Novel Early-Warning Model for Customer Churn of Credit Card Based on GSAIBAS-Cat Boost

As the banking industry gradually steps into the digital era of Bank 4.0,business competition is becoming increasingly fierce,and banks are also facing the problem of massive customer churn.To better maintain their customer resources,it is crucial for banks to accurately predict customers with a tendency to churn.Aiming at the typical binary classification problem like customer churn,this paper establishes an early-warning model for credit card customer churn.That is a dual search algorithm named GSAIBAS by incorporating Golden Sine Algorithm(GSA)and an Improved Beetle Antennae Search(IBAS)is proposed to optimize the parameters of the CatBoost algorithm,which forms the GSAIBAS-CatBoost model.Especially,considering that the BAS algorithm has simple parameters and is easy to fall into local optimum,the Sigmoid nonlinear convergence factor and the lane flight equation are introduced to adjust the fixed step size of beetle.Then this improved BAS algorithm with variable step size is fused with the GSA to form a GSAIBAS algorithm which can achieve dual optimization.Moreover,an empirical analysis is made according to the data set of credit card customers from Analyttica official platform.The empirical results show that the values of Area Under Curve(AUC)and recall of the proposedmodel in this paper reach 96.15%and 95.56%,respectively,which are significantly better than the other 9 common machine learning models.Compared with several existing optimization algorithms,GSAIBAS algorithm has higher precision in the parameter optimization for CatBoost.Combined with two other customer churn data sets on Kaggle data platform,it is further verified that the model proposed in this paper is also valid and feasible.

China agricultural outlook for 2015–2024 based on China Agricultural Monitoring and Early-warning System(CAMES)

The primary goal of Chinese agricultural development is to guarantee national food security and supply of major agricultural products. Hence, the scientiifc work on agricultural monitoring and early warning as wel as agricultural outlook must be strengthened. In this study, we develop the China Agricultural Monitoring and Early-warning System (CAMES) on the basis of a comparative study of domestic and international agricultural outlook models. The system is a dynamic and multi-market partial equilibrium model that integrates biological mechanisms with economic mechanisms. This system, which includes 11 categories of 953 kinds of agricultural products, could dynamical y project agricultural market supply and demand, assess food security, and conduct scenario analysis at different spatial levels, time scale levels, and macro-micro levels. Based on the CAMES, the production, consumption, and trade of the major agricultural products in China over the next decade are projected. The fol owing conclusions are drawn:i) The production of major agricultural products wil continue to grow steadily, mainly because of the increase in yield. i ) The growth of agricultural consumption wil be slightly higher than that of agricultural production. Meanwhile, a high self-sufifciency rate is expected for cereals such as rice, wheat, and maize, with the rate being stable at around 97%. i i) Agricultural trade wil continue to thrive. The growth of soybean and milk im-ports wil slow down, but the growth of traditional agricultural exports such as vegetables and fruits is expected to continue.

Deformation early-warning index for heightened gravity dam during impoundment period

The mechanical parameters of materials in a dam body and dam foundation tend to change when dams are reinforced in aging processes.It is important to use an early-warning index to reflect the safety status of dams,particularly of heightened projects in the impoundment period.Herein,a new method for monitoring the safety status of heightened dams is proposed based on the deformation monitoring data of a dam structure,a statistical model,and finite-element numerical simulation.First,a fast optimization inversion method for estimation of dam mechanical parameters was developed,which used the water pressure component extracted from a statistical model,an improved inversion objective function,and a genetic optimization iterative algorithm.Then,a finite element model of a heightened concrete gravity dam was established,and the deformation behavior of the dam with rising water levels in the impoundment period was simulated.Subsequently,mechanical parameters of aged dam parts were calculated using the fast optimization inversion method with simulated deformation and the water pressure deformation component obtained by the statistical model under the same conditions of water pressure change.Finally,a new earlywarning index of dam deformation was constructed by means of the forward-simulated deformation and other components of the statistical model.The early-warning index is useful for forecasting dam deformation under different water levels,especially high water levels.

Risk Early-Warning Method for Natural Disasters Based on Integration of Entropy and DEA Model

Risk early-warning of natural disasters is a very intricate non-deterministic prediction, and it was difficult to resolve the conflicts and incompatibility of the risk structure. Risk early-warning factors of natural disasters were differentiated into essential attributes and external characters, and its workflow mode was established on risk early-warning structure with integrated Entropy and DEA model, whose steps were put forward. On the basis of standard risk early-warning DEA model of natural disasters, weight coefficient of risk early-warning factors was determined with Information Entropy method, which improved standard risk early-warning DEA model with non-Archimedean infinitesimal, and established risk early-warning preference DEA model based on integrated entropy weight and DEA Model. Finally, model was applied into landslide risk early-warning case in earthquake-damaged emergency process on slope engineering, which exemplified the outcome could reflect more risk information than the method of standard DEA model, and reflected the rationality, feasibility, and impersonality, revealing its better ability on comprehensive safety and structure risk.

The Innovation Research of Financial Early-Warning Index Measurement

The period economic fluctuation is vital for an enterprise to exist and further develop, it directly affect the enterprise financial health. So, it is significant to build up financial early-warning index and measure the warning condition that the enterprise faces and take the effective measures to eliminate. We criticize Altman’sZ calculating model and build up some new indexes for enterprise financial early-warning condition measuring and making sound decision.

Early-warning signals for an outbreak of the influenza pandemic

Over the course of human history, influenza pandemics have been seen as major disasters, so studies on the influenza virus have become an important issue for many experts and scholars. Comprehensive research has been performed over the years on the biological properties, chemical characteristics, external environmental factors and other aspects of the virus, and some results have been achieved. Based on the chaos game representation walk model, this paper uses the time series analysis method to study the DNA sequences of the influenza virus from 1913 to 2010, and works out the early-warning signals indicator value for the outbreak of an influenza pandemic. The variances in the CCR wall〈 sequences for the pandemic years （or ＋ -1 to 2 years） are significantly higher than those for the adjacent years, while those in the non-pandemic years are usually smaller. In this way we can provide an influenza early-warning mechanism so that people can take precautions and be well prepared prior to a pandemic.

A new early-warning prediction system for monitoring shear force of fault plane in the active fault

The most common method used to describe earthquake activity is based on the changes in physical parameters of the earth＇s surface such as displacement of active fault and seismic wave.However,such approach is not successful in forecasting the movement behaviors of faults.In the present study,a new mechanical model of fault activity,considering the shear strength on the fault plane and the influence of the resistance force,is established based on the occurrence condition of earthquake.A remote real-time monitoring system is correspondingly developed to obtain the changes in mechanical components within fault.Taking into consideration the local geological conditions and the history of fault activity in Zhangjiakou of China,an active fault exposed in the region of Zhangjiakou is selected to be directly monitored by the real-time monitoring technique.A thorough investigation on local fault structures results in the selection of two suitable sites for monitoring potential active tectonic movements of Zhangjiakou fault.Two monitoring curves of shear strength,recorded during a monitoring period of 6 months,turn out to be steady,which indicates that the potential seismic activities hardly occur in the adjacent region in the near future.This monitoring technique can be used for early-warning prediction of the movement of active fault,and can help to further gain an insight into the interaction between fault activity and relevant mechanisms.

A study on the early-warning technique concerning debris flow disasters

According to the principle of the eruption of debris flows, the new torrent classification techniques are brought forward. The torrent there can be divided into 4 types such as the debris flow torrent with high destructive strength, the debris flow torrent, high sand-carrying capacity flush flood torrent and common flush flood by the techniques. In this paper, the classification indices system and the quantitative rating methods are presented. Based on torrent classification, debris flow torrent hazard zone mapping techniques by which the debris flow disaster early-warning object can be ascertained accurately are identified. The key techniques of building the debris flow disaster neural network (NN) real time forecasting model are given detailed explanations in this paper, including the determination of neural node at the input layer, the output layer and the implicit layer, the construction of knowledge source and the initial weight value and so on. With this technique, the debris flow disaster real-time forecasting neural network model is built according to the rainfall features of the historical debris flow disasters, which includes multiple rain factors such as rainfall of the disaster day, the rainfall of 15 days before the disaster day, the maximal rate of rainfall in one hour and ten minutes. It can forecast the probability, critical rainfall of eruption of the debris flows, through the real-time rainfall monitoring or weather forecasting. Based on the torrent classification and hazard zone mapping, combined with rainfall monitoring in the rainy season and real-time forecasting models, the debris flow disaster early-warning system is built. In this system, the GIS technique, the advanced international software and hardware are applied, which makes the system’s performance steady with good expansibility. The system is a visual information system that serves management and decision-making, which can facilitate timely inspect of the variation of the torrent type and hazardous zone, the torrent management, the early-warning of disasters and the disaster reduction and prevention.

The Management Platform for Online Rate of Meteorological Early-warning Loudspeakers

The meteorological early-warning loudspeaker is a specific initiative for the meteorological departments to address the issues concerning issues concerning agriculture,countryside and farmers.Its significance is that it can promptly deliver the early-warning information concerning some meteorological disasters(such as torrential rains,typhoons,cold wave,hail)to the areas affected,so as to provide reference and protection for agricultural production and effectively reduce the loss of agricultural producers.Up to now,the meteorological early-warning loudspeakers in Benxi have covered the villages.However,due to irregular occurrence of meteorological disasters,the listeners will turn off the information receivers of meteorological early-warning loudspeakers when they fail to receive meteorological information for a long time,so that the users can not promptly know the early-warning information regarding some sudden meteorological disasters.In view of this,the meteorological departments have introduced a series of management measures,such as the daily use of loudspeakers to publish weather forecast information,aimed at improving the online rate and usage rate of meteorological loudspeakers.And the management platform for online rate of meteorological early-warning loudspeakers is an important part of the management system.

Research on Technology Early-Warning System Based on Dynamic Information Monitoring

Relying on the advanced information technologies, such as information monitoring, data mining, natural language processing etc., the dynamic technology early-warning system is constructed. The system consists of technology information automatic retrieval, technology information monitoring, technology threat evaluation, and crisis response and management subsystem, which implements uninterrupted dynamic monitoring, trace and crisis early-warning to the specific technology. Empirical study testifies that the system improves the accuracy, timeliness and reliability of technology early-warning.

Financial crisis early-warning model of listed companies based on predicted value

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.

Studies on Early-warning System for Social Stability: Vital to China

As a large country in the world, it is both necessary and possible to develop and operate a pre-warning system for social stability, says Prof. Niu Wenyuan, chief scientist for strate-

Evolution of coordination degree of eco-economic system and early-warning in the Yangtze River Delta

On the basis of Landsat TM data of the Yangtze River Delta （YRD） Economic Zone in 1991, 2001 and 2008, this article, taking 90 counties in this region as study units, built spatial data transformation models, ecosystem service value （ESV） and coordination degree of eco-economic system （CDES） models. With the aid of ArcGIS9.3, mass grid and vector data has been processed for spatial analyses. ESV and CDES indexes have demonstrated the relationship between economic development and eco-environment system and its evolu- tion characteristics in the researched areas. Furthermore, the indexes have also been used for functional zoning and pattern recognition. Some results can be shown as follows. Firstly, since 1991, land use in the YRD has greatly changed： urban land area has increased primar- ily from original paddy land, dry land, grassland, garden plot and other land. Secondly, the ESV model has proved the deterioration trend of the YRD ecological system from 1991 to 2001 and slower degradation trend during 2001-2008. Also, it is illustrated that land-use conversion from water area and paddy field to urban area and dry land could cause great damage to ecosystem stabilization. Thirdly, GDP in the central and southern parts of the YRD is higher than that in the northern part since 1991. GDP growth rate in the central part is higher than that in the northern part during 1991-2001. This growth rate in the central part is also higher than that in the southern and northern parts of the YRD from 2001 to 2008. Fourthly, the YRD could be categorized into 12 types of subregions in terms of CDES index. According to its spatial characteristic of CDES index value in the study area, eco-economic conflict area with low CDES value which is located in the central part is surrounded by eco-coordinated areas with high CDES values. This illustrates a core-periphery spatial structure exists in the YRD. During 1991-2001, the CDES value implied the convergent de- terioration trend of eco-economic system in the study area; while it gradually stepped into coexistence of divergent deterioration and coordination during 2001-2008. Finally, this paper analyzed five subregions in the YRD, including initially degrading zone, initially coordinative zone, continuously degrading zone, coordination-declined.zone and coordination-promoted zone, based on eco-economic coordination and evolution patterns. And these subregions can be recognized and categorized by spatial transformation model.

Analysis of Early-Warning Threshold for Metro Construction Collapse Risk Based on D-S Evidence Theory and Rough Set

The existing early-warning system in metro construction are generally based on traditional single-sensor data and simple analytic model, which makes it difficult to deal with the complex and comprehensive environment in metro construction. In this paper, the framework of early-warning threshold for metro construction collapse risk based on D-S evidence theory and rough set is built. By combining the primary data fusion collected based on rough set with the secondary data fusion which is based on D-S evidence theory, the integration of multiple information in metro construction is realized and the risk assessment methods are optimized. A case trial based on Hangzhou metro construction collapse accident is also carried out to exemplify the framework. The empirical analysis guarantees the completeness and independence of the prediction information, and realizes the dynamic prediction of the variation trend of metro construction collapse risk.

Research on the Method of Shipping Risk Early-Warning Based on Matter-Element Theory

The purpose of shipping risk early-warning is that some effective measures are taken to reduce risk probability before the risk brings heavy loss.The shipping risk has the dynamic characteristic,so the key of early-warning is to choice risk early-warning index correctly and evaluate risk grade quantitatively.According to the element extension theory,the rhombus inference model is applied to establish the index system.And the problem of risk grade evaluation can be solved by the assessment model of multi-index performance parameter,which is developed by the extension engineering method.Finally,the main shipping risks and their grades are identified by the example analysis based on the statistical data,which shows the effective and feasible of the shipping risk early-warning method.

Criticality, adaptability and early-warning signals in time series in a discrete quasispecies model

Complex systems from different fields of knowledge often do not allow a mathematical description or modeling, because of their intricate structure composed of numerous interacting components. As an alternative approach, it is possible to study the way in which observables associated with the system fluctuate in time. These time series may provide valuable information about the underlying dynamics. It has been suggested that complex dynamic systems, ranging from ecosystems to financial markets and the climate, produce generic early-warning signals at the ＂tipping points,＂ where they announce a sudden shift toward a different dynamical regime, such as a population extinction, a systemic market crash, or abrupt shifts in the weather. On the other hand, the framework of Self- Organized Criticality （SOC）, suggests that some complex systems, such as life itself, may spontaneously converge toward a critical point. As a particular example, the quasispecies model suggests that RNA viruses self-organize their mutation rate near the error-catastrophe threshold, where robustness and evolvability are balanced in such a way that survival is optimized. In this paper, we study the time series associated to a classical discrete quasispecies model for different mutation rates, and identify early-warning signals for critical mutation rates near the error-catastrophe threshold, such as irregularities in the kurtosis and a significant increase in the autocorrelation range, reminiscent of 1/f noise. In the present context, we find that the early-warning signals, rather than broadcasting the collapse of the system, are the fingerprint of survival optimization.

Key technologies of earthquake early warning system for China’s high-speed railway

Purpose–The purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China,which is based on P-wave earthquake early-warning and multiple ways of rapid treatment.Design/methodology/approach–The paper describes the key technologies that are involved in the development of the system,such as P-wave identification and earthquake early-warning,multi-source seismic information fusion and earthquake emergency treatment technologies.The paper also presents the test results of the system,which show that it has complete functions and its major performance indicators meet the design requirements.Findings–The study demonstrates that the high speed railways earthquake early-warning system serves as an important technical tool for high speed railways to cope with the threat of earthquake to the operation safety.The key technical indicators of the system have excellent performance:The first report time of the P-wave is less than three seconds.From the first arrival of P-wave to the beginning of train braking,the total delay of onboard emergency treatment is 3.63 seconds under 95%probability.The average total delay for power failures triggered by substations is 3.3 seconds.Originality/value–The paper provides a valuable reference for the research and development of earthquake early-warning system for high speed railways in other countries and regions.It also contributes to the earthquake prevention and disaster reduction efforts.

Spread of invasive migratory pest Spodoptera frugiperda and management practices throughout China

The fall armyworm(FAW),Spodoptera frugiperda(J.E.Smith)(Lepidoptera:Noctuidae),a notorious migratory pest native to tropical and subtropical America,invaded China in December 2018,then spread through 26 provinces(autonomous regions,municipalities)in 2019 and 27 in 2020,damaging 1.125 and 1.278 million hectares of crops,respectively.Maize was the most severely affected crop,although wheat and other plants were also ruined.Considering the biological characteristics,incidence regularity and migration patterns of the FAW populations,Chinese government implemented a regional control strategy and divided the areas infested with FAW into the annual breeding grounds in Southwest and South China,the transitional migration area in Jiangnan and Jianghuai and the key preventive area in the Huang-Huai-Hai region and North China.The National Agro-Tech Extension and Service Center constructed"the National Information Platform for the Prevention and Control of the Fall Armyworm"at the county level,which would entail people reporting and mapping the spread of fall armyworm.According to forecasting information,millions of extension workers and small-scale growers in entire country were rallied by local governments to fight the pest through comprehensive control tactics including chemical,physical,biological and ecological measures.Thanks to the joint prevention and control,the final loss of crops infested was controlled within 5%of the total in 2019 and 2020.This review also gives a discussion on existing problems and future management scenarios.

Design of an agricultural economic statistical digitalized system

To improve the efficiency of the management of large farms, a digitalized system of economic statistics is designed based on the Internet platform of the digitalized agricultural integrated system of Friendship Farm, the largest farm in the world. The system can also realize data storage by using the access databank technology. A dynamic website system, based on ASP technology, is used to implement the on-line inquiry of the statistical index of the agricultural economy and the diagrams of the index every year. Furthermore, it can provide the value of comprehensive indicators of farms' economic profits for every year and a trend chart of the comprehensive appraisal of economic development, by using principal component analysis. An early-warning indicator boundary is decided based on the majority principle. The system can realize the farm's terminal data input with effective data-collecting channels and a normative gathering scope and system. This system breaks through the stand-alone database system in agricultural digitalized research to realize the database system in the Internet environment by integrating the existing technologies in China. The system lays a foundation for the further integrated research on the network platform of the digitalized agricultural integrated system in Friendship Farm.