not supported by data; besides, environmental knowledge and media use display significant mediating effects on the formation of the gap between the urban and the rural residents. Last but not the least, it is inappropriate or limited to apply the theories rooted in western societies to explain the urban-rural differences in mainland China; furthermore, judging from both the existing studies and the variation trend in the long term, the urban-rural differences in environmental concern are definitely being convergent and will finally go to the isomorphism. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Natural Resources / Ziran Ziyuan Xuebao is the property of Journal of Natural Resources and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)