Samples ravi sadasivuni

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Published on March 15, 2014

Author: NagaSindu

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Research results from Location Analytics, Economic Hinterland, optimized transportation Corridor analysis. Note: My understanding of the advanced spatial analysis methods and techniques and how these methods connect to location and spatial interaction in theoretical frameworks, provides solutions across diverse fields, including environment & transportation improvements, business applications, economic models, and regional sciences for planning.

www.gri.msstate.edu Sample Works Ravi R Sadasivuni

www.gri.msstate.edu Table of Contents Gravity Models 1.Economic and Retail applications in the Region Using GIS 2.Convection-Diffusion Based Spatial Interaction (Gravity) Model to Predict Anthropogenically-Initiated Wildfire Risk Other Models 1.A Transportation Corridor Case Study: A Multi-criteria Decision Analysis (MCDA) 2.Digitization & Topology Corrections 3.Morphometry & Landform Classification

www.gri.msstate.edu Economic and Retail applications in the Region Using GIS

www.gri.msstate.edu • Total gravity surface derived and then a specific gravity (capture rate) calculated for site in question • The total number of consumers captured are determined by population density. • Distance can be measured as a crow flies, along a transportation network or in terms of estimated travel time. Background Specific Gravity

www.gri.msstate.edu Economic Impact Modeling the economic impact of adding new competitive locations displayed from Red (High) to Low (Blue) Hypothetical Locations Potential Trade region

www.gri.msstate.edu Trade influence in the Southern Mississippi High Low the site and situation of the center influential to economy with respect to factors such as the type and age of center, proximity of competitors, accessibility, visibility, clientele, topography, crime, and socioeconomic factors. Thus, it is essential to account for market structure and the types of products or transportation for proper use of gravity models.

www.gri.msstate.edu Previous Prediction Models  United States Forest Service models use correlations between the historical observational and geographical data, climatic and vegetation components to obtain fire risk potential indices  There are few models which account both fuel loads and ignition sources to predict wildfire potential  Road Density and road buffers tells about the activity along corridors but not the activities (from traffic vol) and its origin from the cities (Cooke and Grala, 2010; Gilreath, 2006)  Gravity based model gives a GLOBAL measure of propulsiveness and a measure of destination attractiveness as the flow of activity between different zones, but does not consider the transportation corridors (Sadasivuni et al., 2013).  Most wildfire ignition distribution models fail to define the diffusion direction, while diffusion seldom occurs across the Euclidean distance, but along the transportation corridors (Ayeni 1979)

www.gri.msstate.edu  A Large city is more likely to attract an individual than a smaller city  Newton’s formula is modified with physical mass for population size PiPj and a friction (cost) is inverse power function of distance f(cij)=dij -λ Pij = PiPj / dij -λ where λ is a parameter of the function dij is the distance between i and j (Sadasivuni et al. 2013, “Ecological Modeling”) “Perhaps the best-known model of spatial interaction is the gravity model, so called because of its analogy with the Newtonian concept of gravity. The traditional gravity model is based on the hypothesis that the interaction between any two masses varies directly with the product of the masses and inversely with some measure of the spatial separation or cost between the two masses”  Predicting human activity patterns of incendiary activity using diffusion and convection with Newton’s Gravity Model. Gravity Model

www.gri.msstate.edu Background: SIM Road Density as Human Ignition (Gilreath, 2005) Not Accurate for high risk zones, such as along roads

www.gri.msstate.edu  Gravity Model previously applied for wildfire prediction  Shows good interaction among cities, aligned along the roads  Performs poorly than Road density model in Medium risk zones (outskirts of the city), and very low risk zones Background Gravity Model In the broadest sense, the movement of people/animals/commodity/capital or information across the geographic space are modeled with origin, destination and transportation routes (in enhanced method later) that solve the problems of dispersion through GIS decision analysis.

www.gri.msstate.edu Approach: Modeling Philosophy  Movement of people are:  general guided by the cities  aligned with the transportation corridors  anisotropically aligned along high traffic volume  roads provide access points to the woods for people with incendiary motive  Anthropogenically initiated wildfire is directly related to the movement of people  Influencing factors grouped as global and local variables.  Global variables are modeled using Gravity term  Local variables are parameters of transportation corridors modeled as convective/diffusive fluxes

www.gri.msstate.edu Fig. Schematic diagram showing the interaction between human activities from population interaction, location and time and its effects on the ecosystem through land use and wildfire. Fig. Human disturbance of wild vegetation of North America. Redrawn from Man and Earth’s Ecosystems, by Charles F. Bennett (1975) Background: Wildfire Impact Complex dynamics of coupled human-natural systems

www.gri.msstate.edu Wildfire Ignition Flow Chart: CDM Modeling Details Intermodal Transportation Local Wildfire Risk Anthropogenic FactorsFuel Vegetation Climate P-E Seasons Population Gravity (P/d2 ) Damping (α) Global Ignition Additive Multiplicative Traffic Vol Convection Roads Rails Diffusion (√ߥ) Diffusion (√ߥ)

www.gri.msstate.edu ),(),,(),,( yxtyxtyx if φφφ ×= Occurrence of fire (φ) depends on fuel (φf) and ignition potentials (φi ) φf varies in space (vegetative characteristics) and time (variation of the seasons) φi assumed to be function of space only and depends on,  proximity (D) to city or population area (P);  proximity (dro/ra) to railroads (ra) and roads (ro);  traffic volume (Vt)  Is a function of time, but not considered herein;  length or density of the corridors (Lro/ra) Wildfire Ignition: CDM Formulation

www.gri.msstate.edu where, V is the convection velocity, ν is the diffusivity coefficients and α is a gravity model damping term inside the city. (Sadasivuni et al., submitted to Ecological Modelling) ( ) ( )  CitiesfromGravity B CorridorsalongDiffusion raro RoadsalongConvection L V Li D P d d V ro t B 2/2 2 1 α δ φδ νφφ ++= ∑ Wildfire Ignition: CDM Formulation  Model couples the multi-criteria behavioral pattern in a single dynamic equation  Unknown coefficients to be calibrated using observational data

www.gri.msstate.edu  Study area - Southeastern Mississippi in Mississippi  Primary roads - white lines (from MARIS)  Railroads - black lines (from MARIS)  Cities and its population (from MARIS)  Wildfire incidences for years 1992-2009 (black symbols) (United States Forest Service )  Traffic volume in yellow symbols (gomdot.com)  Black horizontal and vertical lines show the quadrats for the rail-road interaction analysis Observational data ±

www.gri.msstate.edu • Wildfire decreases with the increase of the distance from the roads • Historic wildfire events across the road buffers helped evaluate the ignition potential and calibration of ν (nu) which is 1/1250 through the curve fit of the observational data Observational Data Analysis : Fires vs Roads

www.gri.msstate.edu Observational Data Analysis : Fires vs Intermodal Transportation  Roads and Rails run in parallel, so rail buffers alone cannot be analyzed.  Wildfire events per unit length of the rail roads were computed in the study region for the years 1992-2009.  Quadrats with Inter-modal transportation corridors show 30% higher wildfire frequency

www.gri.msstate.edu  Road density at the fire locations along with distance from the city center.  Analysis is performed using the cities Hattiesburg, Laurel and Ellisville.  Wildfire events increase along the city radius  Peak frequency at the medium density road, consistent with Gilreath 2006. Observational data Analysis: Fires With Cities 0 0.5 1 1.5 2 0 1000 2000 3000 4000 DistancefromCityCenter/City Radius #Fires Road Density Road Density at Fire Location # Fires Distance from the City (Units: km / Sq. Km)

www.gri.msstate.edu Methodology Observational data : Fires vs Traffic Volume per Road Density Anisotropy conditions in the city potential field (gravity) due to high traffic volume in specified direction/roads as shown by the arrows cθθ θ θ θ

www.gri.msstate.edu Methodology Analytic Model and Calibration: City Potential 2 D P α  City Interaction is modeled following Gravity Model  Damping function is derived from an exponential curve fit over the data

www.gri.msstate.edu Preliminary Results Analytic Model and Calibration: Transportation Corridors Ignition potentials due to the road and cities are somewhat independent of each other, thus can be treated separately.

www.gri.msstate.edu Preliminary Results Analytic Model and Calibration: Traffic Volume ( ) [ ] 22 1 )(1 D P V D P V cTr B L V Lc ro t B αθαφφ +=+= ∑ 45/)( 2 )( c eV cTr θθ θ −− = Traffic volume generates an anisotropic behavior of the city potential along the angular direction (θc), VTr ~ 1- 15 is traffic volume per road density Gaussian distribution along the traffic volume direction θc

www.gri.msstate.edu  Spatial distribution of the combined Road and City ignition potential for two different city and road connection patterns. Preliminary Results Analytic Model and Calibration: Total Potential  Ignition potentials due to the road and cities are somewhat independent of each other, thus can be treated separately, therefore:

www.gri.msstate.edu (a) (b)  The combined wildfire ignition potential is tested using an in- house Fortran code  Test Study region around Hattiesburg area, 80 km x 50 km  Depiction of the study region for the Fortran simulations.  The cities are represented as filled circular regions.  Roads (Black) and Rails (Brown) with straight line segments.  Domain discretized using 1001x1001 points, i.e., 85m x51m resolution. Preliminary Results Depiction in Fortran Code

www.gri.msstate.edu  The relationship between transportation corridors and spatial interaction  SIM diffusion along the inter-modal transportation corridors  Petal side of the Hattiesburg show more interaction behavior Preliminary Results City Interaction and Traffic Volume

www.gri.msstate.edu  High fire ignition potential distribution showing anisotropy conditions of traffic volume  High fire ignition potential distribution coincident with more fires in broken ellipses  The high spikes signify the high activity areas Intermodal Transportation Corridors and City Interaction

www.gri.msstate.edu 0 100 200 300 400 Very Low Low Medium High Very High NumberofFires Fire Probablity Quintile Bins City-w/o Damping City- w Damping Road Total  Observed fires (or fire frequency) distributed in the quintile bins  From the figure roads have high number of fires in the high risk zone and almost equal number in Medium and very high risk zones  Similarly, the observed fires are highest in very high risk zone and next levels holding high fires are medium and high risk zonesCombined model i.e. ‘total’ (Black dotted) performs better for wildfire frequency predictions than the city and road models alone Preliminary Results Quintile analysis of Fire Distribution Across Risk Zones

www.gri.msstate.edu Results Using fluid-dynamics analogy (FMA) model Intermodal Transportation Corridors, City Interaction and Fuel to Predict Wildfire Risk Area Weighted Historic Wildfire Risk (Winter) Gravity model CDM Model01 Hattiesburg Wiggins Poplarville Quitman ±±

www.gri.msstate.edu • In summer, the figure shows slope of 0.667, intercept = 0.1313, and R2 = 0.6057. • In winter, the figure shows slope of 0.99, intercept = 0.0212, and R2 = 0.8725. • CDM model also shows better agreement with observation in winter compared to Gravity model. Seasonal Regression Analysis of the CDM model

www.gri.msstate.edu  Attempted an examination of the linkages among anthropogenic factors and wildfire behavior  Interaction between transportation corridors with traffic volume and city interaction disentangling diverse relationships of factors to identify risk probability  The distribution of the number of wildfire events decay exponentially as the distance from the road increases Conclusion and Future Work Conclusion:

www.gri.msstate.edu DIGITIZATION AND TOPOLOGY ERRORS FIX

www.gri.msstate.edu Fixing Geodatabase Topology Errors Present Work As a part of Landform analysis, I am fixing the topology errors on the appended of counties in Arkansas

www.gri.msstate.edu Morphometry & Landforms

www.gri.msstate.edu Ridges and Valleys and Flat Lands Flat Lands

www.gri.msstate.edu Various Morphometric Indices

www.gri.msstate.edu Appalachians 0 (Black) - indication of concavity or sub-horizontal terrain with isolated peaks 1 (White) - indication of convexity or sub-horizontal terrain with deep incision Values distributed between 0-1 indicating various forms like domes, cylindrical, toe slopes, mesas, basins etc Zoom out View Zoom in View See the curvatures of slope extracted

www.gri.msstate.edu Local Shape is a Function of Curvature Various Parameters: DEM Slope Profile Curvature Planar Curvature Tan Curvature SPI TWI

www.gri.msstate.edu Morphometric Curvatures Extracted From the DEM Profile Curvature Planar Curvature General Curvature

www.gri.msstate.edu Combination of Curvatures and Topographic Position Index (TPI) Landforms extracted 1=canyons, deeply incised streams 2=midslope drainages, shallow valleys 3= upland drainages, headwaters 4=u-shaped valleys 5=plains 6=open slopes 7=upper slopes, mesas 8=local ridges, hills in vallyes 9=midslope ridges, small hills in plains, 10=mountain tops, high ridges.

www.gri.msstate.edu A TRANSPORTATION CORRIDOR CASE STUDY FOR MULTI-CRITERIA DECISION ANALYSIS

www.gri.msstate.edu Past Research Work 1. Past Research Work • Involved in developing new and innovative approaches to address environmental impact assessment aspects in various transportation related projects. • Environmental Impact Analysis study was conducted for the proposed I-69 / I-269 corridor which is sponsored by the U.S. Department of Transportation – Research and Innovative Technology Application (DOT- RITA). • Developed quantitative approaches to reduce subjectivity in weighting schemes for site selection.

www.gri.msstate.edu I-269 location A study area map for the proposed project shows I-269 location study alignments in Mississippi from I-55 to the Tennessee boundary. The study area is defined by a 2 mile buffer around the location study alternatives where remote sensing data collection (aerial imagery, digital stereo imagery) will focus covering about 180 square miles.

www.gri.msstate.edu From Ranking to Weights using AHP Ex: Slope Pair-wise comparison: Relative rank of areas (slope >20%) higher than (slope 15-10%) (rank = 9 / rank = 5) 9 7 5 4 1 44 All factors/criteria are normalized prior to combined analysis

www.gri.msstate.edu Wetlands DEVELOPED METHODOLOGY FOR TRANSPORTATION CORRIDOR CASE STUDY USING MULTI-CRITERIA DECISION ANALYSIS Note how the existing roads are reused and avoidance areas are considered

www.gri.msstate.edu Note how the existing roads are reused and avoidance areas are considered AHP based Generated routes match the existing routes

www.gri.msstate.edu I-269 study bed 32 Cumulative Cost Surface Defining Initial Corridor

www.gri.msstate.edu 3D View: The proposed project shows I-269 location study alignments in Mississippi from I-55 to the Tennessee boundary.

www.gri.msstate.edu Rajiv Gandhi National Drinking Water Mission Project (RGNDWM) – Rajasthan, India

www.gri.msstate.edu Water in India 1 2 3 4 5 6 1947 1967 1987 2007 2027 2047 *000 m3 Declining availability of water per capita Source: ISRO • More than 80% of water needs in India is met from ground water resources of India. • This has aggravated the depletion of water table, and led to an unprecedented water shortage. • After China and India, there is a second tier of countries with large water deficits – Algeria, Egypt, Iran, Mexico, and Pakistan. • The Himalayan glaciers, sources of sub-continent’s largest rivers – the Ganges, the Indus, the Brahmaputra, the Yangtze, the Mekong, the Salween, and the Yellow River

www.gri.msstate.edu Data Layers Used Derivative layers: 1. Hydrogeology (polygon) layer 2. Hydrogeology (line) layer 3. Recharge structure (line) layer 4. Recharge structure (point) layer   Ground water prospects map E00 / Shape files 1. Administrative (polygon) layer 2. Settlement (point) layer 3. Road network (line) layer 4. Railway lines (line) layer 5. Drainage (line) layer 6. Water body (polygon) layer 7. Canal (line) layer 8. Spring (point) layer 9. Rain fall data (point) layer 10. Wells (point) layer 11. Irrigated area (polygon) layer 12. Lithology (polygon) layer 13. Structure - 1 (line) layer 14. Structure - 2 (line) layer 15. Geomorphology (polygon) layer Combination of layers Satellite Data: IRS-1C / SPOT / SOI Topo Sheet

www.gri.msstate.edu Extraction of various layers from the Satellite Data Roads Streams / Rivers Irrigated / Canals and so on…

www.gri.msstate.edu Geological Layers extraction

www.gri.msstate.edu Site-specific Recharge Structures in the form of line and point features

www.gri.msstate.edu Sample Map of RGNDWM

www.gri.msstate.edu Ground water Prospects Information • The depth and yield ranges of the wells recommended hydrogeomorphic unit - wise are represented with different colors and different hachuring patterns. • The yield ranges that are possible belong to nine categories. Similarly the depth ranges that are possible belong to three categories. Any combination of these yield and depth ranges is possible to occur and the numbers of combinations are many. • To represent each combination on the map a VIBGYOR color scheme is used and is given at 15 as fixed part of the legend

www.gri.msstate.edu References 1. Ground water prospects maps - prepared by NRSC; available with State Government Line Departments; 2000 - till date 2. Ground water prospects mapping for Rajiv Gandhi National Drinking Water Mission (RGNDWM) - Technical Manual; NRSC; 2008 3. http://www.loiczsouthasia.org/india.php

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