To establish a financial early-warning model with high accuracy of discrimination and achieve the aim of long-term prediction, principal component analysis (PCA), Fisher discriminant, together with grey forecasting mo...To establish a financial early-warning model with high accuracy of discrimination and achieve the aim of long-term prediction, principal component analysis (PCA), Fisher discriminant, together with grey forecasting models are used at the same time. 110 A-share companies listed on the Shanghai and Shenzhen stock exchange are selected as research samples. And 10 extractive factors with 89.746% of all the original information are determined by applying PCA, which obtains the goal of dimension reduction without information loss. Based on the index system, the early-warning model is constructed according to the Fisher rules. And then the GM(1,1) is adopted to predict financial ratios in 2004, according to 40 testing samples from 2000 to 2003. Finally, two different methods, a self-validated and a forecasting-validated, are used to test the validity of the financial crisis warning model. The empirical results show that the model has better predictability and feasibility, and GM(1,1) contributes to the ability to make long-term predictions.展开更多
The exotic saltmarsh cordgrass,Spartina alterniflora(Loisel)Peterson&Saarela,is one of the important causes for the extensive destruction of mangroves in China due to its invasive nature.The species has rapidly sp...The exotic saltmarsh cordgrass,Spartina alterniflora(Loisel)Peterson&Saarela,is one of the important causes for the extensive destruction of mangroves in China due to its invasive nature.The species has rapidly spread wildly across coastal wetlands,challenging resource managers for control of its further spread.An investigation of S.alterniflora invasion and associated ecological risk is urgent in China's coastal wetlands.In this study,an ecological risk invasive index system was developed based on the Driving Force-Pressure-State-Impact-Response framework.Predictions were made of'warning degrees':zero warning and light,moderate,strong,and extreme warning,by developing a back propagation(BP)artificial neural network model for coastal wetlands in eastern Fujian Province.Our results suggest that S.alterniflora mainly has invaded Kandelia candel beaches and farmlands with clustered distributions.An early warning indicator system assessed the ecological risk of the invasion and showed a ladder-like distribution from high to low extending from the urban area in the central inland region with changes spread to adjacent areas.Areas of light warning and extreme warning accounted for43%and 7%,respectively,suggesting the BP neural network model is reliable prediction of the ecological risk of S.alterniflora invasion.The model predicts that distribution pattern of this invasive species will change little in the next 10 years.However,the invaded patches will become relatively more concentrated without warning predicted.We suggest that human factors such as land use activities may partially determine changes in warning degree.Our results emphasize that an early warning system for S.alterniflora invasion in China's eastern coastal wetlands is significant,and comprehensive control measures are needed,particularly for K.candel beach.展开更多
According to the index early warning method, a commercial bank loans risk early warning system based on BP neural networks is proposed. The warning signal is mainly involved with the financial situation signal of loan...According to the index early warning method, a commercial bank loans risk early warning system based on BP neural networks is proposed. The warning signal is mainly involved with the financial situation signal of loaning corporation. Except the structure description of the system structure the demonstration of attemptive designing is also elaborated.展开更多
降雨引发斜坡破坏的阈值是地质灾害预警的基础。文章以2004—2019年白龙江流域甘肃段5个县区内因长期强降雨引发的滑坡作为研究对象,采用频数法研究不同岩性特征的滑坡降雨预警阈值。构建了不同概率等级下,引发滑坡的事件降雨量(event r...降雨引发斜坡破坏的阈值是地质灾害预警的基础。文章以2004—2019年白龙江流域甘肃段5个县区内因长期强降雨引发的滑坡作为研究对象,采用频数法研究不同岩性特征的滑坡降雨预警阈值。构建了不同概率等级下,引发滑坡的事件降雨量(event rainfall)与降雨历时(duration of rainfall)之间的关系模型,并给出了下限临界累计降雨阈值。通过2020年陇南武都区暴洪灾害引发的滑坡特征及降雨数据验证,滑坡前雨量计监测获得的累计降雨量与模型给出的临界累计降雨阈值基本相符,对持续强降雨引发的滑坡灾害的预警具有指导意义。展开更多
文摘To establish a financial early-warning model with high accuracy of discrimination and achieve the aim of long-term prediction, principal component analysis (PCA), Fisher discriminant, together with grey forecasting models are used at the same time. 110 A-share companies listed on the Shanghai and Shenzhen stock exchange are selected as research samples. And 10 extractive factors with 89.746% of all the original information are determined by applying PCA, which obtains the goal of dimension reduction without information loss. Based on the index system, the early-warning model is constructed according to the Fisher rules. And then the GM(1,1) is adopted to predict financial ratios in 2004, according to 40 testing samples from 2000 to 2003. Finally, two different methods, a self-validated and a forecasting-validated, are used to test the validity of the financial crisis warning model. The empirical results show that the model has better predictability and feasibility, and GM(1,1) contributes to the ability to make long-term predictions.
基金funded by Forestry Peak Discipline Construction Project of Fujian Agriculture and Forestry University (72202200205)Fujian Province Natural Science (2022J01575)Science and Technology Innovation Project of Fujian Agriculture and Forestry University (KFA20036A)。
文摘The exotic saltmarsh cordgrass,Spartina alterniflora(Loisel)Peterson&Saarela,is one of the important causes for the extensive destruction of mangroves in China due to its invasive nature.The species has rapidly spread wildly across coastal wetlands,challenging resource managers for control of its further spread.An investigation of S.alterniflora invasion and associated ecological risk is urgent in China's coastal wetlands.In this study,an ecological risk invasive index system was developed based on the Driving Force-Pressure-State-Impact-Response framework.Predictions were made of'warning degrees':zero warning and light,moderate,strong,and extreme warning,by developing a back propagation(BP)artificial neural network model for coastal wetlands in eastern Fujian Province.Our results suggest that S.alterniflora mainly has invaded Kandelia candel beaches and farmlands with clustered distributions.An early warning indicator system assessed the ecological risk of the invasion and showed a ladder-like distribution from high to low extending from the urban area in the central inland region with changes spread to adjacent areas.Areas of light warning and extreme warning accounted for43%and 7%,respectively,suggesting the BP neural network model is reliable prediction of the ecological risk of S.alterniflora invasion.The model predicts that distribution pattern of this invasive species will change little in the next 10 years.However,the invaded patches will become relatively more concentrated without warning predicted.We suggest that human factors such as land use activities may partially determine changes in warning degree.Our results emphasize that an early warning system for S.alterniflora invasion in China's eastern coastal wetlands is significant,and comprehensive control measures are needed,particularly for K.candel beach.
基金Supported by the National Science Foundation of China(Approved NO.79770086)
文摘According to the index early warning method, a commercial bank loans risk early warning system based on BP neural networks is proposed. The warning signal is mainly involved with the financial situation signal of loaning corporation. Except the structure description of the system structure the demonstration of attemptive designing is also elaborated.
文摘降雨引发斜坡破坏的阈值是地质灾害预警的基础。文章以2004—2019年白龙江流域甘肃段5个县区内因长期强降雨引发的滑坡作为研究对象,采用频数法研究不同岩性特征的滑坡降雨预警阈值。构建了不同概率等级下,引发滑坡的事件降雨量(event rainfall)与降雨历时(duration of rainfall)之间的关系模型,并给出了下限临界累计降雨阈值。通过2020年陇南武都区暴洪灾害引发的滑坡特征及降雨数据验证,滑坡前雨量计监测获得的累计降雨量与模型给出的临界累计降雨阈值基本相符,对持续强降雨引发的滑坡灾害的预警具有指导意义。
文摘随着中国金融市场的高水平开放,中国应对外部输入性风险的压力将进一步上升。探索中国金融市场所面临的输入性风险动态变化并构建预警体系具有重要意义。本文运用时变参数向量自回归模型(TVP-VAR)和深度神经网络模型SCInet(Sample Convolution and Interaction Network),对我国金融市场输入性风险进行测度和前瞻性预警。研究发现:(1)TVP-VAR模型能有效识别极端风险事件发生前的风险积累,极端风险事件时期输入性风险水平会显著提高;(2)通过与主要发达国家(或地区)和发展中国家的输入性风险对比,发现发达经济体的输入性风险波动幅度较小,通过研究各国(地区)对我国的输入性风险,发现香港地区对我国内地的风险输入水平最高,以美国为主的发达国家和以印度为主的发展中国家也向我国输送了大量风险;(3)相比于其他机器学习和神经网络模型,SCInet模型具有最优的预警性能,在输入性风险异常波动前能提前预警。本研究或可为个人规避风险、企业可持续发展、国家金融稳定提供参考和帮助。
