Disaster Advances

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Disaster Advances

Evaluation of Seismic Hazard Parameters for Bangalore Region in South India

Anbazhagan P. 1*, Vinod J. S. 2 and Sitharam T.G. 1

In this paper, seismic hazard parameters are evaluated and presented for Bangalore region following the different methods such as Gutenberg-Richter (G-R) recurrence relation and maximum likelihood procedure and data sets. The seismic data have been collected from various sources for area covering a radius of 350 km around Bangalore. A complete analysis has been carried out using the method as proposed by Stepp27. From the analysis it was found that the seismic data is homogenous for the last four decades irrespective of magnitude. The value of seismic hazard parameter “b” was estimated for complete data by using G-R relation. Completed data do not include the maximum reported magnitudes of 5 and above in this region. Hence b value has been evaluated by considering mixed data magnitude range of 3.5 to 6.2 and 4 to 6.2 using Gutenberg–Richter6 recurrence relation. In addition seismic hazard parameters such as, “b” of the magnitude- frequency relationship, R the mean return period and Mmax maximum regional magnitude is evaluated based on maximum likelihood procedure. It has been observed that the comparative analysis using complete and mixed data, gives comparable values. The “b” values presented in this paper are higher than the earlier reported values.

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Capacity Building for Disaster Prevention in Vulnerable Regions of the World: Development of a Prototype Global Flood/Landslide Prediction System

Hong Yang 1, 2*, Adler Robert F. 2, Bach Dalia 3 and Huffman George 4

With the availability of satellite rainfall analyses at fine time and space resolution, it is now possible to develop a global flood/landslide identifica­tion/prediction system for the most vulnerable regions by combining real-time satellite observations with a database of global terrestrial characteristics. This presentation describes a prototype research frame­work, Global Hazard System (GHS), recently developed by a NASA-OU research partner group. Key components of the GHS-Flood framework are: (a) a fine resolution precipitation acquisition system derived from multi-satellites; (b) a characterization of land surface including digital elevation from NASA SRTM (Shuttle Radar Terrain Mission), topography-derived hydrologic parameters such as flow direction, flow accumulation, basin and river network etc.; (c) a hydrological model to infiltrate rainfall and route overland runoff; and (d) an implementation interface to relay the input data to the models and display the flood inundation results to potential users and decision-makers. In terms of GHS-Landslide, the satellite rainfall information is combined with a global landslide susceptibility map derived from a combination of global surface characteristics (digital elevation topography, slope, soil types, soil texture and land cover classification etc.) using a weighted linear combination approach. In those areas identified as “susceptible” (based on the surface charac­teristics), landslides are forecast where and when a rainfall intensity/duration threshold is exceeded. The GHS (trial version) has been running at near real-time in an effort to offer a practical cost-effective solution to the ultimate challenge of building natural disaster early warning systems for the data-sparse regions of the world (http://trmm.gsfc. nasa. gov). The interactive GHS website shows close-up maps of the flood/landslide risks overlaid on topography/population or integrated with the Google-Earth visualization tool. One additional capability, which extends forecast lead-time by assimilating the satellite rainfall into the Global Forecast System-QPF, also will be implemented in the future.

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Support Vector Machine for Evaluating Seismic Liquefaction Potential Using Standard Penetration Test

Samui Pijush

Liquefaction of sandy soils during earthquakes causes large amount of damage to buildings, highway embankments, retaining structures as well as other civil engineering structures. So the determination of liquefaction potential due to an earthquake is an imperative task in geotechnical earthquake engineering. This paper examines the potential of support vector machines for prediction of liquefaction based on standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake by developing two models. SVM achieves good generalization ability by adopting a structural risk minimization (SRM) induction principle that aims at minimizing a bound on the generalization error of a model rather than minimizing the error on the training data only. In MODEL I, cyclic stress ratio (CSR) vs SPT value (N) value is trained for prediction of liquefaction. In MODEL II, this is further simplified by relating normalized maximum horizontal acceleration (amax/g) vs N for prediction of liquefaction. Further, the generalization capability of the MODEL II has been examined by different case histories available globally. Equations have been also developed to determine the soil condition during an earthquake for MODEL I and MODEL II. The study indicates that SVM can successfully model the complex relationship between seismic parameters, soil parameters and the liquefaction potential.

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Comparison between Prediction Capabilities of Neural Network and Fuzzy Logic Techniques for L and Slide Susceptibility Mapping

Pradhan Biswajeet 1,2* and Pirasteh Saied 2

Preparation of L and slide susceptibility maps is important for engineering geologists and geomorphologists. However, due to complex nature of L and slides, producing a reliable susceptibility map is not easy. In recent years, various data mining and soft computing techniques are getting popular for the prediction and classification of L and slide susceptibility and hazard mapping. This paper presents a comparative analysis of the prediction capabilities between the neural network and fuzzy logic model for L and slide susceptibility mapping in a geographic information system (GIS) environment. In the first stage, L and slide-related factors such as altitude, slope angle, slope aspect, distance to drainage, distance to road, lithology and normalized difference vegetation index (ndvi) were extracted from topographic and geology and soil maps. Secondly, L and slide locations were identified from the interpretation of aerial photographs, high resolution satellite imageries and extensive field surveys. Then L and slide-susceptibility maps were produced by the application of neural network and fuzzy logic approahc using the aforementioned L and slide related factors. Finally, the results of the analyses were verified using the L and slide location data and compared with the neural network and fuzzy logic models. The validation results showed that the neural network model (accuracy is 88%) is better in prediction than fuzzy logic (accuracy is 84%) models. Results show that “gamma” operator (l = 0.9) showed the best accuracy (84%) while “or” operator showed the worst accuracy (66%).

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Using Radar Data to extend the Lead Time of Neural Network Forecasting on the River Ping

Chaipimonplin Tawee,1,2 See Linda M.* 2 and Kneale Pauline E. 3

Neural networks (NNs) and other data-driven methods are appearing with increasing frequency in the literature for the prediction of river levels or flows. Many of these data-driven models are tested on short lead times where they perform very well. There have been much fewer documented attempts at predicting floods at longer, more useful lead times from a flood warning and civil protection perspective. In this paper NN flood forecasting models for the Upper Ping catchment at Chiang Mai are developed. Simple input determination methods are used to automate the process of which inputs to select for inclusion in the model. Lead times of 6, 12 and 18 hours are tested. Radar data inputs are then added to these NN models to see whether the lead time of the prediction can be increased. The models without radar data show reasonable forecasting ability up to 18 hours ahead but the addition of radar extends the lead times up to 36 hours ahead for the prediction of the rising limb of the hydrograph and the flood peak.

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Latest Developments of Windshear Alerting Services at the Hong Kong International Airport

Chan P.W.

Low-level windshear and turbulence are known to cause hazard to the operation of the aircraft. During the operation of the Hong Kong International Airport (HKIA) in the last 10 years or so, the detect­ion and alerting of low-level windshear has been continuously enhanced by the Hong Kong Obse­r­v­a­tory, including the introduction of sophisticated instrumen­tation and detection algorithms to alert pilots of the presence of low-level windshear and the use of more objective data in the development and verification of the windshear alerting services. This paper summarizes these development efforts in the recent years. On the detection side, following the deployment of two LIDARs, one for each of the two runways, the LIDAR Windshear Alerting System (LIWAS) has been enhanced for the alerting of windshear over the departure runway corridors in addition to the arrival runway corridors. A micro­wave radiometer has also been set up at the airport to detect temperature changes in the boundary layer of the atmosphere in association with terrain-disrupted airflow. Moreover, a short-range LIDAR is being tested in the detection of small-scale wind distur­bances arising from buildings at the airport. Studies are also conducted in the calculation of turbulence intensity metric based on LIDAR and Terminal Doppler Weather Radar (TDWR) data. On the data collection side, sophisticated algorithm has been dev­e­lo­ped in collaboration with the National Aerospace Laboratory in the Netherlands in the processing of Quick Access Recorder (QAR) data from commercial jets in establishing a more objective database of windshear and turbulence based on the flight data.

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A 3D Fire Spread Model Implementing Vegetation Combustion applicable to Mountainous Forest Fires

Boboulos Miltiadis A.

Biomass combustion can be modelled on the governing equations of mass conservation both for the gaseous phase and the solid fuel. Current modelling examined the applicability of various chemical reactions describing dense surface layer combustion, the production of volatiles, char and tar and their subsequent oxidation. It employed an analytical five-step chemical reaction mechanism EDM + Kinetics to describe the mixing effect and the chemical reactions. Applying the litter combustion model into a realistic 3D domain implemented the vegetation combustion mechanisms producing behaviour accounting. The process can be evaluated on the characteristics and the range of its main parameters – temperature field and its distribution, velocity field, the biomass energy contents and products, measured on the basis of expert knowledge. Numerical results were compared to integral methods such as fire behaviour prediction nomographs and controlled field experiments. The modelling was enhanced with model adjustments in the constants and boundary conditions implementing TGA results, experimental spread rates and IR camera measurements. The model determines the concentration of gas components in the fire zone in order to establish their effects on environment for various fire intensities and scale, the temperature - fluid flow distribution and flame characteristics.

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