Development of flood risk map at Kuantan using geographical information system

The purpose of this study is to develop the flood risk map in Kuantan. The heavy prolonged rainfall in Kuantan has showed an increasing flood disaster over a year. Geographical Information system (GIS) is integrated with AHP is used to generate the potential flood risk area. Flood map was develop by applying data Digital Elevation Model (DEM) and Landsat 8 download from United States Geological Survey (USGS) to generate the slope map and land use map. Soil type map was obtained from Department of Survey and Mapping (JUPEM) and rainfall intensity data from Department of Irrigation and Drainage (DID). Rainfall distribution map in study area was generated in ArcGIS software using Inverse Distance Weighted (IDW) interpolation method. The spatial analysis and AHP analysis was used to score and compute weights of each criteria. Score for each criteria based on judgement to the parameter risk level against flooding. Using AHP, the percentage derived from the parameters were land use type is 41.55%, slope 28.95%, rainfall 16.93% and soil type is 12.58%. The value of consistency ratio is 6.9% was acceptable and indicate the judgement for each parameter is consistent. Flood risk areas were generating using flood risk index calculation. The result shows that, land use change, slope degree, rainfall intensity and soil type have significant influences on the flood mapping. The flood risk map using AHP was matched to an actual flood mapping developed by DID in determining potential location of flooding

An integrated wind risk warning model for urban rail transport in Shanghai, China

The integrated wind risk warning model for rail transport presented has four elements: Background wind data, a wind field model, a vulnerability model, and a risk model. Background wind data uses observations in this study. Using the wind field model with effective surface roughness lengths, the background wind data are interpolated to a 30-m resolution grid. In the vulnerability model, the aerodynamic characteristics of railway vehicles are analyzed with CFD (Computational Fluid Dynamics) modelling. In the risk model, the maximum value of three aerodynamic forces is used as the criteria to evaluate rail safety and to quantify the risk level under extremely windy weather. The full model is tested for the Shanghai Metro Line 16 using wind conditions during Typhoon Chan-hom. The proposed approach enables quick quantification of real- time safety risk levels during typhoon landfall, providing sophisticated warning information for rail vehicle operation safety

A disposition of interpolation techniques

A large collection of interpolation techniques is available for application in environmental research. To help environmental scientists in choosing an appropriate technique a disposition is made, based on 1) applicability in space, time and space-time, 2) quantification of accuracy of interpolated values, 3) incorporation of ancillary information, and 4) incorporation of process knowledge. The described methods include inverse distance weighting, nearest neighbour methods, geostatistical interpolation methods, Kalman filter methods, Bayesian Maximum Entropy methods, etc. The applicability of methods in aggregation (upscaling) and disaggregation (downscaling) is discussed. Software for interpolation is described. The application of interpolation techniques is illustrated in two case studies: temporal interpolation of indicators for ecological water quality, and spatio-temporal interpolation and aggregation of pesticide concentrations in Dutch surface waters. A valuable next step will be to construct a decision tree or decision support system, that guides the environmental scientist to easy-to-use software implementations that are appropriate to solve their interpolation problem. Validation studies are needed to assess the quality of interpolated values, and the quality of information on uncertainty provided by the interpolation method

Landslide risk management through spatial analysis and stochastic prediction for territorial resilience evaluation

Natural materials, such as soils, are influenced by many factors acting during their formative and evolutionary process: atmospheric agents, erosion and transport phenomena, sedimentation conditions that give soil properties a non-reducible randomness by using sophisticated survey techniques and technologies. This character is reflected not only in spatial variability of properties which differs from point to point, but also in multivariate correlation as a function of reciprocal distance. Cognitive enrichment, offered by the response of soils associated with their intrinsic spatial variability, implies an increase in the evaluative capacity of the contributing causes and potential effects in failure phenomena. Stability analysis of natural slopes is well suited to stochastic treatment of uncertainty which characterized landslide risk. In particular, this study has been applied through a back- analysis procedure to a slope located in Southern Italy that was subject to repeated phenomena of hydrogeological instability (extended for several kilometres in recent years). The back-analysis has been carried out by applying spatial analysis to the controlling factors as well as quantifying the hydrogeological hazard through unbiased estimators. A natural phenomenon, defined as stochastic process characterized by mutually interacting spatial variables, has led to identify the most critical areas, giving reliability to the scenarios and improving the forecasting content. Moreover, the phenomenological characterization allows the optimization of the risk levels to the wide territory involved, supporting decision-making process for intervention priorities as well as the effective allocation of the available resources in social, environmental and economic contexts

SOCIO-ECONOMIC AND ENVIRONMENTAL ASPECTS OF FARMING PRACTICES IN THE PERI-URBAN HINTERLANDS OF NEPAL

Spatial location of the farm households shapes farming practices and livelihoods of the farmers. Many socio-economic variables have strong spatial relations that would otherwise be missed by data aggregation at household level. Geographic Information System (GIS)provides display and analysis of socio-economic data that may be fundamental for many social scientists to understand socio-economic reality influenced by geographical position of the farm households. Present article aims at integrating socio-economic data into GIS environment to examine spatial relation in the resource availability and use employing spatial and random sampling techniques. Result demonstrates the variation in the socioeconomic attributes along the spatial gradient which is mainly related to the infrastructures such as road, market and improved agro-inputs. While households with better access to these infrastructures have tendency to use more agro-chemicals, have larger family, land holding and livestock units, better off-farm opportunities, commercial farming orientation and hence higher family income; opposite is true for the households with poor access to these infrastructures. Peri-urban farmlands, wherever agro-chemicals are applied imprudently, faces the problems of agro-ecological degradation while rural subsistence farming faces the problem of spatial poverty

Stochastic estimation of hydraulic transmissivity fields using flow connectivity indicator data

This is the peer reviewed version of the following article: [Freixas, G., D. Fernàndez-Garcia, and X. Sanchez-Vila (2017), Stochastic estimation of hydraulic transmissivity fields using flow connectivity indicator data, Water Resour. Res., 53, 602–618, doi:10.1002/2015WR018507], which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/2015WR018507/abstract. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Most methods for hydraulic test interpretation rely on a number of simplified assumptions regarding the homogeneity and isotropy of the underlying porous media. This way, the actual heterogeneity of any natural parameter, such as transmissivity ( math formula), is transferred to the corresponding estimates in a way heavily dependent on the interpretation method used. An example is a long-term pumping test interpreted by means of the Cooper-Jacob method, which implicitly assumes a homogeneous isotropic confined aquifer. The estimates obtained from this method are not local values, but still have a clear physical meaning; the estimated math formula represents a regional-scale effective value, while the log-ratio of the normalized estimated storage coefficient, indicated by math formula, is an indicator of flow connectivity, representative of the scale given by the distance between the pumping and the observation wells. In this work we propose a methodology to use math formula, together with sampled local measurements of transmissivity at selected points, to map the expected value of local math formula values using a technique based on cokriging. Since the interpolation involves two variables measured at different support scales, a critical point is the estimation of the covariance and crosscovariance matrices. The method is applied to a synthetic field displaying statistical anisotropy, showing that the inclusion of connectivity indicators in the estimation method provide maps that effectively display preferential flow pathways, with direct consequences in solute transport.Peer ReviewedPostprint (published version

Fast DD-classification of functional data

A fast nonparametric procedure for classifying functional data is introduced. It consists of a two-step transformation of the original data plus a classifier operating on a low-dimensional hypercube. The functional data are first mapped into a finite-dimensional location-slope space and then transformed by a multivariate depth function into the $DD$-plot, which is a subset of the unit hypercube. This transformation yields a new notion of depth for functional data. Three alternative depth functions are employed for this, as well as two rules for the final classification on $[0,1]^q$. The resulting classifier has to be cross-validated over a small range of parameters only, which is restricted by a Vapnik-Cervonenkis bound. The entire methodology does not involve smoothing techniques, is completely nonparametric and allows to achieve Bayes optimality under standard distributional settings. It is robust, efficiently computable, and has been implemented in an R environment. Applicability of the new approach is demonstrated by simulations as well as a benchmark study