The Weibull function,a continuous probability distribution,is widely used for diameter distribution modelling,in which parameter estimation performance is affected by stand attributes and fitting methods.The Weibull c...The Weibull function,a continuous probability distribution,is widely used for diameter distribution modelling,in which parameter estimation performance is affected by stand attributes and fitting methods.The Weibull cumulative distribution function is nonlinear,and classical fitting methods may provide a not optimal solution.Invoking the use of artificial intelligence by metaheuristics is reasonable for this optimisation task.Therefore,aimed and compared(1)the metaheuristics genetic algorithm and simulated annealing performance over the moment and percentile methods;(2)the hybrid strategy merging the metaheuristics tested and the percentile method and,(3)the metaheuristics fitness functions under basal area and density errors.A long-term experiment in a Pinus taeda stand subjected to crown thinning was used.According to our findings,all methods have a similar performance,independent of the thinning regimes and age.The pattern of the estimated parameters tends to be acceptable,as b increases over time and c increases after thinning.Overall,our findings suggest that methods based on metaheuristics have a higher precision than classical methods for estimating Weibull parameters.According to the classification test,the methods that involved simulated annealing stood out.The hybrid method involving this metaheuristic also stood out,with accurate estimates.Classical methods showed the poorest performance,and we therefore suggest the use of simulated annealing due to its faster processing time and high-quality solution.展开更多
The diameter distribution function(DDF)is a crucial tool for accurately predicting stand carbon storage(CS).The current key issue,however,is how to construct a high-precision DDF based on stand factors,site quality,an...The diameter distribution function(DDF)is a crucial tool for accurately predicting stand carbon storage(CS).The current key issue,however,is how to construct a high-precision DDF based on stand factors,site quality,and aridity index to predict stand CS in multi-species mixed forests with complex structures.This study used data from70 survey plots for mixed broadleaf Populus davidiana and Betula platyphylla forests in the Mulan Rangeland State Forest,Hebei Province,China,to construct the DDF based on maximum likelihood estimation and finite mixture model(FMM).Ordinary least squares(OLS),linear seemingly unrelated regression(LSUR),and back propagation neural network(BPNN)were used to investigate the influences of stand factors,site quality,and aridity index on the shape and scale parameters of DDF and predicted stand CS of mixed broadleaf forests.The results showed that FMM accurately described the stand-level diameter distribution of the mixed P.davidiana and B.platyphylla forests;whereas the Weibull function constructed by MLE was more accurate in describing species-level diameter distribution.The combined variable of quadratic mean diameter(Dq),stand basal area(BA),and site quality improved the accuracy of the shape parameter models of FMM;the combined variable of Dq,BA,and De Martonne aridity index improved the accuracy of the scale parameter models.Compared to OLS and LSUR,the BPNN had higher accuracy in the re-parameterization process of FMM.OLS,LSUR,and BPNN overestimated the CS of P.davidiana but underestimated the CS of B.platyphylla in the large diameter classes(DBH≥18 cm).BPNN accurately estimated stand-and species-level CS,but it was more suitable for estimating stand-level CS compared to species-level CS,thereby providing a scientific basis for the optimization of stand structure and assessment of carbon sequestration capacity in mixed broadleaf forests.展开更多
基金This work was supported fi nancially by agency CAPES(Coordination for the Improvement of Higher Education Personnel)(Finance Code 001).
文摘The Weibull function,a continuous probability distribution,is widely used for diameter distribution modelling,in which parameter estimation performance is affected by stand attributes and fitting methods.The Weibull cumulative distribution function is nonlinear,and classical fitting methods may provide a not optimal solution.Invoking the use of artificial intelligence by metaheuristics is reasonable for this optimisation task.Therefore,aimed and compared(1)the metaheuristics genetic algorithm and simulated annealing performance over the moment and percentile methods;(2)the hybrid strategy merging the metaheuristics tested and the percentile method and,(3)the metaheuristics fitness functions under basal area and density errors.A long-term experiment in a Pinus taeda stand subjected to crown thinning was used.According to our findings,all methods have a similar performance,independent of the thinning regimes and age.The pattern of the estimated parameters tends to be acceptable,as b increases over time and c increases after thinning.Overall,our findings suggest that methods based on metaheuristics have a higher precision than classical methods for estimating Weibull parameters.According to the classification test,the methods that involved simulated annealing stood out.The hybrid method involving this metaheuristic also stood out,with accurate estimates.Classical methods showed the poorest performance,and we therefore suggest the use of simulated annealing due to its faster processing time and high-quality solution.
基金funded by the National Key Research and Development Program of China(No.2022YFD2200503-02)。
文摘The diameter distribution function(DDF)is a crucial tool for accurately predicting stand carbon storage(CS).The current key issue,however,is how to construct a high-precision DDF based on stand factors,site quality,and aridity index to predict stand CS in multi-species mixed forests with complex structures.This study used data from70 survey plots for mixed broadleaf Populus davidiana and Betula platyphylla forests in the Mulan Rangeland State Forest,Hebei Province,China,to construct the DDF based on maximum likelihood estimation and finite mixture model(FMM).Ordinary least squares(OLS),linear seemingly unrelated regression(LSUR),and back propagation neural network(BPNN)were used to investigate the influences of stand factors,site quality,and aridity index on the shape and scale parameters of DDF and predicted stand CS of mixed broadleaf forests.The results showed that FMM accurately described the stand-level diameter distribution of the mixed P.davidiana and B.platyphylla forests;whereas the Weibull function constructed by MLE was more accurate in describing species-level diameter distribution.The combined variable of quadratic mean diameter(Dq),stand basal area(BA),and site quality improved the accuracy of the shape parameter models of FMM;the combined variable of Dq,BA,and De Martonne aridity index improved the accuracy of the scale parameter models.Compared to OLS and LSUR,the BPNN had higher accuracy in the re-parameterization process of FMM.OLS,LSUR,and BPNN overestimated the CS of P.davidiana but underestimated the CS of B.platyphylla in the large diameter classes(DBH≥18 cm).BPNN accurately estimated stand-and species-level CS,but it was more suitable for estimating stand-level CS compared to species-level CS,thereby providing a scientific basis for the optimization of stand structure and assessment of carbon sequestration capacity in mixed broadleaf forests.
