This paper presents an analysis to forecast the loads of an isolated area where the history of load is not available or the history may not represent the realistic demand of electricity. The analysis is done through l...This paper presents an analysis to forecast the loads of an isolated area where the history of load is not available or the history may not represent the realistic demand of electricity. The analysis is done through linear regression and based on the identification of factors on which electrical load growth depends. To determine the identification factors, areas are selected whose histories of load growth rate known and the load growth deciding factors are similar to those of the isolated area. The proposed analysis is applied to an isolated area of Bangladesh, called Swandip where a past history of electrical load demand is not available and also there is no possibility of connecting the area with the main land grid system.展开更多
Understanding the spatial-temporal dynamics of crop nitrogen(N)use efficiency(NUE)and the relationship with explanatory environmental variables can support land-use management and policymaking.Nevertheless,the applica...Understanding the spatial-temporal dynamics of crop nitrogen(N)use efficiency(NUE)and the relationship with explanatory environmental variables can support land-use management and policymaking.Nevertheless,the application of statistical models for evaluating the explanatory variables of space-time variation in crop NUE is still under-researched.In this study,stepwise multiple linear regression(SMLR)and Random Forest(RF)were used to evaluate the spatial and temporal variation of NUE indicators(i.e.,partial factor productivity of N(PFPN);partial nutrient balance of N(PNBN))at county scale in Northeast China(Heilongjiang,Liaoning and Jilin provinces)from 1990 to 2015.Explanatory variables included agricultural management practices,topography,climate,economy,soil and crop types.Results revealed that the PFPN was higher in the northern parts and lower in the center of the Northeast China and PNBN increased from southern to northern parts during the 1990–2015 period.The NUE indicators decreased with time in most counties during the study period.The model efficiency coefficients of the SMLR and RF models were 0.44 and 0.84 for PFPN,and 0.67 and 0.89 for PNBN,respectively.The RF model had higher relative importance of soil and climatic covariates and lower relative importance of crop covariates compared to the SMLR model.The planting area index of vegetables and beans,soil clay content,saturated water content,enhanced vegetation index in November&December,soil bulk density,and annual minimum temperature were the main explanatory variables for both NUE indicators.This is the first study to show the quantitative relative importance of explanatory variables for NUE at a county level in Northeast China using RF and SMLR.This novel study gives reference measurements to improve crop NUE which is one of the most effective means of managing N for sustainable development,ensuring food security,alleviating environmental degradation and increasing farmer’s profitability.展开更多
Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have dev...Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.展开更多
In this paper,a geometric approach to fault detection and isolation (FDI) is applied to a Multiple-Input Multi-ple-Output (MIMO) model of a frame and the FDI results are compared to the ones obtained in the Single-Inp...In this paper,a geometric approach to fault detection and isolation (FDI) is applied to a Multiple-Input Multi-ple-Output (MIMO) model of a frame and the FDI results are compared to the ones obtained in the Single-Input Single-Output (SISO),Multiple-Input Single-Output (MISO),and Single-Input Multiple-Output (SIMO) cases. A proper distance function based on parameters obtained from parametric system identification method is used in the geometric approach. ARX (Auto Regressive with eXogenous input) and VARX (Vector ARX) models with 12 parameters are used in all of the above-mentioned models. The obtained results reveal that by increasing the number of inputs,the classification errors reduce,even in the case of applying only one of the inputs in the computations. Furthermore,increasing the number of measured outputs in the FDI scheme results in decreasing classification errors. Also,it is shown that by using probabilistic space in the distance function,fault diagnosis scheme has better performance in comparison with the deterministic one.展开更多
In the Forest Department of Bangladesh, a Participatory Agroforestry Program (PAP) was initiated at a denuded Sal forests area to protect the forest resources and to alleviate poverty amongst the local poor populati...In the Forest Department of Bangladesh, a Participatory Agroforestry Program (PAP) was initiated at a denuded Sal forests area to protect the forest resources and to alleviate poverty amongst the local poor population. We explored whether the PAP reduced poverty and what factors might be responsible for poverty alleviation. We used three poverty measurement methods: the Head Count Index, the Poverty Gap Index and the Foster-Greer-Thorbecke index to determine the extent poverty reduction. We used a linear regression model to determine the possible differences among factors in poverty reduction. Data were collected through semi structured questionnaires and face to face interviews within the study area. PAP proved effective at poverty alleviation, considerably improving the local situation. The linear regression model showed that PAP output explained the income differences in poverty reduction. Participants identified bureaucracy and illegal money demands by forest department officials, an uncontrolled market system, and underdeveloped road infrastructure as the main obstacles to reduction of poverty. Overall, PAP is quite successful in alleviating poverty. So this program might be of interest at other degraded forest areas as a tool to alleviate poverty.展开更多
We evaluated the spatial and temporal patterns of forest fires in two fire seasons (March to June and September to November) from 1996 to 2010 in Jilin Province, China, using the Canadian Forest Fire Weather Index Sys...We evaluated the spatial and temporal patterns of forest fires in two fire seasons (March to June and September to November) from 1996 to 2010 in Jilin Province, China, using the Canadian Forest Fire Weather Index System. Fire data were obtained from the Provincial Fire Agency, and historical climate records of daily weather observations were collected from 36 weather stations in Jilin and its neighboring provinces. A linear regression model was used to analyze linear trends between climate and fire weather indices with time treated as an independent variable. Correlation analysis was used to detect correlations between fire frequency, areas burned, and fire weather indices. A thin-plate smooth spline model was used to interpolate the point data of 36 weather stations to generate a surface covering the whole province. Our analyses indicated fire frequency and areas burned were significantly correlated with fire weather indices. Overall, the Canadian Forest Fire Weather Index System appeared to be work well for determining the fire danger rating in Jilin Province. Also, our analyses indicated that in the forthcoming decades, the overall fire danger in March and April should decrease across the province, but the chance of a large fire in these months would increase. The fire danger in the fall fire season would increase in the future, and the chance of large fire would also increase. Historically, because most fires have occurred in the spring in Jilin Province, such a shift in the future fire danger between the two fire seasons would be beneficial for the province's fire management.展开更多
Accurate estimates of forest aboveground biomass(AGB)are critical for supporting strategies of ecosystem conservation and climate change mitigation.The Jiuzhaigou National Nature Reserve,located in Eastern Tibet Plate...Accurate estimates of forest aboveground biomass(AGB)are critical for supporting strategies of ecosystem conservation and climate change mitigation.The Jiuzhaigou National Nature Reserve,located in Eastern Tibet Plateau,has rich forest resources on steep slopes and is very sensitive to climate change but plays an important role in the regulation of regional carbon cycles.However,an estimation of AGB of subalpine forests in the Nature Reserve has not been carried out and whether a global biomass model is available has not been determined.To provide this information,Landsat 8 OLI and Sentinel-2B data were combined to estimate subalpine forest AGB using linear regression,and two machine learning approaches–random forest and extreme gradient boosting,with 54 inventory plots.Regardless of forest type,Observed AGB of the Reserve varied from 61.7 to 475.1 Mg hawith an average of 180.6 Mg ha.Results indicate that integrating the Landsat 8 OLI and Sentinel-2B imagery significantly improved model efficiency regardless of modelling approaches.The results highlight a potential way to improve the prediction of forest AGB in mountainous regions.Modelled AGB indicated a strong spatial variability.However,the modelled biomass varied greatly with global biomass products,indicating that global biomass products should be evaluated in regional AGB estimates and more field observations are required,particularly for areas with complex terrain to improve model accuracy.展开更多
The Natural Forest Protection(NFP) program is one of the Six Key Forestry Projects which were adopted by the Chinese Government since the 1980s to address important natural issues in China. It advanced to protecting a...The Natural Forest Protection(NFP) program is one of the Six Key Forestry Projects which were adopted by the Chinese Government since the 1980s to address important natural issues in China. It advanced to protecting and restoring the structures and functions of the natural forests through sustainable forest management. However, the role of forest carbon storage and tree carbon pool dynamics since the adoption of the NFP remains unknown. To address this knowledge gap, this study calculated forest carbon storage(tree, understory, forest floor and soil) in the forest region of northeastern(NE) China based on National Forest Inventory databases and field investigated databases. For tree biomass, this study utilized an improved method for biomass estimation that converts timber volume to total forest biomass; while for understory, forest floor and soil carbon storage, this study utilized forest type-specific mean carbon densities multiplied by their areas in the region. Results showed that the tree carbon pool under the NFP in NE China functioned as a carbon sink from 1998 to 2008, with an increase of 6.3 Tg C/yr, which was mainly sequestrated by natural forests(5.1 Tg C/yr). At the same time, plantations also acted as a carbon sink, reflecting an increase of 1.2 Tg C/yr. In 2008, total carbon storage in forests covered by the NFP in NE China was 4603.8 Tg C, of which 4393.3 Tg C was stored in natural forests and 210.5 Tg C in planted forests. Soil was the largest carbon storage component, contributing 69.5%–77.8% of total carbon storage; followed by tree and forest floor, accounting for 16.3%–23.0% and 5.0%–6.5% of total carbon storage, respectively. Understory carbon pool ranged from 1.9 to 42.7 Tg C, accounting for only 0.9% of total carbon storage.展开更多
This paper presents a new approach to identifying the climate variables that influence the size of the area burned by forest wildfires.Multiple linear regression was used in combination with nonlinear variable transfo...This paper presents a new approach to identifying the climate variables that influence the size of the area burned by forest wildfires.Multiple linear regression was used in combination with nonlinear variable transformations to determine relevant nonlinear forest wildfire size functions.Data from the Prague-East District of the Czech Republic was used for model derivation.Individual burned forest area was hypothesized as a function of water vapor pressure,air temperature and wind speed.Wind speed was added to enhance predictions of the size of forest wildfires,and further improvements to the utility of prediction methods were added to the regression equation.The results show that if the air temperature increases,it may contain less water and the fuel will become drier.The size of the burned area then increases.If the relative humidity in the air increases and the wind speed decreases,the size of the burned area is reduced.Our model suggests that changes in the climate factors caused by ongoing climate change could cause significant changes in the size of wildfire in forests.展开更多
Topographic attributes are often used as explanatory variables when providing spatial estimates of various environmental attribute response variables. Elevation of sampling locations can be derived from global positio...Topographic attributes are often used as explanatory variables when providing spatial estimates of various environmental attribute response variables. Elevation of sampling locations can be derived from global positioning systems (GPS) or digital elevation models (DEM). Given the potential for differences in elevation among these two data sources, especially in response to forest canopy cover, our objective was to compare GPS and DEM-derived elevation values during the dormant season. A non-parametric Wilcoxon test indicated GPS elevation was higher than DEM elevation with a mean difference of 6 m. Linear regression analysis indicated that GPS and DEM elevation were well correlated (R2 = 0.71, r = 0.84, p 【0.0001). Although elevation among the two data sources differed, the strong linear relationship allows for correction of elevation values in a predictable manner.展开更多
文摘This paper presents an analysis to forecast the loads of an isolated area where the history of load is not available or the history may not represent the realistic demand of electricity. The analysis is done through linear regression and based on the identification of factors on which electrical load growth depends. To determine the identification factors, areas are selected whose histories of load growth rate known and the load growth deciding factors are similar to those of the isolated area. The proposed analysis is applied to an isolated area of Bangladesh, called Swandip where a past history of electrical load demand is not available and also there is no possibility of connecting the area with the main land grid system.
基金the China Scholarship Council(CSC)(201903250115)the National Natural Science Foundation of China(31972515)the China Agriculture Research System of MOF and MARA(CARS-09-P31).
文摘Understanding the spatial-temporal dynamics of crop nitrogen(N)use efficiency(NUE)and the relationship with explanatory environmental variables can support land-use management and policymaking.Nevertheless,the application of statistical models for evaluating the explanatory variables of space-time variation in crop NUE is still under-researched.In this study,stepwise multiple linear regression(SMLR)and Random Forest(RF)were used to evaluate the spatial and temporal variation of NUE indicators(i.e.,partial factor productivity of N(PFPN);partial nutrient balance of N(PNBN))at county scale in Northeast China(Heilongjiang,Liaoning and Jilin provinces)from 1990 to 2015.Explanatory variables included agricultural management practices,topography,climate,economy,soil and crop types.Results revealed that the PFPN was higher in the northern parts and lower in the center of the Northeast China and PNBN increased from southern to northern parts during the 1990–2015 period.The NUE indicators decreased with time in most counties during the study period.The model efficiency coefficients of the SMLR and RF models were 0.44 and 0.84 for PFPN,and 0.67 and 0.89 for PNBN,respectively.The RF model had higher relative importance of soil and climatic covariates and lower relative importance of crop covariates compared to the SMLR model.The planting area index of vegetables and beans,soil clay content,saturated water content,enhanced vegetation index in November&December,soil bulk density,and annual minimum temperature were the main explanatory variables for both NUE indicators.This is the first study to show the quantitative relative importance of explanatory variables for NUE at a county level in Northeast China using RF and SMLR.This novel study gives reference measurements to improve crop NUE which is one of the most effective means of managing N for sustainable development,ensuring food security,alleviating environmental degradation and increasing farmer’s profitability.
基金This research was funded by the National Natural Science Foundation of China(grant no.32271881).
文摘Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.
文摘In this paper,a geometric approach to fault detection and isolation (FDI) is applied to a Multiple-Input Multi-ple-Output (MIMO) model of a frame and the FDI results are compared to the ones obtained in the Single-Input Single-Output (SISO),Multiple-Input Single-Output (MISO),and Single-Input Multiple-Output (SIMO) cases. A proper distance function based on parameters obtained from parametric system identification method is used in the geometric approach. ARX (Auto Regressive with eXogenous input) and VARX (Vector ARX) models with 12 parameters are used in all of the above-mentioned models. The obtained results reveal that by increasing the number of inputs,the classification errors reduce,even in the case of applying only one of the inputs in the computations. Furthermore,increasing the number of measured outputs in the FDI scheme results in decreasing classification errors. Also,it is shown that by using probabilistic space in the distance function,fault diagnosis scheme has better performance in comparison with the deterministic one.
文摘In the Forest Department of Bangladesh, a Participatory Agroforestry Program (PAP) was initiated at a denuded Sal forests area to protect the forest resources and to alleviate poverty amongst the local poor population. We explored whether the PAP reduced poverty and what factors might be responsible for poverty alleviation. We used three poverty measurement methods: the Head Count Index, the Poverty Gap Index and the Foster-Greer-Thorbecke index to determine the extent poverty reduction. We used a linear regression model to determine the possible differences among factors in poverty reduction. Data were collected through semi structured questionnaires and face to face interviews within the study area. PAP proved effective at poverty alleviation, considerably improving the local situation. The linear regression model showed that PAP output explained the income differences in poverty reduction. Participants identified bureaucracy and illegal money demands by forest department officials, an uncontrolled market system, and underdeveloped road infrastructure as the main obstacles to reduction of poverty. Overall, PAP is quite successful in alleviating poverty. So this program might be of interest at other degraded forest areas as a tool to alleviate poverty.
基金financially supported by the National Natural Science Foundation of China(31470497)Project 2013-158,Jilin Provincial Education Department+1 种基金Project 2013-007,Jilin Provincial Forestry Departmentsupported by the Program for New Century Excellent Talents in the University(NCET-12-0726)
文摘We evaluated the spatial and temporal patterns of forest fires in two fire seasons (March to June and September to November) from 1996 to 2010 in Jilin Province, China, using the Canadian Forest Fire Weather Index System. Fire data were obtained from the Provincial Fire Agency, and historical climate records of daily weather observations were collected from 36 weather stations in Jilin and its neighboring provinces. A linear regression model was used to analyze linear trends between climate and fire weather indices with time treated as an independent variable. Correlation analysis was used to detect correlations between fire frequency, areas burned, and fire weather indices. A thin-plate smooth spline model was used to interpolate the point data of 36 weather stations to generate a surface covering the whole province. Our analyses indicated fire frequency and areas burned were significantly correlated with fire weather indices. Overall, the Canadian Forest Fire Weather Index System appeared to be work well for determining the fire danger rating in Jilin Province. Also, our analyses indicated that in the forthcoming decades, the overall fire danger in March and April should decrease across the province, but the chance of a large fire in these months would increase. The fire danger in the fall fire season would increase in the future, and the chance of large fire would also increase. Historically, because most fires have occurred in the spring in Jilin Province, such a shift in the future fire danger between the two fire seasons would be beneficial for the province's fire management.
基金supported financially by the Specialized Fund for the Post-Disaster Reconstruction and Heritage Protec-tion in Sichuan Province(5132202019000128)the Everest Scientific Research Program of Chengdu University of Technology(80000-2021ZF11410)+3 种基金the Second Tibetan Plateau Scientific Expedition and Research Program(STEP,2019QZKK0307)the State Key Laborato-ry of Geohazard Prevention and Geoenvironment Protection Independent Research Project(SKLGP2018Z004)the key technologies of Mountain rail transit green construction in ecologically sensitive region based on Mountain rail transit from Dujiangyan to Mt.Siguniang anti-poverty project(2018-zl-08)Study on risk identification and countermeasures of Sichuan-Tibet Railway Major Projects(2019YFG0460)。
文摘Accurate estimates of forest aboveground biomass(AGB)are critical for supporting strategies of ecosystem conservation and climate change mitigation.The Jiuzhaigou National Nature Reserve,located in Eastern Tibet Plateau,has rich forest resources on steep slopes and is very sensitive to climate change but plays an important role in the regulation of regional carbon cycles.However,an estimation of AGB of subalpine forests in the Nature Reserve has not been carried out and whether a global biomass model is available has not been determined.To provide this information,Landsat 8 OLI and Sentinel-2B data were combined to estimate subalpine forest AGB using linear regression,and two machine learning approaches–random forest and extreme gradient boosting,with 54 inventory plots.Regardless of forest type,Observed AGB of the Reserve varied from 61.7 to 475.1 Mg hawith an average of 180.6 Mg ha.Results indicate that integrating the Landsat 8 OLI and Sentinel-2B imagery significantly improved model efficiency regardless of modelling approaches.The results highlight a potential way to improve the prediction of forest AGB in mountainous regions.Modelled AGB indicated a strong spatial variability.However,the modelled biomass varied greatly with global biomass products,indicating that global biomass products should be evaluated in regional AGB estimates and more field observations are required,particularly for areas with complex terrain to improve model accuracy.
基金Under the auspices of Strategic Priority Research Program of Chinese Academy of Sciences(No.XDA05060200)National Key Technology Research and Development Program of China(No.2012BAD22B04)Visiting Professorship for Senior International Scientists of Chinese Academy of Sciences(No.2012T1Z0006)
文摘The Natural Forest Protection(NFP) program is one of the Six Key Forestry Projects which were adopted by the Chinese Government since the 1980s to address important natural issues in China. It advanced to protecting and restoring the structures and functions of the natural forests through sustainable forest management. However, the role of forest carbon storage and tree carbon pool dynamics since the adoption of the NFP remains unknown. To address this knowledge gap, this study calculated forest carbon storage(tree, understory, forest floor and soil) in the forest region of northeastern(NE) China based on National Forest Inventory databases and field investigated databases. For tree biomass, this study utilized an improved method for biomass estimation that converts timber volume to total forest biomass; while for understory, forest floor and soil carbon storage, this study utilized forest type-specific mean carbon densities multiplied by their areas in the region. Results showed that the tree carbon pool under the NFP in NE China functioned as a carbon sink from 1998 to 2008, with an increase of 6.3 Tg C/yr, which was mainly sequestrated by natural forests(5.1 Tg C/yr). At the same time, plantations also acted as a carbon sink, reflecting an increase of 1.2 Tg C/yr. In 2008, total carbon storage in forests covered by the NFP in NE China was 4603.8 Tg C, of which 4393.3 Tg C was stored in natural forests and 210.5 Tg C in planted forests. Soil was the largest carbon storage component, contributing 69.5%–77.8% of total carbon storage; followed by tree and forest floor, accounting for 16.3%–23.0% and 5.0%–6.5% of total carbon storage, respectively. Understory carbon pool ranged from 1.9 to 42.7 Tg C, accounting for only 0.9% of total carbon storage.
基金funded by grant"EVA4.0",No.CZ.02.1.01/0.0/0.0/16_019/0000803 financed by the Operational Program Research,Development and Education(OP RDE),the Ministry of Education of the Czech Republic。
文摘This paper presents a new approach to identifying the climate variables that influence the size of the area burned by forest wildfires.Multiple linear regression was used in combination with nonlinear variable transformations to determine relevant nonlinear forest wildfire size functions.Data from the Prague-East District of the Czech Republic was used for model derivation.Individual burned forest area was hypothesized as a function of water vapor pressure,air temperature and wind speed.Wind speed was added to enhance predictions of the size of forest wildfires,and further improvements to the utility of prediction methods were added to the regression equation.The results show that if the air temperature increases,it may contain less water and the fuel will become drier.The size of the burned area then increases.If the relative humidity in the air increases and the wind speed decreases,the size of the burned area is reduced.Our model suggests that changes in the climate factors caused by ongoing climate change could cause significant changes in the size of wildfire in forests.
文摘Topographic attributes are often used as explanatory variables when providing spatial estimates of various environmental attribute response variables. Elevation of sampling locations can be derived from global positioning systems (GPS) or digital elevation models (DEM). Given the potential for differences in elevation among these two data sources, especially in response to forest canopy cover, our objective was to compare GPS and DEM-derived elevation values during the dormant season. A non-parametric Wilcoxon test indicated GPS elevation was higher than DEM elevation with a mean difference of 6 m. Linear regression analysis indicated that GPS and DEM elevation were well correlated (R2 = 0.71, r = 0.84, p 【0.0001). Although elevation among the two data sources differed, the strong linear relationship allows for correction of elevation values in a predictable manner.