Institution
Guizhou University of Finance and Economics
Education•Guiyang, China•
About: Guizhou University of Finance and Economics is a education organization based out in Guiyang, China. It is known for research contribution in the topics: Hopf bifurcation & Exponential stability. The organization has 791 authors who have published 885 publications receiving 5668 citations.
Topics: Hopf bifurcation, Exponential stability, Population, Artificial neural network, Computer science
Papers published on a yearly basis
Papers
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TL;DR: A new hybrid algorithm based on grey wolf optimizer and cuckoo search (GWOCS) is developed to extract the parameters of different PV cell models with the experimental data under different operating conditions and comprehensively experimental results show the GWOCS is a promising candidate approach.
198 citations
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TL;DR: The comparisons show that the proposed EEGWO algorithm significantly improves the performance of GWO and offers the highest solution quality, strongest robustness, and fastest global convergence among all of the contenders on almost all ofThe test functions.
186 citations
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TL;DR: In this article, the authors improved the logarithmic mean divisia index technique, which includes energy density and energy consumption intensity, to explore the driving factors of carbon emission intensity (CI) in 29 Chinese provinces from 1995-2012.
166 citations
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TL;DR: The findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform in an efficient manner.
Abstract: This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner.
161 citations
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TL;DR: Overall, the present study quantitatively evaluates the spatio-temporal variations in nitrate sources in a subtropical watershed, and the high-frequency monitoring gives a better estimate of nitrate exports and proportional contributions of nitrates sources.
156 citations
Authors
Showing all 811 results
Name | H-index | Papers | Citations |
---|---|---|---|
Xiaofeng Yang | 79 | 551 | 28055 |
Tom Christensen | 44 | 226 | 8847 |
B. G. Zakharov | 37 | 239 | 6870 |
Shan Liu | 29 | 94 | 2891 |
Zhongfei Li | 26 | 97 | 2029 |
Sergey V. Meleshko | 20 | 150 | 1603 |
Changjin Xu | 16 | 70 | 747 |
Tian Sang | 16 | 60 | 989 |
Wen Long | 15 | 31 | 730 |
Mingsen Deng | 14 | 46 | 951 |
Tai-Chee Wong | 11 | 21 | 340 |
Rui Yang | 10 | 11 | 282 |
Bo Chen | 10 | 13 | 238 |
Jianjun Jiao | 10 | 13 | 434 |
Long Liang | 10 | 14 | 300 |