选用符合林火发生数据结构的Poisson和零膨胀Poisson(ZIP)模型对大兴安岭林区1980—2005年间林火发生与气象因素关系进行建模分析,并与普通最小二乘回归(ordinary least squares,OLS)方法的结果进行了对比分析.结果表明:OLS模型对研究...选用符合林火发生数据结构的Poisson和零膨胀Poisson(ZIP)模型对大兴安岭林区1980—2005年间林火发生与气象因素关系进行建模分析,并与普通最小二乘回归(ordinary least squares,OLS)方法的结果进行了对比分析.结果表明:OLS模型对研究区域林火发生与气象因素关系的拟合结果较差(R2=0.215);Poisson和ZIP模型的拟合效果较好,具有较好的火灾次数预测能力,且ZIP模型的预测能力高于Poisson模型.运用AIC和Vuong检验方法对Poisson和ZIP模型的拟合水平进行进一步检验,表明ZIP模型的数据拟合度优于Poisson模型.展开更多
The occurrence of lightning-induced forest fires during a time period is count data featuring over-dispersion （i.e., variance is larger than mean） and a high frequency of zero counts. In this study, we used six gene...The occurrence of lightning-induced forest fires during a time period is count data featuring over-dispersion （i.e., variance is larger than mean） and a high frequency of zero counts. In this study, we used six generalized linear models to examine the relationship between the occurrence of lightning-induced forest fires and meteorological factors in the Northern Daxing＇an Mountains of China. The six models included Poisson, negative binomial （NB）, zero- inflated Poisson （ZIP）, zero-inflated negative binomial （ZINB）, Poisson hurdle （PH）, and negative binomial hurdle （NBH） models. Goodness-of-fit was compared and tested among the six models using Akaike information criterion （AIC）, sum of squared errors, likelihood ratio test, and Vuong test. The predictive performance of the models was assessed and compared using independent validation data by the data-splitting method. Based on the model AIC, the ZINB model best fitted the fire occurrence data, followed by （in order of smaller AIC） NBH, ZIP, NB, PH, and Poisson models. The ZINB model was also best for pre- dicting either zero counts or positive counts （〉1）. The two Hurdle models （PH and NBH） were better than ZIP, Poisson, and NB models for predicting positive counts, but worse than these three models for predicting zero counts. Thus, the ZINB model was the first choice for modeling the occurrence of lightning-induced forest fires in this study, which implied that the excessive zero counts of lightning- induced fires came from both structure and sampling zeros.展开更多
Urban taxi demand prediction faces a critical resolution paradox:high-resolution forecasts enable operational agility but suffer from extreme sparsity-induced volatility,while low-resolution predictions sacrifice resp...Urban taxi demand prediction faces a critical resolution paradox:high-resolution forecasts enable operational agility but suffer from extreme sparsity-induced volatility,while low-resolution predictions sacrifice responsiveness for stability.We present a Scalable SpatioTemporal Zero-Inflated Poisson Graph Neural Network(SSTZIP-GNN),that resolves this paradox through three innovations:(1)Zero-Inflated Poisson(ZIP)integration that explicitly models structural zeros in sparse demand distributions,distinguishing genuine low-demand periods from data artifacts;(2)Adaptive spatiotemporal learning that dynamically adjusts kernel dilation factors and graph diffusion rates across temporal resolutions using Diffusion Graph Convolutional Networks(DGCNs)and Temporal Convolutional Networks(TCNs);(3)Multimodal feature fusion incorporating real-time crowd-sourced mobility data,socioeconomic indicators,and Global Position System(GPS)trajectories for enhanced robustness under variable urban conditions.Extensive evaluation on 130 million real-world mobility records demonstrates superior performance,achieving 34.8%Mean Absolute Error(MAE)reduction over state-of-the-art baselines.The model reduces computational costs by 46.3%compared to ensemble approaches while maintaining high accuracy across resolutions,delivering 33.4%-53.3%Root Mean Square Error(RMSE)reduction across different prediction resolution scenarios.This unified framework enables cities to implement demand-responsive fleet management,dynamic pricing,and sustainable mobility planning across diverse urban landscapes.展开更多
文摘选用符合林火发生数据结构的Poisson和零膨胀Poisson(ZIP)模型对大兴安岭林区1980—2005年间林火发生与气象因素关系进行建模分析,并与普通最小二乘回归(ordinary least squares,OLS)方法的结果进行了对比分析.结果表明:OLS模型对研究区域林火发生与气象因素关系的拟合结果较差(R2=0.215);Poisson和ZIP模型的拟合效果较好,具有较好的火灾次数预测能力,且ZIP模型的预测能力高于Poisson模型.运用AIC和Vuong检验方法对Poisson和ZIP模型的拟合水平进行进一步检验,表明ZIP模型的数据拟合度优于Poisson模型.
基金funded by Asia–Pacific Forests Net(APFNET/2010/FPF/001)National Natural Science Foundation of China(Grant No.31400552)
文摘The occurrence of lightning-induced forest fires during a time period is count data featuring over-dispersion （i.e., variance is larger than mean） and a high frequency of zero counts. In this study, we used six generalized linear models to examine the relationship between the occurrence of lightning-induced forest fires and meteorological factors in the Northern Daxing＇an Mountains of China. The six models included Poisson, negative binomial （NB）, zero- inflated Poisson （ZIP）, zero-inflated negative binomial （ZINB）, Poisson hurdle （PH）, and negative binomial hurdle （NBH） models. Goodness-of-fit was compared and tested among the six models using Akaike information criterion （AIC）, sum of squared errors, likelihood ratio test, and Vuong test. The predictive performance of the models was assessed and compared using independent validation data by the data-splitting method. Based on the model AIC, the ZINB model best fitted the fire occurrence data, followed by （in order of smaller AIC） NBH, ZIP, NB, PH, and Poisson models. The ZINB model was also best for pre- dicting either zero counts or positive counts （〉1）. The two Hurdle models （PH and NBH） were better than ZIP, Poisson, and NB models for predicting positive counts, but worse than these three models for predicting zero counts. Thus, the ZINB model was the first choice for modeling the occurrence of lightning-induced forest fires in this study, which implied that the excessive zero counts of lightning- induced fires came from both structure and sampling zeros.
基金supported by the Lingnan University(LU)(Nos.DR25F4,DB24C4,and 871242)the Lam Woo Research Fund at LU(No.LWP20021)the Shenzhen University-Lingnan University Joint Research Programme(No.SZU-LU007/2526).
文摘Urban taxi demand prediction faces a critical resolution paradox:high-resolution forecasts enable operational agility but suffer from extreme sparsity-induced volatility,while low-resolution predictions sacrifice responsiveness for stability.We present a Scalable SpatioTemporal Zero-Inflated Poisson Graph Neural Network(SSTZIP-GNN),that resolves this paradox through three innovations:(1)Zero-Inflated Poisson(ZIP)integration that explicitly models structural zeros in sparse demand distributions,distinguishing genuine low-demand periods from data artifacts;(2)Adaptive spatiotemporal learning that dynamically adjusts kernel dilation factors and graph diffusion rates across temporal resolutions using Diffusion Graph Convolutional Networks(DGCNs)and Temporal Convolutional Networks(TCNs);(3)Multimodal feature fusion incorporating real-time crowd-sourced mobility data,socioeconomic indicators,and Global Position System(GPS)trajectories for enhanced robustness under variable urban conditions.Extensive evaluation on 130 million real-world mobility records demonstrates superior performance,achieving 34.8%Mean Absolute Error(MAE)reduction over state-of-the-art baselines.The model reduces computational costs by 46.3%compared to ensemble approaches while maintaining high accuracy across resolutions,delivering 33.4%-53.3%Root Mean Square Error(RMSE)reduction across different prediction resolution scenarios.This unified framework enables cities to implement demand-responsive fleet management,dynamic pricing,and sustainable mobility planning across diverse urban landscapes.
