Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments,but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT)model and the K-means cluster algorithm to produce a regional landslide susceptibility map.Yanchang County,a typical landslide-prone area located in northwestern China,was taken as the area of interest to introduce the proposed application procedure.A landslide inventory containing 82 landslides was prepared and subse-quently randomly partitioned into two subsets:training data(70%landslide pixels)and validation data(30%landslide pixels).Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter algorithm.The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC)curve)of the proposed model was the highest,reaching 0.88,compared with traditional models(support vector machine(SVM)=0.85,Bayesian network(BN)=0.81,frequency ratio(FR)=0.75,weight of evidence(WOE)=0.76).The landslide frequency ratio and fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,respectively,which were much higher than those of the low susceptibility zones.The top 20%interval of landslide occurrence probability contained 89%of the historical landslides but only accounted for 10.3%of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices. 相似文献
Upon completion, China’s national emissions trading scheme (C-ETS) will be the largest carbon market in the world. Recent research has evaluated China’s seven pilot ETSs launched from 2013 on, and academic literature on design aspects of the C-ETS abounds. Yet little is known about the specific details of the upcoming C-ETS. This article combines currently understood details of China’s national carbon market with lessons learned in the pilot schemes as well as from the academic literature. Our review follows the taxonomy of Emissions Trading in Practice: A Handbook on Design and Implementation (Partnership for Market Readiness & International Carbon Action Partnership. (2016). Retrieved from www.worldbank.org): The 10 categories are: scope, cap, distribution of allowances, use of offsets, temporal flexibility, price predictability, compliance and oversight, stakeholder engagement and capacity building, linking, implementation and improvements.
Key policy insights
Accurate emissions data is paramount for both design and implementation, and its availability dictates the scope of the C-ETS.
The stakeholder consultative process is critical for effective design, and China is able to build on its extensive experience through the pilot ETSs.
Current policies and positions on intensity targets and Clean Development Mechanism (CDM) credits constrain the market design of the C-ETS.
Most critical is the nature of the cap. The currently discussed rate-based cap with ex post adjustment is risky. Instead, an absolute, mass-based emissions cap coupled with the conditional use of permits would allow China to maintain flexibility in the carbon market while ensuring a limit on CO2 emissions.