This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting de...This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.展开更多
Restoring degraded forests and agricultural lands has become a global conservation priority. A growing number of tools can quantify ecosystem service tradeoffs associated with forest restoration. This evolving ＂tools...Restoring degraded forests and agricultural lands has become a global conservation priority. A growing number of tools can quantify ecosystem service tradeoffs associated with forest restoration. This evolving ＂tools landscape＂ presents a dilemma： more tools are available, but selecting appropriate tools has become more challenging. We present a Restoration Ecosystem Service Tool Selector （RESTS） framework that describes key characteristics of 13 ecosystem service assessment tools. Analysts enter information about their decision context, services to be analyzed, and desired outputs. Tools are filtered and presented based on five evaluative criteria： scalability, cost, time requirements, handling of uncertainty, and applicability to benefit-cost analysis. RESTS uses a spreadsheet interface but a web-based interface is planned. Given the rapid evolution of ecosystem services science, RESTS provides an adaptable framework to guide forest restoration decision makers toward tools that can help quantify ecosystem services in support of restoration.展开更多
We investigated procurement of raw materials for particleboard to minimize costs and develop an efficient optimization model for product mix. In a multiple-vendor market, vendors must be evaluated based on specified c...We investigated procurement of raw materials for particleboard to minimize costs and develop an efficient optimization model for product mix. In a multiple-vendor market, vendors must be evaluated based on specified criteria. Assuming sourcing from the highest-scoring vendors, annual purchase quantities are then planned. To meet procure- ment needs, we first propose a model to describe the problem. Then, an appropriate multi-criteria decision making （MCDM） technique is se- lected to solve it. We ran the model using commercial software such as LINGO~ and then compared the model results to a real case involving one of the largest particleboard manufacturers in the region. The model run based real data yielded a procurement program that is more efficient and lower in cost than the program currently in use. Use of this procurement modelling approach would yield considerable financial returns.展开更多
Electronic Health Records(EHRs)are the digital form of patients’medical reports or records.EHRs facilitate advanced analytics and aid in better decision-making for clinical data.Medical data are very complicated and ...Electronic Health Records(EHRs)are the digital form of patients’medical reports or records.EHRs facilitate advanced analytics and aid in better decision-making for clinical data.Medical data are very complicated and using one classification algorithm to reach good results is difficult.For this reason,we use a combination of classification techniques to reach an efficient and accurate classification model.This model combination is called the Ensemble model.We need to predict new medical data with a high accuracy value in a small processing time.We propose a new ensemble model MDRL which is efficient with different datasets.The MDRL gives the highest accuracy value.It saves the processing time instead of processing four different algorithms sequentially;it executes the four algorithms in parallel.We implement five different algorithms on five variant datasets which are Heart Disease,Health General,Diabetes,Heart Attack,and Covid-19 Datasets.The four algorithms are Random Forest(RF),Decision Tree(DT),Logistic Regression(LR),and Multi-layer Perceptron(MLP).In addition to MDRL(our proposed ensemble model)which includes MLP,DT,RF,and LR together.From our experiments,we conclude that our ensemble model has the best accuracy value for most datasets.We reach that the combination of the Correlation Feature Selection(CFS)algorithm and our ensemble model is the best for giving the highest accuracy value.The accuracy values for our ensemble model based on CFS are 98.86,97.96,100,99.33,and 99.37 for heart disease,health general,Covid-19,heart attack,and diabetes datasets respectively.展开更多
基金This work was supported in part by the National Natural Science Foundation of China(61601418,41602362,61871259)in part by the Opening Foundation of Hunan Engineering and Research Center of Natural Resource Investigation and Monitoring(2020-5)+1 种基金in part by the Qilian Mountain National Park Research Center(Qinghai)(grant number:GKQ2019-01)in part by the Geomatics Technology and Application Key Laboratory of Qinghai Province,Grant No.QHDX-2019-01.
文摘This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.
文摘Restoring degraded forests and agricultural lands has become a global conservation priority. A growing number of tools can quantify ecosystem service tradeoffs associated with forest restoration. This evolving ＂tools landscape＂ presents a dilemma： more tools are available, but selecting appropriate tools has become more challenging. We present a Restoration Ecosystem Service Tool Selector （RESTS） framework that describes key characteristics of 13 ecosystem service assessment tools. Analysts enter information about their decision context, services to be analyzed, and desired outputs. Tools are filtered and presented based on five evaluative criteria： scalability, cost, time requirements, handling of uncertainty, and applicability to benefit-cost analysis. RESTS uses a spreadsheet interface but a web-based interface is planned. Given the rapid evolution of ecosystem services science, RESTS provides an adaptable framework to guide forest restoration decision makers toward tools that can help quantify ecosystem services in support of restoration.
文摘We investigated procurement of raw materials for particleboard to minimize costs and develop an efficient optimization model for product mix. In a multiple-vendor market, vendors must be evaluated based on specified criteria. Assuming sourcing from the highest-scoring vendors, annual purchase quantities are then planned. To meet procure- ment needs, we first propose a model to describe the problem. Then, an appropriate multi-criteria decision making （MCDM） technique is se- lected to solve it. We ran the model using commercial software such as LINGO~ and then compared the model results to a real case involving one of the largest particleboard manufacturers in the region. The model run based real data yielded a procurement program that is more efficient and lower in cost than the program currently in use. Use of this procurement modelling approach would yield considerable financial returns.
文摘Electronic Health Records(EHRs)are the digital form of patients’medical reports or records.EHRs facilitate advanced analytics and aid in better decision-making for clinical data.Medical data are very complicated and using one classification algorithm to reach good results is difficult.For this reason,we use a combination of classification techniques to reach an efficient and accurate classification model.This model combination is called the Ensemble model.We need to predict new medical data with a high accuracy value in a small processing time.We propose a new ensemble model MDRL which is efficient with different datasets.The MDRL gives the highest accuracy value.It saves the processing time instead of processing four different algorithms sequentially;it executes the four algorithms in parallel.We implement five different algorithms on five variant datasets which are Heart Disease,Health General,Diabetes,Heart Attack,and Covid-19 Datasets.The four algorithms are Random Forest(RF),Decision Tree(DT),Logistic Regression(LR),and Multi-layer Perceptron(MLP).In addition to MDRL(our proposed ensemble model)which includes MLP,DT,RF,and LR together.From our experiments,we conclude that our ensemble model has the best accuracy value for most datasets.We reach that the combination of the Correlation Feature Selection(CFS)algorithm and our ensemble model is the best for giving the highest accuracy value.The accuracy values for our ensemble model based on CFS are 98.86,97.96,100,99.33,and 99.37 for heart disease,health general,Covid-19,heart attack,and diabetes datasets respectively.
