Artificial neural network models predicting the leaf area index:a case study in pure even-aged Crimean pine forests from Turkey

Background: Leaf Area Index(LAI) is an important parameter used in monitoring and modeling of forest ecosystems. The aim of this study was to evaluate performance of the artificial neural network(ANN) models to predict the LAI by comparing the regression analysis models as the classical method in these pure and even-aged Crimean pine forest stands.Methods: One hundred eight temporary sample plots were collected from Crimean pine forest stands to estimate stand parameters. Each sample plot was imaged with hemispherical photographs to detect the LAI. The partial correlation analysis was used to assess the relationships between the stand LAI values and stand parameters, and the multivariate linear regression analysis was used to predict the LAI from stand parameters. Different artificial neural network models comprising different number of neuron and transfer functions were trained and used to predict the LAI of forest stands.Results: The correlation coefficients between LAI and stand parameters(stand number of trees, basal area, the quadratic mean diameter, stand density and stand age) were significant at the level of 0.01. The stand age, number of trees, site index, and basal area were independent parameters in the most successful regression model predicted LAI values using stand parameters(R_(adj)~2=0.5431). As corresponding method to predict the interactions between the stand LAI values and stand parameters, the neural network architecture based on the RBF 4-19-1 with Gaussian activation function in hidden layer and the identity activation function in output layer performed better in predicting LAI(SSE(12.1040), MSE(0.1223), RMSE(0.3497), AIC(0.1040), BIC(-77.7310) and R^2(0.6392)) compared to the other studied techniques.Conclusion: The ANN outperformed the multivariate regression techniques in predicting LAI from stand parameters. The ANN models, developed in this study, may aid in making forest management planning in study forest stands.

Some Convergence Properties for Weighted Sums of Martingale Difference Random Vectors

Let{X_(ni),F_(ni);1≤i≤n,n≥1}be an array of R^(d)martingale difference random vectors and{A_(ni),1≤i≤n,n≥1}be an array of m×d matrices of real numbers.In this paper,the Marcinkiewicz-Zygmund type weak law of large numbers for maximal weighted sums of martingale difference random vectors is obtained with not necessarily finite p-th(1<p<2)moments.Moreover,the complete convergence and strong law of large numbers are established under some mild conditions.An application to multivariate simple linear regression model is also provided.

THE APPLICATION OF COBB-DOUGLAS FUNCTION IN FORECASTING THE DURATION OF INTERNET PUBLIC OPINIONS CAUSED BY THE FAILURE OF PUBLIC POLICIES

Gushes of Internet public opinions may trigger unexpected incidents that significantly affectsocial security and stability, especially for ones caused by the failure of public policies. Therefore,forecasting this kind of Interact public opinions is of great significance. The duration could be citedas one of the most direct indicators that can reflect the severity of a specific Internet public opinioncase. Based on this background, this paper aims to find the factors that may affect the duration of Internet public opinions, and accordingly proposes a model that can accurately predict the durationbefore the release of public policies. Specifically, an index system including 8 factors by consideringfour dimensions, namely, object, environment, reality （offline）, and the network （online）, isestablished. In addition, based on the dataset containing 23 typical Internet public opinion casescaused by the failure of public policies, 9 prediction models are gained by applying the multivariatelinear regression model, multivariate nonlinear regression model, and the Cobb-Douglas function.

The relationship between time to a high COVID-19 response level and timing of peak daily incidence:an analysis of governments’Stringency Index from 148 countries

Background The transmission dynamics and severity of coronavirus disease 2019(COVID-19)pandemic is different across countries or regions.Differences in governments’policy responses may explain some of these differences.We aimed to compare worldwide government responses to the spread of COVID-19,to examine the relationship between response level,response timing and the epidemic trajectory.Methods Free publicly-accessible data collected by the Coronavirus Government Response Tracker(OxCGRT)were used.Nine sub-indicators reflecting government response from 148 countries were collected systematically from January 1 to May 1,2020.The sub-indicators were scored and were aggregated into a common Stringency Index(SI,a value between 0 and 100)that reflects the overall stringency of the government’s response in a daily basis.Group-based trajectory modelling method was used to identify trajectories of SI.Multivariable linear regression models were used to analyse the association between time to reach a high-level SI and time to the peak number of daily new cases.Results Our results identified four trajectories of response in the spread of COVID-19 based on when the response was initiated:before January 13,from January 13 to February 12,from February 12 to March 11,and the last stage—from March 11(the day WHO declared a pandemic of COVID-19)on going.Governments’responses were upgraded with further spread of COVID-19 but varied substantially across countries.After the adjustment of SI level,geographical region and initiation stages,each day earlier to a high SI level(SI>80)from the start of response was associated with 0.44(standard error:0.08,P<0.001,R2=0.65)days earlier to the peak number of daily new case.Also,each day earlier to a high SI level from the date of first reported case was associated with 0.65(standard error:0.08,P<0.001,R2=0.42)days earlier to the peak number of daily new case.Conclusions Early start of a high-level response to COVID-19 is associated with early arrival of the peak number of daily new cases.This may help to reduce the delays in flattening the epidemic curve to the low spread level.