Landslide susceptibility mapping of mountain roads based on machine learning combined model

Landslide susceptibility mapping of mountain roads is frequently confronted by insufficient historical landslide sample data,multicollinearity of existing evaluation index factors,and inconsistency of evaluation factors due to regional environmental variations. Then, a single machine learning model can easily become overfitting,thus reducing the accuracy and robustness of the evaluation model. This paper proposes a combined machine-learning model to address the issues. The landslide susceptibili...     

Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed,Gansu Province,China

Bailongjiang watershed in southern Gansu province, China, is one of the most landslide-prone regions in China, characterized by very high frequency of landslide occurrence. In order to predict the landslide occurrence, a comprehensive map of landslide susceptibility is required which may be significantly helpful in reducing loss of property and human life. In this study, an integrated model of information value method and logistic regression is proposed by using their merits at maximum and overcoming their weaknesses, which may enhance precision and accuracy of landslide susceptibility assessment. A detailed and reliable landslide inventory with 1587 landslides was prepared and randomly divided into two groups, (i) training dataset and (ii) testing dataset. Eight distinct landslide conditioning factors including lithology, slope gradient, aspect, elevation, distance to drainages, distance to faults, distance to roads and vegetation coverage were selected for landslide susceptibility mapping. The produced landslide susceptibility maps were validated by the success rate and prediction rate curves. The validation results show that the success rate and the prediction rate of the integrated model are 81.7 % and 84.6 %, respectively, which indicate that the proposed integrated method is reliable to produce an accurate landslide susceptibility map and the results may be used for landslides management and mitigation.     

基于逻辑回归模型和3S技术的思南县滑坡易发性评价

区域滑坡易发性评价对滑坡灾害防治具有重要意义，贵州省思南县由于其特殊的自然地理和地质条件，受滑坡地质灾害的影响非常严重，因此，非常有必要对思南县的滑坡易发性进行评价。在滑坡编录的基础上，采用由RS、GIS和GPS组成的3S技术，获取了思南县的数字高程模型、坡度、坡向、剖面曲率、坡长、岩土类型、地表湿度指数、距离水系的距离、植被覆盖度和地表建筑物指数10个滑坡影响因子；再在频率比和相关性分析的基础上，利用逻辑回归模型对思南县的滑坡易发性进行了评价并绘制了易发性分布图。结果表明:利用逻辑回归模型预测思南县滑坡易发性的准确率(AUC值)达到0.797，较为准确地预测出了思南县滑坡分布规律；极高和高滑坡易发区主要分布在高程低于600 m、地表坡度较大且以软质岩类为主的区域；而极低和低滑坡易发区主要分布在高程较高、地表坡度较小且以硬质岩类为主的区域。      

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 set as 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.     

Rapid susceptibility mapping of co-seismic landslides triggered by the 2013 Lushan Earthquake using the regression model developed for the 2008 Wenchuan Earthquake

The primary objective of landslide susceptibility mapping is the prediction of potential landslides in landslide-prone areas.The predictive power of a landslide susceptibility mapping model could be tested in an adjacent area of similar geoenvironmental conditions to find out the reliability.Both the 2008 Wenchuan Earthquake and the 2013 Lushan Earthquake occurred in the Longmen Mountain seismic zone,with similar topographical and geological conditions.The two earthquakes are both featured by thrust fault and similar seismic mechanism.This paper adopted the susceptibility mapping model of co-seismic landslides triggered by Wenchuan earthquake to predict the spatial distribution of landslides induced by Lushan earthquake.Six influencing parameters were taken into consideration: distance from the seismic fault,slope gradient,lithology,distance from drainage,elevation and Peak Ground Acceleration(PGA).The preliminary results suggested that the zones with high susceptibility of coseismic landslides were mainly distributed in the mountainous areas of Lushan,Baoxing and Tianquan counties.The co-seismic landslide susceptibility map was completed in two days after the quake and sent to the field investigators to provide guidance for rescue and relief work.The predictive power of the susceptibility map was validated by ROC curve analysis method using 2037 co-seismic landslides in the epicenter area.The AUC value of 0.710 indicated that the susceptibility model derived from Wenchuan Earthquake landslides showed good accuracy in predicting the landslides triggered by Lushan earthquake.     

Landslide inventory and susceptibility assessment using multiple statistical approaches along the Karakoram highway,northern Pakistan

China-Pakistan Economic Corridor(CPEC)is a framework of regional connectivity,which will not only benefit China and Pakistan but will have positive impact on Iran,Afghanistan,India,Central Asian Republic,and the region.The surrounding area in CPEC is prone to frequent disruption by geological hazards mainly landslides in northern Pakistan.Comprehensive landslide inventory and susceptibility assessment are rarely available to utilize for landslide mitigation strategies.This study aims to utilize the high-resolution satellite images to develop a comprehensive landslide inventory and subsequently develop landslide susceptibility maps using multiple techniques.The very high-resolution(VHR)satellite images are utilized to develop a landslide inventory using the visual image classification techniques,historic records and field observations.A total of 1632 landslides are mapped in the area.Four statistical models i.e.,frequency ratio,artificial neural network,weights of evidence and logistic regression were used for landslide susceptibility modeling by comparing the landslide inventory with the topographic parameters,geological features,drainage and road network.The developed landslides susceptibility maps were verified using the area under curve(AUC)method.The prediction power of the model was assessed by the prediction rate curve.The success rate curves show 93%,92.8%,92.7%and 87.4%accuracy of susceptibility maps for frequency ratio,artificial neural network,weights of evidence and logistic regression,respectively.The developed landslide inventory and susceptibility maps can be used for land use planning and landslide mitigation strategies.     

GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.     

Genetic transformation of Platymonas (Tetraselmis) subcordiformis (Prasinophyceae, Chlorophyta) using particle bombardment and glass-bead agitation

Platymonas (Tetraselmis) subcordiformis is a unicellular marine green alga. It was found to be very sensitive to the herbicide Basta through a sensitivity test indicating it could be employed as a selective agent. The bar gene is a practicable and selectable marker gene. The vector containing the expression cassette of the bar gene was transferred to P. subcordiformis by both particle bombardment and glass-bead agitation and transformants were then selected using Basta. Finally, Southern blotting analysis indicated that the bar gene had been successfully integrated into the nuclear genome of P. subcordiformis using both of the transgenic techniques, with the transformation efficiency of the glass-bead method being slightly higher than that of particle bombardment. This is the first report on stable transformation of P. subcordiformis, and will improve fundamental research and enlarge application of this alga.     

Genetic mapping and QTL analysis for body weight in Jian carp (Cyprinus carpio var. Jian) compared with mirror carp (Cyprinus carpio L.)

We report the genetic linkage map of Jian carp(C yprinus carpio var. Jian). An F1 population comprising 94 Jian carp individuals was mapped using 254 microsatellite markers. The genetic map spanned 1 381.592 c M and comprised 44 linkage groups,with an average marker distance of 6.58 c M. We identified eight quantitative trait loci(QTLs) for body weight(BW) in seven linkage groups,explaining 12.6% to 17.3% of the phenotypic variance. Comparative mapping was performed between Jian carp and mirror carp( Cyprinus carpio L.),which both have 50 chromosomes. One hundred and ninety-eight Jian carp marker loci were found in common with the mirror carp map,with 186(93.94%) showing synteny. All 44 Jian carp linkage groups could be one-to-one aligned to the 44 mirror carp linkage groups,mostly sharing two or more common loci. Three QTLs for BW in Jian carp were conserved in mirror carp. QTL comparison suggested that the QTL confidence interval in mirror carp was more precise than the homologous interval in Jian carp,which was contained within the QTL interval in Jian carp. The syntenic relationship and consensus QTLs between the two varieties provide a foundation for genomic research and genetic breeding in common carp.     

Preliminary Mapping and Characterisation of Soils in the Pirin Mountains （Bulgaria）

Soil types, humus types and vegetation as well as their hypsometric variation were analysed in terms of sequences in the northern part of the high mountains of the Pirin National Park at altitudes between looo and 2400 m a.s.1. The study area is characterised by a large variety of natural parameters like petrology （mainly marble and granite）, morphology （different slope deposits, exposition） and the orographic climate gradient. Statistical analyses using these parameters provided a basis for the soil group classification of the sites. Based on a Digital Terrain Model （DTM） and a geological map of the Pirin National Park, the results of these statistical analyses were used to generate a ＂map of potential soil groups＂ （regionalisation using GIS）. Six potential soil groups could be determined. The resulting map exhibits a confidence level of 68 % on 74.4 % of the covered area. Rendzic Leptosols, in combination with Folic Histosols and Histi-lithic Leptosols occur in the alpine and subalpine regions on calcareous substrates. With decreasing altitude they are replaced by a mosaic of Rendzic Leptosols, Phaeozems and an increasing occurrence of Cambic Umbrisols. Umbrisols found on silicatic substrates in the alpine region are replaced by Cambic Umbrisols with decreasing altitude as well. Hence, pedogenesis is characterised by increasing browning and depth of the soil profiles with decreasing altitude. The pH-level is slightly acidic to neutral in lower zones and on calcareous rocky bases. Acidification increases in the subalpine zone. Soil pH decreases down to 4 on silicate subtrates. Typical humic values in mineral topsoils are 10 to 12 %, and in organic layers of the soils above 2000 m a.s.1, they are even more. The C：N ratio closely ranges around 20 （median）.     

Comparison of different sampling strategies for debris flow susceptibility mapping: A case study using the centroids of the scarp area,flowing area and accumulation area of debris flow watersheds

The quality of debris flow susceptibility mapping varies with sampling strategies. This paper aims at comparing three sampling strategies and determining the optimal one to sample the debris flow watersheds. The three sampling strategies studied were the centroid of the scarp area(COSA), the centroid of the flowing area(COFA), and the centroid of the accumulation area(COAA) of debris flow watersheds. An inventory consisting of 150 debris flow watersheds and 12 conditioning factors were prepared for research. Firstly, the information gain ratio(IGR) method was used to analyze the predictive ability of the conditioning factors. Subsequently, 12 conditioning factors were involved in the modeling of artificial neural network(ANN), random forest(RF) and support vector machine(SVM). Then, the receiver operating characteristic curves(ROC) and the area under curves(AUC) were used to evaluate the model performance. Finally, a scoring system was used to score the quality of the debris flow susceptibility maps. Samples obtained from the accumulation area have the strongest predictive ability and can make the models achieve the best performance. The AUC values corresponding to the best model performance on the validation dataset were 0.861, 0.804 and 0.856 for SVM, ANN and RF respectively. The sampling strategy of the centroid of the scarp area is optimal with the highest quality of debris flow susceptibility maps having scores of 373470, 393241 and 362485 for SVM, ANN and RF respectively.     

Projection of future streamflow of the Hunza River Basin,Karakoram Range (Pakistan) using HBV hydrological model

Hydrologiska Byrans Vattenbalansavdeling(HBV) Light model was used to evaluate the performance of the model in response to climate change in the snowy and glaciated catchment area of Hunza River Basin. The study aimed to understand the temporal variation of streamflow of Hunza River and its contribution to Indus River System(IRS). HBV model performed fairly well both during calibration(R2=0.87, Reff=0.85, PBIAS=-0.36) and validation(R2=0.86, Reff=0.83, PBIAS=-13.58) periods on daily time scale in the Hunza River Basin. Model performed better on monthly time scale with slightly underestimated low flows period during bothcalibration(R2=0.94, Reff=0.88, PBIAS=0.47) and validation(R2=0.92, Reff=0.85, PBIAS=15.83) periods. Simulated streamflow analysis from 1995-2010 unveiled that the average percentage contribution of snow, rain and glacier melt to the streamflow of Hunza River is about 16.5%, 19.4% and 64% respectively. In addition, the HBV-Light model performance was also evaluated for prediction of future streamflow in the Hunza River using future projected data of three General Circulation Model(GCMs) i.e. BCC-CSM1.1, CanESM2, and MIROCESM under RCP2.6, 4.5 and 8.5 and predictions were made over three time periods, 2010-2039, 2040-2069 and 2070-2099, using 1980-2010 as the control period. Overall projected climate results reveal that temperature and precipitation are the most sensitiveparameters to the streamflow of Hunza River. MIROC-ESM predicted the highest increase in the future streamflow of the Hunza River due to increase in temperature and precipitation under RCP4.5 and 8.5 scenarios from 2010-2099 while predicted slight increase in the streamflow under RCP2.6 during the start and end of the 21 th century. However, BCCCSM1.1 predicted decrease in the streamflow under RCP8.5 due to decrease in temperature and precipitation from 2010-2099. However, Can ESM2 predicted 22%-88% increase in the streamflow under RCP4.5 from 2010-2099. The results of this study could be useful for decision making and effective future strategic plans for water management and their sustainability in the region.     

A Modified Certainty Coefficient Method（M-CF） for Debris Flow Susceptibility Assessment：A Case Study for the Wenchuan Earthquake Meizoseismal Areas

In the meizoseismal areas hit by the China Wenchuan earthquake on May 12, 2008, the disasterprone environment has changed dramatically, making the susceptibility assessment of debris flow more complex and uncertain. After the earthquake, debris flow hazards occurred frequently and effective susceptibility assessment of debris flow has become extremely important. Shenxi gully in Du Jiangyan city, located in the meizoseismal areas, was selected as the study area. Based on the research of disaster-prone environment and the main factors controlling debris flow, the susceptibility zonations of debris flow were mapped using factor weight method（FW）, certainty coefficient method（CF） and geomorphic information entropy method（GI）. Through comparative analysis, the study showed that these three methods underestimated susceptible degree of debris flow when used in the meizoseismal areas of Wenchuan earthquake. In order to solve this problem, this paper developed a modified certainty coefficient method（M-CF） to reflect the impact of rich loose materials on the susceptible degree of debris flow. In the modified method, the distribution and area of loose materials were obtained by field investigations and postearthquake remote sensing image, and four data sets, namely, lithology, elevation, slop and aspect, wereused to calculate the CF values. The result of M-CF method is in agreement with field investigations and the accuracy of the method is satisfied. The method has a wide application to the susceptibility assessment of debris flow in the earthquake stricken areas.     

区域气候模式RegCM3对中国夏季降水的模拟

利用意大利国际理论物理中心（ICTP）最新发布的区域气候模式RegCM3检验我国包括青藏高原地区夏季降水的模拟能力。初始值及边界值取自美国国家环境预测中心（NCEP）和国家大气中心（NCAR）的全球再分析资料。模式积分时间为2005年5月1日到2005年8月31日,考虑到模式的“spin-up”时间,只对6月1日-8月31日的模式结果进行分析。模式水平分辨率取为60km,范围包括整个青藏高原在内的我国及周边地区（14°-55°N,70°-140°E）。结果表明：RegCM3具有模拟我国夏季降水主要分布特征的能力,尤其在观测站点稀少的青藏高原地区可提供局地降水分布的较可靠信息。模式较好地模拟了包括整个青藏高原在内的我国区域降水的月际尺度变化和空间分布等基本特征,但对我国东南地区的夏季降水模拟能力有待进一步提高。     

Biogeography and Evolutionary Factors Determine Genetic Differentiation of Pinus mugo(Turra) in the Tatra Mountains(Central Europe)

Inter Simple Sequence Repeats(ISSR) markers were used to assess genetic diversity within and among populations of dwarf mountain pine(Pinus mugo Turra) growing in the Tatra National Park(UNESCO Biosphere Reserve) in Southern Poland(Central Europe). The analyzed population belongs to two different geobotanical sub-districts: the Western and High Tatras. The level of genetic diversity assessed in this study for P. mugo is generally comparable to that reported for the other pine species in the Pinaceae family assessed by ISSR markers, especially with respect to Nei’s genetic diversity and the percentage of polymorphic bands. Bayesian analysis clustered the analyzed populations into two groups, corresponding to their geobotanical locations in the Tatras. Significant divergence between the two genetical clusters was supported by the results of Analysis of Molecular Variance(AMOVA). According to the Mantel test, there was no correlation between the genetic distance and the geographical distance. The present study confirms the existence of two genetically distinct clusters of P. mugo populations in the Tatra Mountains. The observed high population-genetic differentiation of P.mugo in the Tatras could be attributed to several genetic, environmental and historical factors occurring in this mountain area.     

Production of the Blood Pressure Lowing Peptides from Brown Alga ( Undaria pinnatifida )

1 IntroductionIncreased blood pressure appears to be one of theprimary risk factors of circulatory organ diseases suchas encepharo-apoplexy, encepharo-infarction and cardi-ac infraction. Angiotensin-I-converting enzyme (ACE)plays an important role in the rennin-angiotensin sys-tem by regulating blood pressure. Antihypertensivedrugs such as captopril and enalapril are potent ACEinhibitors (Ondetti et al., 1977). Recently, severalinhibitory peptides derived from food proteins havebeen isolat…     

Production of the Blood Pressure Lowing Peptides from Brown Alga （ Undaria pinnatifida ）

Brown alga （ Undaria pinnatifida） was treated with alginate lyase and hydrolyzed using 17 kinds of proteases and the inhibitory activity of the hydrolysates for the angiotensin-I-converting enzyme （ACE） was measured. Four hydrolysates with potent ACE-inhibitory activity were administered singly and orally to spontaneously hypertensive rats （SHRs）. The systolic blood pressure of SHRs decreases significantly after single oral administration of the brown alga hydrolysates by pro- tease S ＇ Amano＇ （from Bacillus stearothermophilus） at the concentration of 10 （mg protein） （kg body weight）^ - 1. In the 17 weeks of feeding experiment, 7-week-old SHRs were fed standard diet supplemented with the brown alga hydrolysates for 10 weeks. In SHRs fed 1.0 and 0.1% brown alga hydrolysates, elevating of systolic bloodpressure was significantly suppressed for 7 weeks. To elucidate the active components, the brown alga hydrolysates were fractionated by 1-butanol extraction and HPLC on a reverse-phase column. Seven kinds of ACE-inhibitory peptides were isolated and identified by amino acid composition analysis, sequence analysis, and LC-MS with the results Val-Tyr, Ile-Tyr, Ala-Trp, Phe-Tyr, Val-Trp, Ile-Trp, and Leu-Trp. Each peptide was determined to have an antihypertensive effect after a single oral administration in SHRs. The brown alga hydrolysates were also confirmed to decrease the blood pressure in humans.     

非等间距GM(1,1)模型的总体最小二乘算法及其病态问题

在非等间距GM(1,1)模型中,系数矩阵中有无误差的常数项和有误差的随机项,并且系数矩阵与观测向量误差同源,即系数矩阵与观测向量中有相同的元素存在,这些相同元素应该有相同的改正数,为此本文推导了一种适合非等间距GM(1,1)模型求解的总体最小二乘算法。同时,考虑到非等间距GM(1,1)模型中存在病态问题时影响总体最小二乘计算结果的稳定性,提出对系数矩阵常数列乘以某一常数的方法,以改善病态问题。     

Application of a Delay-difference model for the stock assessment of southern Atlantic albacore (Thunnus alalunga)

Delay-difference models are intermediate between simple surplus-production models and complicated age-structured models. Such intermediate models are more efficient and require less data than age-structured models. In this study, a delay-difference model was applied to fit catch and catch per unit effort(CPUE) data(1975–2011) of the southern Atlantic albacore(Thunnus alalunga) stock. The proposed delay-difference model captures annual fluctuations in predicted CPUE data better than Fox model. In a Monte Carlo simulation, white noises(CVs) were superimposed on the observed CPUE data at four levels. Relative estimate error was then calculated to compare the estimated results with the true values of parameters α and β in Ricker stock-recruitment model and the catchability coefficient q. α is more sensitive to CV than β and q. We also calculated an 80% percentile confidence interval of the maximum sustainable yield(MSY, 21756 t to 23408 t; median 22490 t) with the delay-difference model. The yield of the southern Atlantic albacore stock in 2011 was 24122 t, and the estimated ratios of catch against MSY for the past seven years were approximately 1.0. We suggest that care should be taken to protect the albacore fishery in the southern Atlantic Ocean. The proposed delay-difference model provides a good fit to the data of southern Atlantic albacore stock and may be a useful choice for the assessment of regional albacore stock.