Landslide susceptibility assessment using the certainty factor and analytic hierarchy process

A new approach combining the certainty factor(CF) and analytic hierarchy process(AHP) methods was proposed to assess landslide susceptibility in the Ziyang district, which is situated in the Qin-Ba Mountain region, China. Landslide inventory data were collected based on field investigations and remote sensing interpretations. A total of 791 landslides were identified. A total of 633 landslides were randomly selected from this data setas the training set, and the remaining landslides were used for validation as the test set. Nine factors, including the slope angle, slope aspect, slope curvature, lithology, distance to faults, distance to streams, precipitation, road network intensity degree and land use were chosen as the landslide causal factors for further susceptibility assessment. The weight of each factor and its subclass were calculated by AHP and CF methods. Landslide susceptibility was compared between the bivariate statistical method and the proposed CF-AHP method. The results indicate that the distance to streams, distance to faults and lithology are the most dominant causal factors associated with landslides. The susceptibility zonation was categorized into five classes of landslide susceptibility, i.e., very high, high, moderate, low and very low level. Lastly, the relative operating characteristics(ROC) curve was used to validate the accuracy of the new approach, and the result showed a satisfactory prediction rate of 78.3%, compared to 69.2% obtained with the landslide susceptibility index method. The results indicate that the CF-AHP combined method is more appropriate for assessing the landslide susceptibility in this area.

Certainty factors of earthquake precursory anomaly evidences CF(E)

In this paper, we propose the principle, methods and calculating formulas for determining the certainty factors of earthquake precursory anomaly evidences CF (E). Based on the guidebooks for earthquake prediction, we give the methods of determining the CF values of 22 evidences (including seismic gap, belt, b-value, c-value, velocity ratio, strengthen of anomalous activities, quiet of anomalous activities, seismic window, earthquake swarm,earthquake sequence, coda wave, initial motion of P wave, stress drop, geoelectricity, geomagnetism, stress,ground tilt, ground water level, radon and hydrochemistry, gravity, space environment and macroscopic anomalies), and show three examples. The purposes are to use the Expert System for Earthquake Prediction (ESEP) further.

Improvements on Certainty Factor Model and Its Application to Conflict Resolution in CDPS

In this paper some improvements on certainty factor model are discussed aiming at:1)including, in a rule“E→H”,not only the CF of H when E exists but also CF of(?)when E does not exist(partly or completely).For this purpose another factor(?)is added into the original model;2) improving the model so that it can tackle problems with unknown evidence.In this aspect two concepts are introduced:(relative)maximum existence risk and(relative)maximum non-existence risk.An impor- tant result is that even if some necessary evidence is unknown one can still know the tendency whether the conclusion is true.Based on the improvements a conflict resolution model for problem-level conflict is also presented

ApplicationofcertaintyfactorsofearthquakeprecursoryanomalyevidencesCF（E）

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Landslide susceptibility assessment at Kathmandu Kyirong Highway Corridor in pre-quake, co-seismic and post-quake situations

Kathmandu Kyirong Highway(KKH)is one of the most strategic Sino-Nepal highways.Lowcost mitigation measures are common in Nepalese highways,however,they are not even applied sufficiently to control slope instability since the major part of this highway falls still under the category of feeder road,and thus less resources are made available for its maintenance.It is subjected to frequent landslide events in an annual basis,especially during monsoon season.The Gorkha earthquake,2015 further mobilized substantial hillslope materials and damaged the road in several locations.The aim of this research is to access the dynamic landslide susceptibility considering pre,co and post seismic mass failures.We mapped 5,349 multi-temporal landslides of 15 years(2004-2018),using high resolution satellite images and field data,and grouped them in aforementioned three time periods.Landslide susceptibility was assessed with the application of’certainty factor’(CF).Seventy percent landslides were used for susceptibility modelling and 30%for validation.The obtained results were evaluated by plotting’receiver operative characteristic’(ROC)curves.The CF performed well with the’area under curve’(AUC)0.820,0.875 and 0.817 for the success rates,and 0.809,0.890 and 0.760 for the prediction rates for respective pre,co and post seismic landslide susceptibility.The accuracy for seismic landslide susceptibility was better than pre and post-quake ones.It might be because of the differences on completeness of the landslide inventory,which might have been possibly done better for the single event based co-seismic landslide mapping in comparison with multitemporal inventories in pre and post-quake situations.The results obtained in this study provide insights on dynamic spatial probability of landslide occurrences in the changing condition of triggering agents.This work can be a good contribution to the methodologies for the evaluation of the dynamic landslide hazard and risk,which will further help to design the efficient mitigation measures along the mountain highways.

Quantitative prediction and evaluation of geothermal resource areas in the southwest section of the Mid-Spine Belt of Beautiful China

The geothermal resources in the southwest section of the Mid-Spine Belt of Beautiful China are abundant,but the quantitative prediction and evaluation of geothermal resources are very difficult. Based on geographic information system (GIS) and remote sensing (RS) platforms,six impact factors,namely land surface temperature,fault density,Gutenberg–Liszt B value,formation combination entropy,distance to river and aeromagnetic anomaly were selected. Through the establishment of the certainty factor model (CF),weights of the information entropy certainty factor model (ICF) and weights of the evidence certainty factor model (ECF),the geothermal potential in the study area were predicted quantitatively. Based on the ECF results,the six main geothermal resource areas were delineated. The results show that (1) ECF had high prediction accuracy (success index is 0.00405%,area ratio is 0.867);(2) The geothermal resource areas obtained were Ganzi–Ya’an–Liangshan,Panzhihua–Liangshan,Dali–Chuxiong,Nujiang–Baoshan,Diqing–Dali,and Lijiang–Diqing. The results provide a basis for the effective development and utilization of geothermal resources in the southwest section of the mid-ridge belt.

Reliable assessment approach of landslide susceptibility in broad areas based on optimal slope units and negative samples involving priori knowledge

Reliable assessment of landslide susceptibility in broad areas of terrain remains challenging due to complex topography and poor representation of randomly selected negative samples.Assessment in broad areas is now primarily based on grid units,which do not have a clear physical meaning like slope units,and their accuracy is not ideal.Nevertheless,the large amount of manual editing,due to the incorrectly generated horizontal and vertical lines during slope unit partitioning,limits using slope units for rapid assessment over large areas.Hence,this paper proposes a reliable susceptibility assessment approach to solve this problem based on optimal slope units and negative samples involving prior knowledge.Precisely,an algorithm to automatically extract slope units is designed to eliminate fragmented and erroneous units.Second,a samples labeling index(SLI)is defined based on the certainty factors model to select negative samples reasonably.Sichuan Province,China is selected for experimental analysis,with the results demonstrate that the optimized slope unit and the negative samples selection strategy consider prior knowledge achieve better results in the random forest model,support vector machine model,and artificial neural network model.In particular,the composite performance index AUC of artificial neural network model improved from 0.81 to 0.90.