Forecasting hurricane-induced power outage durations

Accurate estimates of the duration of power outages caused by hurricanes prior to landfall are valuable for utility companies and government agencies that wish to plan and optimize their restoration efforts. Accurate pre-storm estimates are also important information for customers and operators of other infrastructures systems, who rely heavily on electricity. Traditionally, utilities make restoration plans based on managerial judgment and experience. However, skillful outage forecast models are conducive to improved decision-making practices by utilities and can greatly enhance storm preparation and restoration management procedures of power companies and emergency managers. This paper presents a novel statistical approach for estimating power outage durations that is 87 % more accurate than existing models in the literature. The power outage duration models are developed and carefully validated for outages caused by Hurricanes Dennis, Katrina, and Ivan in a central Gulf Coast state. This paper identifies the key variables in predicting hurricane-induced outage durations and their degree of influence on predicting outage restoration for the utility company service area used as our case study.     

Socioeconomic vulnerability and electric power restoration timelines in Florida: the case of Hurricane Irma

Large-scale damage to the power infrastructure from hurricanes and high-wind events can have devastating ripple effects on infrastructure, the broader economy, households, communities, and regions. Using Hurricane Irma’s impact on Florida as a case study, we examined: (1) differences in electric power outages and restoration rates between urban and rural counties; (2) the duration of electric power outages in counties exposed to tropical storm force winds versus hurricane Category 1 force winds; and (3) the relationship between the duration of power outage and socioeconomic vulnerability. We used power outage data for the period September 9, 2017–September 29, 2017. At the peak of the power outages following Hurricane Irma, over 36% of all accounts in Florida were without electricity. We found that the rural counties, predominantly served by rural electric cooperatives and municipally owned utilities, experienced longer power outages and much slower and uneven restoration times. Results of three spatial lag models show that large percentages of customers served by rural electric cooperatives and municipally owned utilities were a strong predictor of the duration of extended power outages. There was also a strong positive association across all three models between power outage duration and urban/rural county designation. Finally, there is positive spatial dependence between power outages and several social vulnerability indicators. Three socioeconomic variables found to be statistically significant highlight three different aspects of vulnerability to power outages: minority groups, population with sensory, physical and mental disability, and economic vulnerability expressed as unemployment rate. The findings from our study have broader planning and policy relevance beyond our case study area, and highlight the need for additional research to deepen our understanding of how power restoration after hurricanes contributes to and is impacted by the socioeconomic vulnerabilities of communities.     

A GIS-based approach for gully erosion susceptibility modelling: a test in Sicily, Italy

The aim of this study is to analyze the susceptibility conditions to gully erosion phenomena in the Magazzolo River basin and to test a method that allows for driving the factors selection. The study area is one of the largest (225 km²) watershed of southern Sicily and it is mostly characterized by gentle slopes carved into clayey and evaporitic sediments, except for the northern sector where carbonatic rocks give rise to steep slopes. In order to obtain a quantitative evaluation of gully erosion susceptibility, statistical relationships between the spatial distributions of gullies affecting the area and a set of twelve environmental variables were analyzed. Stereoscopic analysis of aerial photographs dated 2000, and field surveys carried out in 2006, allowed us to map about a thousand landforms produced by linear water erosion processes, classifiable as ephemeral and permanent gullies. The linear density of the gullies, computed on each of the factors classes, was assumed as the function expressing the susceptibility level of the latter. A 40-m digital elevation model (DEM) prepared from 1:10,000-scale topographic maps was used to compute the values of nine topographic attributes (primary: slope, aspect, plan curvature, profile curvature, general curvature, tangential curvature; secondary: stream power index; topographic wetness index; LS-USLE factor); from available thematic maps and field checks three other physical attributes (lithology, soil texture, land use) were derived. For each of these variables, a 40-m grid layer was generated, reclassifying the topographic variables according to their standard deviation values. In order to evaluate the controlling role of the selected predictive variables, one-variable susceptibility models, based on the spatial relationships between each single factor and gullies, were produced and submitted to a validation procedure. The latter was carried out by evaluating the predictive performance of models trained on one half of the landform archive and tested on the other. Large differences of accuracy were verified by computing geometric indexes of the validation curves (prediction and success rate curves; ROC curves) drawn for each one-variable model; in particular, soil texture, general curvature and aspect demonstrated a weak or a null influence on the spatial distribution of gullies within the studied area, while, on the contrary, tangential curvature, stream power index and plan curvature showed high predictive skills. Hence, predictive models were produced on a multi-variable basis, by variously combining the one-variable models. The validation of the multi-variables models, which generally indicated quite satisfactory results, were used as a sensitivity analysis tool to evaluate differences in the prediction results produced by changing the set of combined physical attributes. The sensitivity analysis pointed out that by increasing the number of combined environmental variables, an improvement of the susceptibility assessment is produced; this is true with the exception of adding to the multi-variables models a variable, as slope aspect, not correlated to the target variable. The addition of this attribute produces effects on the validation curves that are not distinguishable from noise and, as a consequence, the slope aspect was excluded from the final multi-variables model used to draw the gully erosion susceptibility map of the Magazzolo River basin. In conclusion, the research showed that the validation of one-variable models can be used as a tool for selecting factors to be combined to prepare the best performing multi-variables gully erosion susceptibility model.     

Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study

Four statistical techniques for modelling landslide susceptibility were compared: multiple logistic regression (MLR), multivariate adaptive regression splines (MARS), classification and regression trees (CART), and maximum entropy (MAXENT). According to the literature, MARS and MAXENT have never been used in landslide susceptibility modelling, and CART has been used only twice. Twenty independent variables were used as predictors, including lithology as a categorical variable. Two sets of random samples were used, for a total of 90 model replicates (with and without lithology, and with different proportions of positive and negative data). The model performance was evaluated using the area under the receiver operating characteristic curve (AUC) statistic. The main results are (a) the inclusion of lithology improves the model performance; (b) the best AUC values for single models are MLR (0.76), MARS (0.76), CART (0.77), and MAXENT (0.78); (c) a smaller amount of negative data provides better results; (d) the models with the highest prediction capability are obtained with MAXENT and CART; and (e) the combination of different models is a way to evaluate the model reliability. We further discuss some key issues in landslide modelling, including the influence of the various methods that we used, the sample size, and the random replicate procedures.     

Providing optimum models for predicting the water loss after harvesting and extracting trees aboveground biomass from the Hyrcanian mixed-beech forests

The ecological and biological attributes of trees stand as well as the water cycle in forests are substantially related to variations of water storage capacity in forest ecosystems. This study aimed to figure out a protocol for monitoring the water storage capacity variations in the Hyrcanian mixed-beech stands after harvesting and extracting trees form the forest. A total of 174 trees were felled and weighed, and destructive sampling following lines of exploitation was carried out for measuring the water content in aboveground biomass of trees. Curve estimation regression analyses including the tree biophysical variables (breast height diameter DBH, total height H, basic wood density \(\rho\)) were used for examining the prediction accuracy. Nonlinear models were log-transformed, and systematic bias was corrected by correction factor depending on standard error of estimate when back-transforming to the originally dependent value. The findings showed that the power-law models were the best functional form for predicting the dependent variables. Using only DBH in the simple power model explained 76% of total variance (Adj.R ² = 0.76) with a low Akaike information criterion (AIC) and normalized root-mean-square error RMSE%, indicating a high accuracy of prediction (\({\text{AIC}} \approx - 238\); RMSE% = 11.7). Adding H and \(\rho\) in the linear log-transformed power models with different interaction terms increased the certainty of prediction with the highest accuracy (\({\text{Adj}}.R^{2} = 0.86,\;{\text{AIC}} = - 329,\;{\text{RMSE}}\% = 9\)). Considering diverse conditions for natural forest sites, the optimum models including the biophysical variables may have associated parameters in the other forests having different stand types and compositions.     

A comparative study on the landslide susceptibility mapping using logistic regression and statistical index models

The logistic regression and statistical index models are applied and verified for landslide susceptibility mapping in Daguan County, Yunnan Province, China, by means of the geographic information system (GIS). A detailed landslide inventory map was prepared by literatures, aerial photographs, and supported by field works. Fifteen landslide-conditioning factors were considered: slope angle, slope aspect, curvature, plan curvature, profile curvature, altitude, STI, SPI, and TWI were derived from digital elevation model; NDVI was extracted from Landsat ETM7; rainfall was obtained from local rainfall data; distance to faults, distance to roads, and distance to rivers were created from a 1:25,000 scale topographic map; the lithology was extracted from geological map. Using these factors, the landslide susceptibility maps were prepared by LR and SI models. The accuracy of the results was verified by using existing landslide locations. The statistical index model had a predictive rate of 81.02%, which is more accurate prediction in comparison with logistic regression model (80.29%). The models can be used to land-use planning in the study area.     

Assessment of the hurricane-induced power outages from a demographic,socioeconomic, and transportation perspective

Natural disasters have devastating effects on the infrastructure and disrupt every aspect of daily life in the regions they hit. To alleviate problems caused by these disasters, first an impact assessment is needed. As such, this paper focuses on a two-step methodology to identify the impact of Hurricane Hermine on the City of Tallahassee, the capital of Florida. The regional and socioeconomic variations in the Hermine’s impact were studied via spatially and statistically analyzing power outages. First step includes a spatial analysis to illustrate the magnitude of customers affected by power outages together with a clustering analysis. This step aims to determine whether the customers affected from outages are clustered or not. Second step involves a Bayesian spatial autoregressive model in order to identify the effects of several demographic-, socioeconomic-, and transportation-related variables on the magnitude of customers affected by power outages. Results showed that customers affected by outages are spatially clustered at particular regions rather than being dispersed. This indicates the need to pinpoint such vulnerable locations and develop strategies to reduce hurricane-induced disruptions. Furthermore, the increase in the magnitude of affected customers was found to be associated with several variables such as the power network and total generated trips as well as the demographic factors. The information gained from the findings of this study can assist emergency officials in identifying critical and/or less resilient regions, and determining those demographic and socioeconomic groups which were relatively more affected by the consequences of hurricanes than others.     

Linking multi-temporal analysis and community consultation to evaluate the response to the impact of Hurricane Stan in coffee areas of Chiapas,Mexico

This paper analyses the responses related to land use of coffee growers in Chiapas, Mexico to the impact of Hurricane Stan (October 2005). A multi-temporal analysis of the effect on land cover was performed through the combination of unsupervised classification of SPOT multispectral images and visual interpretation of panchromatic images (8 months previous to the hurricane, and 2, 14, and 40 months after the hurricane). The information provided by this geographic analysis was interpreted in light of information gathered though household surveys. Although the hurricane wrecked havoc across the region, the main impact in the study area was in the riparian zones where the extent of the loss experienced in terms of coffee harvest and soil was such that, even 14 months after the event, households with land in those areas were struggling to recover. Nevertheless, after 40 months, the zones that had suffered total soil loss began to support soil and vegetation, indicating the possibility of replanting coffee in those areas. Although the hurricane occurred when the coffee sector was particularly fragile as a result of the preceding several years of poor prices, the impact did not trigger extensive land use change. The surveys showed, however, that people are now more informed of the risk of living and farming on the river margins and are now performing soil conservation practices and planting trees to reduce risk.     

Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco,Italy

This study deals with landslide susceptibility mapping in the northern part of Lecco Province, Lombardy Region, Italy. In so doing, a valid landslide inventory map and thirteen predisposing factors (including elevation, slope aspect, slope degree, plan curvature, profile curvature, distance to waterway, distance to road, distance to fault, soil type, land use, lithology, stream power index, and topographic wetness index) form the spatial database within geographic information system. The used predictive models comprise a bivariate statistical approach called frequency ratio (FR) and two machine learning tools, namely multilayer perceptron neural network (MLPNN) and adaptive neuro-fuzzy inference system (ANFIS). These models first use landslide and non-landslide records for comprehending the relationship between the landslide occurrence and predisposing factors. Then, landslide susceptibility values are predicted for the whole area. The accuracy of the produced susceptibility maps is measured using area under the curve (AUC) index, according to which, the MLPNN (AUC?=?0.916) presented the most accurate map, followed by the ANFIS (AUC?=?0.889) and FR (AUC?=?0.888). Visual interpretation of the susceptibility maps, FR-based correlation analysis, as well as the importance assessment of predisposing factors, all indicated the significant contribution of the road networks to the crucial susceptibility of landslide. Lastly, an explicit predictive formula is extracted from the implemented MLPNN model for a convenient approximation of landslide susceptibility value.

     

GIS-based spatial prediction of debris flows using logistic regression and frequency ratio models for Zêzere River basin and its surrounding area,Northwest Covilhã, Portugal

Landslide susceptibility mapping (LSM) is important for catastrophe management in the mountainous regions. They focus on generating susceptibility maps beginning from landslide inventories and considering the main predisposing parameters. The aim of this study was to assess the susceptibility of the occurrence of debris flows in the Zêzere River basin and its surrounding area using logistic regression (LR) and frequency ratio (FR) models. To achieve this, a landslide inventory map was created using historical information, satellite imagery, and extensive field works. One hundred landslides were mapped, of which 75% were randomly selected as training data, while the remaining 25% were used for validating the models. The landslide influence factors considered for this study were lithology, elevation, slope gradient, slope aspect, plan curvature, profile curvature, normalized difference vegetation index (NDVI), distance to roads, topographic wetness index (TWI), and stream power index (SPI). The relationships between landslide occurrence and these factors were established, and the results were then evaluated and validated. Validation results show that both methods give acceptable results [the area under curve (AUC) of success rates is 83.71 and 76.38 for LR and FR, respectively]. Furthermore, the AUC results for prediction accuracy revealed that LR model has the highest predictive performance (AUC of predicted rate?=?80.26). Hence, it is concluded that the two models showed reasonably good accuracy in predicting the landslide susceptibility in the study area. These two models have the potential to aid planners in development and land-use planning and to offer tools for hazard mitigation measures.     

Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-ri Creek,South Korea

The objective of this study was to produce and evaluate a landslide susceptibility map for weathered granite soils in Deokjeok-ri Creek, South Korea. The relative effect (RE) method was used to determine the relationship between landslide causative factors (CFs) and landslide occurrence. To determine the effect of CFs on landslides, data layers of aspect, elevation, slope, internal relief, curvature, distance to drainage, drainage density, stream power index, sediment transport index, topographic wetness index, soil drainage character, soil type, soil depth, forest type, timber age, and geology were analyzed in a geographical information system (GIS) environment. A GIS-based landslide inventory map of 748 landslide locations was prepared using data from previous reports, aerial photographic interpretation, and extensive field work. A RE model was generated from a training set consisting of 673 randomly selected landslides in the inventory map, with the remaining 75 landslides used for validation of the susceptibility map. The results of the analysis were verified using the landslide location data. According to the analysis, the RE model had a success rate of 86.3 % and a predictive accuracy of 88.6 %. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations. The results of this study can therefore be used to mitigate landslide-induced hazards and to plan land use.     

Dynamic analysis of emergency inter-organizational communication network under public health emergency: a case study of COVID-19 in Hubei Province of China

Storm surge induced by hurricane is a major threat to the Gulf Coasts of the United States. A numerical modeling study was conducted to simulate the storm surge during Hurricane Michael, a category 5 hurricane that landed on the Florida Panhandle in 2018. A high-resolution model mesh was used in the ADCIRC hydrodynamic model to simulate storm surge and tides during the hurricane. Two parametric wind models, Holland 1980 model and Holland 2010 model, have been evaluated for their effects on the accuracy of storm surge modeling by comparing simulated and observed maximum water levels along the coast. The wind model parameters are determined by observed hurricane wind and pressure data. Results indicate that both Holland 1980 and Holland 2010 wind models produce reasonable accuracy in predicting maximum water level in Mexico Beach, with errors between 1 and 3.7%. Comparing to the observed peak water level of 4.74 m in Mexico Beach, Holland 1980 wind model with radius of 64-knot wind speed for parameter estimation results in the lowest error of 1%. For a given wind model, the wind profiles are also affected by the wind data used for parameter estimation. Away from hurricane eye wall, using radius of 64-knot wind speed for parameter estimation generally produces weaker wind than those using radius of 34-knot wind speed for parameter estimation. Comparing model simulated storm tides with 17 water marks observed along the coast, Holland 2010 wind model using radius of 34-knot wind speed for parameter estimation leads to the minimum mean absolute error. The results will provide a good reference for researchers to improve storm surge modeling. The validated model can be used to support coastal hazard mitigation planning.

     

Evaluation of TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis)’s predictive skill for hurricane-triggered landslides: a case study in Macon County,North Carolina

The key to advancing the predictability of rainfall-triggered landslides is to use physically based slope-stability models that simulate the transient dynamical response of the subsurface moisture to spatiotemporal variability of rainfall in complex terrains. TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis) is a USGS landslide prediction model, coded in Fortran, that accounts for the influences of hydrology, topography, and soil physics on slope stability. In this study, we quantitatively evaluate the spatiotemporal predictability of a Matlab version of TRIGRS (MaTRIGRS) in the Blue Ridge Mountains of Macon County, North Carolina where Hurricanes Ivan triggered widespread landslides in the 2004 hurricane season. High resolution digital elevation model (DEM) data (6-m LiDAR), USGS STATSGO soil database, and NOAA/NWS combined radar and gauge precipitation are used as inputs to the model. A local landslide inventory database from North Carolina Geological Survey is used to evaluate the MaTRIGRS’ predictive skill for the landslide locations and timing, identifying predictions within a 120-m radius of observed landslides over the 30-h period of Hurricane Ivan’s passage in September 2004. Results show that within a radius of 24 m from the landslide location about 67% of the landslide, observations could be successfully predicted but with a high false alarm ratio (90%). If the radius of observation is extended to 120 m, 98% of the landslides are detected with an 18% false alarm ratio. This study shows that MaTRIGRS demonstrates acceptable spatiotemporal predictive skill for landslide occurrences within a 120-m radius in space and a hurricane-event-duration (h) in time, offering the potential to serve as a landslide warning system in areas where accurate rainfall forecasts and detailed field data are available. The validation can be further improved with additional landslide information including the exact time of failure for each landslide and the landslide’s extent and run out length.     

Precipitation forecasting using classification and regression trees (CART) model: a comparative study of different approaches

Interest in semiarid climate forecasting has prominently grown due to risks associated with above average levels of precipitation amount. Longer-lead forecasts in semiarid watersheds are difficult to make due to short-term extremes and data scarcity. The current research is a new application of classification and regression trees (CART) model, which is rule-based algorithm, for prediction of the precipitation over a highly complex semiarid climate system using climate signals. We also aimed to compare the accuracy of the CART model with two most commonly applied models including time series modeling (ARIMA), and adaptive neuro-fuzzy inference system (ANFIS) for prediction of the precipitation. Various combinations of large-scale climate signals were considered as inputs. The results indicated that the CART model had a better results (with Nash–Sutcliffe efficiency, NSE?>?0.75) compared to the ANFIS and ARIMA in forecasting precipitation. Also, the results demonstrated that the ANFIS method can predict the precipitation values more accurately than the time series model based on various performance criteria. Further, fall forecasts ranked “very good” for the CART method, while the ANFIS and the time series model approximately indicated “satisfactory” and “unsatisfactory” performances for all stations, respectively. The forecasts from the CART approach can be helpful and critical for decision makers when precipitation forecast heralds a prolonged drought or flash flood.     

Landslide susceptibility mapping using random forest,boosted regression tree,classification and regression tree,and general linear models and comparison of their performance at Wadi Tayyah Basin,Asir Region,Saudi Arabia

The purpose of the current study is to produce landslide susceptibility maps using different data mining models. Four modeling techniques, namely random forest (RF), boosted regression tree (BRT), classification and regression tree (CART), and general linear (GLM) are used, and their results are compared for landslides susceptibility mapping at the Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslide locations were identified and mapped from the interpretation of different data types, including high-resolution satellite images, topographic maps, historical records, and extensive field surveys. In total, 125 landslide locations were mapped using ArcGIS 10.2, and the locations were divided into two groups; training (70 %) and validating (25 %), respectively. Eleven layers of landslide-conditioning factors were prepared, including slope aspect, altitude, distance from faults, lithology, plan curvature, profile curvature, rainfall, distance from streams, distance from roads, slope angle, and land use. The relationships between the landslide-conditioning factors and the landslide inventory map were calculated using the mentioned 32 models (RF, BRT, CART, and generalized additive (GAM)). The models’ results were compared with landslide locations, which were not used during the models’ training. The receiver operating characteristics (ROC), including the area under the curve (AUC), was used to assess the accuracy of the models. The success (training data) and prediction (validation data) rate curves were calculated. The results showed that the AUC for success rates are 0.783 (78.3 %), 0.958 (95.8 %), 0.816 (81.6 %), and 0.821 (82.1 %) for RF, BRT, CART, and GLM models, respectively. The prediction rates are 0.812 (81.2 %), 0.856 (85.6 %), 0.862 (86.2 %), and 0.769 (76.9 %) for RF, BRT, CART, and GLM models, respectively. Subsequently, landslide susceptibility maps were divided into four classes, including low, moderate, high, and very high susceptibility. The results revealed that the RF, BRT, CART, and GLM models produced reasonable accuracy in landslide susceptibility mapping. The outcome maps would be useful for general planned development activities in the future, such as choosing new urban areas and infrastructural activities, as well as for environmental protection.     

Landslide prediction from machine learning

Predicting where and when landslides are likely to occur in a specific region of interest remains a key challenge in natural hazards research and mitigation. While the basic mechanics of slope‐failure initiation and runout can be cast into physical and numerical models, a scarcity of sufficiently detailed and real‐time measurements of soil, rock‐mass and groundwater conditions prohibits accurate landslide forecasting. Researchers are therefore increasingly exploring multivariate data analysis techniques from the fields of data mining or machine learning in order to approximate future occurrences of landslides from past distribution patterns. This work has elucidated patterns of spatial susceptibility, but temporal forecasts have remained largely empirical. Most machine learning techniques achieve overall success rates of 75–95 percent. Whilst this may seem very promising, issues remain with data input quality, potential overfitting and commensurate inadequate choice of prediction models, inadvertent inclusion of redundant or noise variables, and technical limits to predicting only certain types and sizes of landslides. Simpler models provide only slightly inferior predictions to more complex models, and should guide the way for a more widespread application of data mining in regional landslide prediction. This approach should especially be communicated to planners and decision makers. Future research may want to develop: (1) further best‐practice guidelines for model selection; (2) predictions of occurrence and runout of large slope failures at the regional scale; and (3) temporal forecasts of landslides.     

Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models

This paper presents landslide hazard analysis at Cameron area, Malaysia, using a geographic information system (GIS) and remote sensing data. Landslide locations were identified from interpretation of aerial photographs and field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence are topographic slope, topographic aspect, topographic curvature, and distance to rivers, all from the topographic database; lithology and distance to faults were taken from the geologic database; land cover from TM satellite image; the vegetation index value was taken from Landsat images; and precipitation distribution from meteorological data. Landslide hazard area was analyzed and mapped using the landslide occurrence factors by frequency ratio and bivariate logistic regression models. The results of the analysis were verified using the landslide location data and compared with the probabilistic models. The validation results showed that the frequency ratio model (accuracy is 89.25%) is better in prediction of landslide than bivariate logistic regression (accuracy is 85.73%) model.     

Spatial prediction of urban landslide susceptibility based on topographic factors using boosted trees

As global warming accelerates, abnormal weather events are occurring more frequently. In the twenty-first century in particular, hydrological disruption has increased as water flows have changed globally, causing the strength and frequency of hydrological disasters to increase. The damage caused by such disasters in urban areas can be extreme, and the creation of landslide susceptibility maps to predict and analyze the extent of future damage is an urgent necessity. Therefore, in this study, probabilistic and data mining approaches were utilized to identify landslide-susceptible areas using aerial photographs and geographic information systems. Areas where landslides have occurred were located through interpretation of aerial photographs and field survey data. In addition, topographic maps generated from aerial photographs were used to determine the values of topographic factors. A frequency ratio (FR) model was utilized to examine the influences of topographic, soil and vegetation factors on the occurrence of landslides. A total of 23 variables that affect landslide frequency were selected through FR analysis, and a spatial database was constructed. Finally, a boosted tree model was applied to determine the correlations between various factors and landslide occurrence. Correlations among related input variables were calculated as predictor importance values, and sensitivity analysis was performed to quantitatively analyze the impact of each variable. The boosted tree model showed validation accuracies of 77.68 and 78.70% for the classification and regression algorithms using receiver operating characteristic curve, respectively. Reliable accuracy can provide a scientific basis to urban municipalities for policy recommendations in the management of urban landslides.     

A multiple scale modeling system for coastal hurricane wind damage mitigation

Hurricane wind damage constitutes the largest percentage of catastrophic insured losses in the US. Yet the complicated wind structures and their changes are not fully understood and, thus, have not been considered in current risk catastrophic models. To obtain realistic landfall hurricane surface winds, a large eddy simulation (LES) framework in a weather forecasting mode has been developed from a multiple nested Weather Research & Forecasting (WRF) model to explicitly simulate a spectrum of scales from large-scale background flow, hurricane vortex, mesoscale organizations, down to fine-scale turbulent eddies in a unified system. The unique WRF-LES enables the high resolution data to be generated in a realistic environment as a hurricane evolves. In this paper, a simulation of the landfalling Hurricane Katrina is presented to demonstrate various features of the WRF-LES. It shows that the localized damaging winds are caused by the large eddy circulations generated in the hurricane boundary layer. With a sufficient computational power, WRF-LES has the potential to be developed into the next generation operational public wind-field model for hurricane wind damage mitigation.     

吸力对弱膨胀土强度贡献的试验研究与预测分析

对部分应用土-水特征曲线来预测非饱和土抗剪强度的公式进行了归纳分析。应用压力板仪与非饱和三轴仪,测试了荆门原状弱膨胀土的土-水特征曲线和控制吸力的非饱和三轴抗剪强度参数,并将试验结果与各抗剪强度公式的预测值进行对比,分析了各强度公式的局限性。试验结果表明,非饱和原状膨胀土的净法向应力摩擦角随着吸力的不同而变化,根据双应力变量理论确定的吸力对强度的贡献与围压有关,不同围压下吸力对强度的贡献不同,表观凝聚力 与吸力间符合乘幂函数关系。