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3D Dynamic Simulation and Visualization for GIS-based Infiltration Excess Overland Flow Modelling

3D Dynamic Simulation and Visualization for GIS-based Infiltration Excess Overland Flow Modelling
3D Dynamic Simulation and Visualization for GIS-based Infiltration Excess Overland Flow Modelling

Chapter 26

3D Dynamic Simulation and Visualization for GIS-based Infiltration Excess Overland Flow Modelling Izham Mohamad Yusoff, Muhamad Uznir Ujang and Alias Abdul Rahman Abstract. Effective GIS-based Infiltration Excess Overland Flow (IEOF) simu-

lation and visualization requires good knowledge of GIS core concepts and pre-

diction of soil infiltration rates due to impervious area coverage. The success or

failure of GIS-based IEOF simulation and visualization resides initially with the

georeference system used. C artographers have long complained about the poor

quality of the output from GIS, which today is generally due not to limitations

of the GIS itself but instead to a lack of understanding of cartographic princi-

ples among hydrologists and environmentalists. Implementation of soft geo-

objects representing flow elements such as streams, mudflows, and runoff pro-

vides better dynamic visualization in terms of velocity and direction. Inclusion

of volumetric overland flow would help in determining the volume of runoff

that hits the flood-plain areas, estimating channel flow capacity, and routing

and diversions to reduce effects from flooding. With rapid urbanization, indus-

trialization, and climate change, historical runoff and infiltration rates would

provide an improper guide for future enhanced visualization of the current 2D

land use surface. This study aims to visualize the influence of georeferencing

on IEOF simulation when represented by volumetric soft geo-objects within a

3D environment, which is driven by the physically based Green-Ampt method.

Visualization is analyzed by focusing on infiltration and overland flow proc-

esses using the conformal-based Malaysian Rectified Skew Orthomorphic

(MRSO) and the equidistant-based Cassini-Soldner projection. Appropriate us-

age of a georeferencing system to visualize 3D dynamic IEOF simulation may

see high demand from civil engineers, environmentalists, town planners, geolo-

gists, and meteorologists as a basis for producing scientific results in flood

management control, sustainability for long-term development purposes, stream

restoration, rehabilitation, and hydrologic impact assessment.

Department of Geoinformatics, Faculty of Geoinformation Science and Engineering, University Technology Malaysia

{izham1,uznir,alias}@utm.my

413

414 Izham Mohamad Yusoff, Muhamad Uznir Ujang and Alias Abdul Rahman 26.1 Introduction

GIS is very well adapted for spatial data organization, visualization, querying, and analysis, and it is also helpful in the context of simulation and modelling of spatial phenomena (e.g., floods, subsurface flow, evapotranspiration and groundwater flow) [20, 5]. The use of GIS in infiltration modelling has given benefits in expanding vari-ous kinds of simulation basis, spatial representation, and temporal representation models to display results based on site-specific measurements and experiments [10, 4, 12]. Such models have been used by many civil engineers, environmental scientists, town planners, geologists, and meteorologists to handle, analyze, and manage spatial information such as the rate of stormwater infiltrated into a soil profile, total surface runoff generated by urban growth, floodplain analysis, total Non-Point Sources (NPS) pollutant load, and channelizing [14]. Many catchments in Malaysia and other coun-tries are now under intense pressure from urban, industrial, and infrastructural devel-opment. Downstream receiving water bodies such as rivers, lakes, ponds, reservoirs, and estuary and coastal waters are facing increased rates and volumes of runoff and pollutant discharge [18]. Urbanization increases the percentage of impervious area in a watershed, causing the infiltration rate in a post-development area to be less than in a pre-development area, particularly in the western states of Peninsular Malaysia.

Existing GIS-based overland flow models such as Agriculture Nonpoint Source (AGNPS), Source Water Assessment Tools (SWAT), QUALHYMO, Long-Term Hy-drologic Impact Assessment model (L-THIA), LISFLOOD, and Storm Water Man-agement Model (SWMM) focus on modifying a hydrologic algorithm to delineate wa-tershed, flow accumulation, flow direction, runoff flow path, runoff volume, peak flow, NPS pollutant load, and sediment loading based on empirical, physical, kine-matic, and dynamic equations. These models have not focused on the influence of georeferencing and transformations of map projections towards spatial properties of hydrologic parameters [10]. Implementing GIS-based applications requires careful understanding in terms of selecting appropriate georeferencing systems and using ap-propriate transformations, scales, and grid resolutions when entering spatial data [23, 9].In planning the mapping of a limited area, one of the first decisions to be made is choice of a map projection[16].

Recently, GIS have started to move from 2D basis spatial hydrologic information systems towards 3D applications, and most recently from static (3D) systems towards dynamic systems that incorporate a temporal element, i.e., 4D [5]. Modelling dynamic overland flow simulation helps specialists and decision makers to understand, ana-lyze, and predict natural disasters (e.g., flash floods, landslides, water pollution) to re-duce related damages. Due to limitations in analyzing and modelling multidimen-sional data sets within GIS software, hydrologic modellers such as [17] linked HEC-HMS (Hydrologic Engineering Centre-Hydrologic Modelling System) and HEC-RAS (Hydrologic Engineering Centre-River Analysis System) with ArcGIS in a case study of flood simulation for Rosillo Creek in San Antonio, Texas. Although the commer-cial GIS software package was good at representing 2D spatial features, it did not support dynamic, probabilistic modelling.

Existing approaches for 3D GIS modelling can be classified into three geometry types as mentioned by [11]: surface-based (e.g., grid modelling), volume-based (e.g., tetrahedron network (TEN) modelling), and hybrids (e.g., TIN-Octree modelling).

26. 3D Dynamic Simulation and Visualization 415 Previous work has shown that these approaches can appropriately represent rigid geo-objects such as mountains, roads, and buildings [19], but soft geo-objects are less rep-resented. [21] developed a method for representing 3D soft geo-objects representing overland flow using the GIS flow element (FE) concept, which can be realized using particle systems and the metaball approach. However, volumetric dynamic flow is not visualized, and the influence of different map projections towards simulation results is not clear. The visualization techniques in this study improve the IEOF process deter-mination and allow hydrologists, environmentalists, and other professionals to be in-cluded in the decision-making process. Moving from a 2D map to a 3D landscape im-age can help end-users envision complex infiltration and overland flow information.

This paper describes the influence of conformal-based MRSO and equidistant-based Cassini-Soldner projection for GIS based IEOF modelling visualized in 3D dy-namic simulation using volumetric soft geo-objects. Simulation is performed within IEOF boundaries driven by the physically based Green-Ampt method using the meta-ball approach. Section 26.2 introduces the concepts of georeferencing for 3D dynamic IEOF simulation and volumetric soft geo-objects. Section 26.3 presents experiments in determining infiltration and volumetric soft geo-objects overland flow simulation within IEOF boundaries. Determination of IEOF area is explained in section 26.4, 3D dynamic simulation results are visualized in section 26.5, and conclusions are stated in section 26.6.

26.2 GIS for Infiltration Excess Overland Flow

Soils and soil properties are fundamental to the partitioning of water inputs at the earth’s surface. There is a maximum limiting rate at which a soil in a given condition can absorb surface water input. Important infiltration factors include soil surface con-ditions, subsurface conditions, hydrophobic, flow characteristics of the fluid, and fac-tors that influence soil surface and subsurface conditions [26].

The land use surface is a dynamic zone, representing at any time a net balance be-tween changing processes and landforms, with complex scale-dependent interactions. Current policy initiatives in Malaysia, such as Urban Storm water Management (USM), recognize this dynamism and are encouraging longer planning horizons and improvement of the storm water runoff process [18]. Flood management initiatives include the general public in the decision-making process through consultations, but flood management remains a difficult task because of the dynamic complexities of the land surface. Identifying present or historic patterns of surface runoff processes may be less appropriate due to changes in driving forces and climate. Current GIS com-mercial software packages are adapted for handling 2D and 2.5D aspects of TIN and raster surfaces with single z values for each data point. Advanced GIS software could represent data well as 3D objects on surfaces, but to access true volumetric analysis, specialist domain-specific packages are required [5].

Existing GIS products use a very static map-based analysis and have been success-fully implemented for managing natural and physical resources as assets. However, the IEOF process is fuzzy, uncertain and dynamic. Successful simulation of the GIS-based IEOF process may require multi-dimensional space-time modelling. There are

416 Izham Mohamad Yusoff, Muhamad Uznir Ujang and Alias Abdul Rahman some encouraging trends in ArcGIS software to support some dynamic simulation ca-pabilities through scripting [5].

26.2.1 Dynamic GIS based IEOF Visualization

Dynamic representation of geographic features in GIS began in the late 1980s and early 1990s. Several researchers proposed event-based models [27] that move from geographic feature identification and location characterization to an explicit focus on changes. Appropriate temporal data streams from monitoring and sensor networks provide tremendous scientific value but are not fully exploitable due to difficulties in integrating across the heterogeneous spatial and temporal sampling regimes and as-similating across a large multi-variant space. GIS have been useful tools for investi-gating spatial patterns but have suffered from a lack of abilities to explore the dy-namic aspects of geographic phenomena. The event-based Green-Ampt method provides the foundation of dynamic infiltration and overland flow phenomena. The method requires new visualization methods to fully address the spatial detail of shape movement and changes.

Furthermore, the method allows interpretation of future uncertainty through the use of different IEOF management strategies in combination with climate change scenar-ios. The link between the visualization system and the simulation model is provided by a GIS, which allows querying of the model data, integration with other datasets us-ing a common format, and then transfer between the modules used for visualization. By developing IEOF model-based simulations, scientific analysis can potentially pro-vide a replicable, rational, and transparent method to explore the complex processes of the infiltration and overland flow process within a structured framework.

26.2.2 Georeferencing for Dynamic IEOF Visualization

[9] stated that the main component of any GIS usage is the adaption of georeferencing systems to retrieve the actual positions of features in the real world. Information re-garding georeferencing and transformation are often lacked by civil engineers and hy-drologists when using a GIS approach [10]. [16] stated that representations of spatial features larger than 10 km2 are often distorted on a projected map. Cartographers have long complained of low-quality output from GIS, which today is generally not due not to limitations in the GIS itself but to a lack of understanding of cartographic princi-ples by users [7].

There are two types of ellipsoids used for georeferencing purposes in Peninsular Malaysia: the Modified Everest ellipsoid for the existing Malayan Revised Triangula-tion (MRT) system, and the Geodetic Reference System 1980 (GRS80) ellipsoid for the new Geocentric Datum of Malaysia (GDM2000) system [15]. Both systems derive the Malaysian Rectified Skew Orthomorphic (MRSO) and Cassini-Soldner coordinate system for mapping in Peninsular Malaysia. The MRSO coordinate system preserves the shape of spatial features for mapping topographic layers, while Cassini-Soldner preserves distances between spatial features for mapping cadastral lots based on ori-gin within each state in Malaysia [25]. Considering georeferencing influences for hy-drologic modelling would benefit the actual GIS core concepts and their applications.

26. 3D Dynamic Simulation and Visualization 417

The IEOF process depends on soil properties and land use. Transforming between

MRSO and Cassini-Soldner causes distortion on the shape, area, distance, and direc-

tion of the original position [25]. The MRSO projection is characterised as a confor-

mal based system where the shape of each feature in the map are preserved, but other

physical characteristics of those features, such as area, distance, and direction, are dis-

torted. The Cassini-Soldner projection is an equidistant-based system where the dis-

tance and area between features on the map are maintained, but the angle of those fea-

tures are distorted. Thus would result in influences on terrain, flow direction, and

volume of infiltrated stormwater and overland flow being represented by volumetric

soft geo-objects. Such situations may lead to inappropriate IEOF simulation results.

Water management and environmental practitioners may need to verify the physical

properties of spatial features that need to be preserved for modelling the IEOF process

within a GIS.

26.2.3 Mathematical Transformation between MRSO and Cassini-

Soldner Map Projection.

The MRSO provides an optimum solution in the sense of minimizing distortion of

spatial objects whilst remaining conformal for Malaysia [15]. The Cassini-Soldner

projection system for the Peninsula is based on several local data and realized by their

published equations and coordinates of their respective State origin. The existing Cas-

sini-Soldner projection for cadastral mapping is based on the MRT system referenced

to the Modified Everest ellipsoid. It is useful for mapping areas with limited longitu-

dinal extent. The projection has a straight central meridian along which the scale is

true, all other meridians and parallels are curved, and the scale distortion increases

rapidly with increasing distance from the central meridian.

Transformation of coordinate system between MRSO and Cassini-Soldner is done

via two methods: the general method or a polynomial equation [25]. General trans-

formation is done by changing a coordinate in its existing projection to geographical

coordinates as in (1) and then re-computing them to the coordinate grid in the targeted

map projection.

(X,Y) (Q,L)P (x,y)p

(1)

The polynomial solution is used when the numbers of coordinate points are high.

In this method, a relationship is established as in Equations (2) and (3).

X = C 1 + x.C 2 + y.C 3 + xy.C 4 + x 2.C 5 + y 2.C 6 + ...

(2) Y = D 1 + x.D 2 + y.D 3 + xy.D 4 + x 2.D 5 + y 2.D 6 + ...

where x,y are the coordinates in the existing map projection, X,Y are the coordi-

nates in the targeted map projection, and C i ,D i are the parameters of the transforma-tion of the projections. Transformation of MRSO into the Cassini-Soldner coordi-nate system is done by using the equations in (4) and (5). The reverse process of

418 Izham Mohamad Yusoff, Muhamad Uznir Ujang and Alias Abdul Rahman

coordinate system transformation from Cassini-Soldner to MRSO is performed us-

ing the equations in (6) and (7).

N cs = N 0cs + X - (R 1 + xA 1 + yA 2 + xyA 3 + x 2A 4 + y 2A 5) (3)

E cs = E 0cs + Y - (R 2 + xB 1 + yB 2 + xyB 3 + x 2B 4 + y 2B 5)

where X = N rso - N 0rso ; Y = E rso - E 0rso ; x = X/10000, y = Y/10000; N rso , E rso is the state coordinate in MRSO; N 0rso , E 0rso is the state origin coordinate in MRSO; N o cs, E 0cs is the state origin coordinate in Cassini-Soldner; and R i , A i , B i where i = 1,2,..5 are the transformation parameters.

N RSO = N 0RSO + X + R 1 + xA 1 + yA 2 + xyA 3 + x 2A 4 + y 2A 5

(4) E RSO = E 0RSO + Y + R 2 + xB 1 + yB 2 + xyB 3 + x 2B 4 + y 2B 5

where X = N cs - N 0cs ; Y = E cs - E 0cs ; x = X/10000, y = Y/10000; N cs , E cs is the state coordinate in Cassini-Soldner; N 0cs , E 0cs is the state origin coordinate in Cassini-Soldner; N o RSO, E 0RSO is the state origin coordinate in MRSO; and R i , A i , B i where i = 1,2,..5 are the transformation parameters.

26.2.4 Volumetric soft geo-objects in GIS

Geo-objects can be represented by two approaches: soft geo-objects, which display

streams, fire, and mudflows, and rigid geo-objects, which display buildings, bridges,

and mountains [21]. Simulating soft geo-objects can be performed using a particle

system, which uses small particles as basic elements representing soft geo-objects;

and the metaball approach, which displays different formations where more meta-

balls collide with each other. [21] introduced the soft geo-objects concept by per-

forming GIS FE based on pixel imagery and controlled by geoscientific models.

The GIS FE concept has position, velocity, and direction but neglects volume.

Hence, this paper intends to model IEOF processes dealing with the calculation of

total infiltrated stormwater and formation of overland flow volume towards streams.

Inclusion of volumetric soft geo-objects, controlled by the physically Green-Ampt

method under conformal-based MRSO and equidistant-based Cassini-Soldner pro-

jection, would provide proper usage of the georeferencing concept, guidelines for

enhanced visualization of the current land use and soil surface, designation of chan-

nel capacity, and diversion to improve future hydrologic impact assessment. Volu-

metric soft geo-objects are simulated using the metaball approach, which visualizes

the continuous surface that is formed when various overland flow sources meet. The

contribution from all volumetric soft geo-objects are collected and merged into

26. 3D Dynamic Simulation and Visualization 419 ordinary rendered settings and represented as volume, flow direction, and flow discharge.

26.2.5 IEOF Modelling

[8] stated that two conditions must be fulfilled for distribution of IEOF: the delivery of surface water input must be in excess of the hydraulic conductivity on the soil sur-face, and the duration of precipitation must be longer than the time required saturating the soil surface as in Fig. 26.1. Due to spatial variability of the soil properties affect-ing infiltration capacity and surface water inputs, IEOF does not necessarily occur over a whole drainage basin during rainfall events [24]. Moreover, the exception to localized Horton flow in temperate areas occurs on exposed bedrock [1], anthropo-genic effects such as urban development [6], agriculture, and removal of vegetation due to air pollution [3]. IEOF produced on catchment ridges and extended down slope until the entire catchment generated runoff, low slope angle and high saturated hy-draulic conductivity [2].

Source : Following Beven, (2000) Fig. 26.1. Generation of Infiltration Excess Overland Flow mechanism.

26.2.6 Mathematical Green-Ampt Infiltration Equation

[13] developed the Green-Ampt method for determining the amount of precipitation infiltrating into the soil during a precipitation event. The Green-Ampt infiltration model is a physical model which relates the rate of infiltration to measurable soil properties such as the porosity, hydraulic conductivity, and moisture content of a par-ticular soil based on simplified solutions to the Richards equation. This approach was developed for three reasons: (a) the solution of the Richards equation is difficult and not justified given that this equation is, at best, only a rough approximation of the ac-tual field infiltration; (b) a simplified solution still produces the exponentially de-creasing relationship between infiltration capacity and cumulative infiltration; and (c) the parameters of the methods can be related to soil properties that can be measured in the laboratory, such as porosity and hydraulic conductivity [24].

420 Izham Mohamad Yusoff, Muhamad Uznir Ujang and Alias Abdul Rahman

[13] developed a flow equation for infiltration under constant rainfall based on Darcy’s law, assuming a capillary tube analogy for flow in porous soil:

f = K(H

o + S

w

+ L)/L (5)

where K is the hydraulic conductivity of the transmission zone, H o is the depth of flow ponded at the surface, S w is the effective suction at the wetting front, and L is the depth from the surface to the wetting front. The method assumes piston flow (water moving down as a front with no mixing) and a distinct wetting front between the infil-tration zone and soil at the initial water content. [22] stated the basic Green and Ampt equation for calculating soil infiltration rate as:

f = K

s

(1 + [Ψθ / F]) (6)

where K

s is the saturated hydraulic conductivity, Ψ is the average capillary suction

in the wetted zone, θ is the soil moisture deficit (dimensionless), equal to the effective soil porosity times the difference in final and initial volumetric soil saturations, and F is the depth of rainfall that has infiltrated into the soil since the beginning of rainfall.

Proper information delivery and prediction of Green-Ampt equation parameters are vital for integrating GIS techniques to compute the infiltration rate and overland flow. The capability of GIS techniques to analyze IEOF, as mentioned by [1], [6], [3] and [2], may produce certain features on each layer with which to perform overlay, buffer-ing, intersection, union, and merge analysis to produce a new layer as the IEOF area.

26.3 Modelling 3D Dynamic IEOF Visualization

26.3.1 Study Area

The Pinang River basin is located between Latitudes 5° 21’ 32” and 5° 26’ 48” and Longitudes 100° 14’ 26” and 100° 19’ 42”. Pinang River is the main river system in Penang Island, with a catchment size of approximately 52 km2, as illustrated in Fig.

26.2. The Pinang River basin has been selected for the IEOF process due to the con-tinuous development that has affected land use and soils, degraded water quality, and increased water quantity in the entire basin. Flash floods and water pollution are the major problems faced by highly urbanized areas such as Georgetown, Jelutong, and Air Hitam.

In this study, the procedure for linking GIS and infiltration model parameter com-ponents involves the following steps: (1) acquisition and development of GIS map data layers of the Pinang River basin in MRSO and Cassini-Soldner projection; (2) pre-processing of Green-Ampt model input data, parameter and computation re-sults; and (3) post-processing of all infiltration components results for the 3D simu-lation, with volumetric soft geo-objects displaying IEOF area, volume of infiltrated

26. 3D Dynamic Simulation and Visualization 421 stormwater, and overland flow. The Green-Ampt model parameters are linked into the PC-based GIS package called ArcGIS and commercial 3D modelling software packages, both of which are used to store and visualize dynamic GIS-based IEOF volumetric soft geo-objects simulation results.

Fig. 26.2. Location of Pinang River basin (a) and its land use for 2006 (b).

A digital topography map with 1 : 25 000 scale is used to extract layers consisting of Buildings, Contours, DEMs, Road networks, and River networks. The land use and soil map is obtained from the Department of Agriculture for evaluating the soil condi-tion at the locations of interest. In this study, the rainfall data on the 18th of June, 2006, with a duration of 60 minutes, is used to determine infiltration rate and overland flow generated from IEOF areas using grid data layers with 20 meter and 5 meter resolution. Topographic information such as slope, aspect, flow length, contributing area, drainage divides, and channel network can be reliably extracted from DEM.

422 Izham Mohamad Yusoff, Muhamad Uznir Ujang and Alias Abdul Rahman

Square-grid DEMs are used in the IEOF modelling because of its simplicity, ease of

processing, and efficiency for computational purposes.

26.3.2 Determining Potential Area of IEOF using MRSO and Cassini-

Soldner Projection

Raster-based layers with 20 meter and 5 meter grid cells are used within MRSO and

Cassini-Soldner projection, respectively. Analysis is performed in two phases. The

first phase is to model spatial data layers by overlaying raster layers of Precipitation,

Land use, Slope, Soils, Buildings, and Road network based on criteria mentioned by

[8, 6, 3, 2] to map the potential IEOF area. The second phase is to intersect these

raster layers to map the potential location of IEOF and its area. The schematic dia-

gram for determining IEOF area is presented in Fig. 26.3.

Fig. 26.3. Schematic diagrams for determining IEOF area.

26.3.3 Computation and 3D Dynamic Visualization of Infiltration and

Overland Flow Volume Simulated within IEOF Area

The parameters required for the Green-Ampt method — soil hydraulic conductivity

(K s ), soil percent impervious (R s ), percent effective soil area (Eff ), the initial abstrac-tion (I a ), land percent impervious (R l ), percent vegetation (Veg ) and the degree of satu-ration (dry, normal or saturated) — are obtained through ground observation and

26. 3D Dynamic Simulation and Visualization 423 assigned to each pixel [22]. Simulations of volumetric soft geo-objects are performed based on the soil infiltration rate, referring to equations (8) and (9). The total overland flows within IEOF areas are computed by subtracting rainfall volume with the infil-trated rainfall volume. Fig. 26.4 shows a flow diagram of determining 3D dynamic volumetric soft geo-objects IEOF simulation, which is represented by a fine cylinder.

Fig. 26.4. Flow diagrams for rendering 3D dynamic IEOF volumetric soft geo-objects simula-tion.

26.4 Potential IEOF Areas

Fig. 26.5 illustrates our experiment in determining the IEOF area. The IEOF area is shaded in white.

Approximately 5.2 km2 of IEOF area is identified. Most of the IEOF coverage lies in areas of Paya Terubong, Air Hitam, Air Terjun River, Kebun Bunga, Green Lane, and partly in Gelugur and Jelutong. Table 26.1 summarises the differential amount of calculated IEOF area using 20 meter and 5 meter grid size under MRSO and Cassini-Soldner map projection. The location of IEOF lies in the sub-humid to humid regions, which are the major controls on the various runoff processes, based on climate data, land use, soil topography, and rainfall characteristics as stated by [24, 26].

424 Izham Mohamad Yusoff, Muhamad Uznir Ujang and Alias Abdul Rahman

Fig. 26.5. Potential area of IEOF within Pinang River basin.

26.5 3D Dynamic Simulation of Infiltration and Overland Flow Volume

Approximately 355,000 m3 of rainfall volume were recorded within the IEOF boundaries with 60 minutes of precipitation time. The estimated volume of rainfall in-filtrated into soil is 129,700 m3. The total overland flow simulated by volumetric soft geo-objects within the IEOF area is estimated to be approximately 223,700 m3. The results obtained are illustrated in Fig. 26.6 and Fig. 26.7.

Fig. 26.7 illustrates the large overland flow recorded in the areas of Georgetown, Jelutong, and Air Hitam. Construction of apartments, flats, the Jelutong Coastal Ex-pressway, and shop lots increase the proportion of land cover with impervious areas, the main factor contributing to flood risk. Existing river networks and drainage sys-tems of the Pinang River basin dealt with degrading of water quality and NPS pollut-ant load. This has caused the existing rivers and drainage systems to lack the capabil-ity to shift runoff volumes from highly urbanized areas.

26. 3D Dynamic Simulation and Visualization 425

Fig. 26.6. Infiltration Volume based on Green-Ampt equation within IEOF area.

Fig. 26.7. Amount of Overland Flow generated from IEOF area using Green-Ampt equation based on 18th of June 2006 precipitation data.

426 Izham Mohamad Yusoff, Muhamad Uznir Ujang and Alias Abdul Rahman

Fig. 26.8. 3D dynamic volumetric soft geo-objects simulation visualized at (a) 20 minutes, (b) 40 minutes and (c) 60 minutes precipitation time using conformal MRSO projection (left) and equidistant Cassini-Soldner (right) for Pinang River basin, Penang.

Fig. 26.8 shows the effects of georeferencing system on infiltration and overland flow volume visualization. Continuous input from precipitation increases the height and coverage of overland flow volume, mainly on down slope and flat areas. The vol-ume of infiltrated storm water and overland flow is proportional to the Green-Ampt method and physical characteristics of the conformal MRSO and equidistant Cassini-Soldner projections, which result in differentials in area, shape, flow path, slope, and deformation of volumetric soft geo-objects within the basin boundaries. These

26. 3D Dynamic Simulation and Visualization 427

changes cause dissimilarity of high and low infiltration and overland flow volume for

each sub-basin.

Table 26.1. Summary of identified IEOF area, precipitation, infiltration and overland flow vol-

ume under MRSO and Cassini-Soldner projection with 20 meter and 5 meter grid cell resolu-

tion.

Raster based

Analysis and Results

20 meter 5 meter

5253950.0000 1. Total area of IEOF under RSO Projection (m2) 5238000.0000

Total area of IEOF under Cassini Projection (m2) 5265600.0000

5255650.0000

15950.0000

Different (20 meter and 5 meter RSO Projection) (m2) ±

9950.0000

Different (20 meter and 5 meter Cassini Projection) (m2) ±

Different (20 meter RSO and Cassini Projection) (m2) ±

27600.0000

1700.0000 Different (5 meter RSO and Cassini Projection) (m2) ±

Different (20 meter RSO and 5 meter Cassini Projection) (m2) ±

17650.0000

Different (5 meter RSO and 20 meter Cassini Projection) (m2) ± 11650.0000

2. Total Precipitation Volume for RSO projection within IEOF area (m3) 352849.60000 353944.17500

Total Precipitation Volume for Cassini projection within IEOF area

355180.80000 354057.92500

(m3)

10945.5750

Different (20 meter and 5 meter RSO Projection) (m3) ±

1122.8750

Different (20 meter and 5 meter Cassini Projection) (m3) ±

2331.2000

Different (20 meter RSO and Cassini Projection) (m3) ±

113.7500 Different (5 meter RSO and Cassini Projection) (m3) ±

1208.3250

Different (20 meter RSO and 5 meter Cassini Projection) (m3) ±

Different (5 meter RSO and 20 meter Cassini Projection) ± 1236.6250

129830.9597 129679.1689

3. Total Infiltration Volume (Green-Ampt Equation under RSO

Projection), (m3)

129923.2375 129663.2530

Total Infiltration Volume (Green-Ampt Equation under Cassini

Projection), (m3)

151.7908

Different (20 meter and 5 meter RSO Projection) (m3) ±

259.9845

Different (20 meter and 5 meter Cassini Projection) (m3) ±

92.2778

Different (20 meter RSO and Cassini Projection) (m3) ±

15.9159 Different (5 meter RSO and Cassini Projection) (m3) ±

167.7067

Different (20 meter RSO and 5 meter Cassini Projection) (m3) ±

Different (5 meter RSO and 20 meter Cassini Projection) (m3) ± 244.0686

223981.0403 223436.8311

4. Total Overland Flow (Green-Ampt Equation under RSO Projection),

(m3)

224414.3625 223391.4970

Total Overland Flow (Green-Ampt Equation under Cassini Projection),

(m3)

544.2092

Different (20 meter and 5 meter RSO Projection) (m3) ±

1022.8665

Different (20 meter and 5 meter Cassini Projection) (m3) ±

Different (20 meter RSO and Cassini Projection) (m3) ±

433.3222

45.3341 Different (5 meter RSO and Cassini Projection) (m3) ±

589.5433

Different (20 meter RSO and 5 meter Cassini Projection) (m3) ±

977.5314

Different (5 meter RSO and 20 meter Cassini Projection) (m3) ±

Simulated results visualize the changes on the calculations of potential IEOF area,

precipitation volume, infiltration, and overland flow volume. Alternation of basin

shape, size, and distance due to different map projections would greatly affect the

physical shape, distance, area, and direction of soft geo-objects. It would also affect

calculations of total infiltrated precipitation onto ground surface, change of physical

soil parameters (soil porosity, conductivity, path of subsurface flow, return flow) with

different soil types, and amount of direct runoff generated in the study area. The mod-

elling performed, however, does not consider factors in the water balance equation

such as evapotranspiration losses, percolation, return flow, groundwater flow, and

shallow and subsurface flow. Different physical characteristics of the MRSO and

428 Izham Mohamad Yusoff, Muhamad Uznir Ujang and Alias Abdul Rahman Cassini-Soldner projections cause different values of infiltrated storm water, overland flow volume, and flow direction by volumetric soft geo-objects.

26.6 Concluding Remarks

This paper discusses the definition, mathematical expression and representation of 3D dynamic simulation of GIS based IEOF modelling using the volumetric soft geo-objects approach driven by the Green-Ampt method. We use this approach to estimate the potential locations of IEOF area, infiltration, and overland flow volume. The in-fluence of the MRSO and Cassini-Soldner projections results in deformation of volu-metric soft geo-objects, sub-basin area, slope angle, flow direction, and stream net-work paths. The spatial layers of soils, land use, precipitation, and runoff coefficient are all important sub-basin parameters that result in significant changes of infiltration and overland flow volume at various locations under different map projections.

3D dynamic simulation of IEOF within GIS is important for better understanding of observed phenomena as well as the representation and management of all steps of the process. Performing volumetric soft geo-objects simulation offers the possibility of visualizing affected areas, as well as of reducing economic and social losses. A thorough understanding of physical geography in hydrological process and determina-tion of GIS properties such as map projections, scale, and coordinate systems are re-quired before runoff modelling and data processing can be performed. A map can be drawn at any scale, but it is unclear to what extent existing hydrologic models can be applied at different map projections and scales.

To obtain higher-accuracy computation of IEOF areas, infiltration and overland flow volumes, further investigation is needed for possible new criteria. This investiga-tion may take the form of examining volumetric soft geo-objects data structure for other locations and conducting validation using combinations of GIS and hydrologic algorithms. Although the method involves some identifiable sources of uncertainty, the results nevertheless provide an initial indication of the importance of considering map projections, scales, and grid resolutions for an actual GIS application in a region. The results obtained would benefit the relevant agencies, such as the Department of Irrigation and Drainage (DID), Department of Environment (DOE), Department of Town Planning (DOTP), and Department of Minerals and Geosciences (DMG) in de-termining flood risk zones, areas of prompt to produce large direct runoff volumes, careful monitoring of NPS runoff pollutant loading, proper development plan and constructions, and monitoring of water quantity and quality of river networks. References

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模拟驾驶总结

模拟驾驶总结 两周的模拟驾驶眨眼间就过去了,曾经总以为自己学习的理论知识是纸上弹兵,但在这次模拟驾驶的中我深深的发现没有掌握系统完善的理论知识,在实践的过程中将会艰难曲折。课堂上我们学习的有关驾驶的方法和驾驶时遇到的故障处理都以为自己掌握的比较清楚,可动起手来发现并不是那么容易“事非经过才知难”,在模拟驾驶的过程里我发现了自己原以为懂了的知识其实并不熟练;以为比较简单的手动操作突然变的复杂了起来;平时耳熟能详的故障处理起来并完全符合操作手册。这一切都告诉我需要认真对待这次来之不易的模拟驾驶! 第一周,邓老师将我们带进微机室让我们熟悉了模拟驾驶的基本要求,在学校的微机室内,我们同过电脑“模拟驾驶小游戏”熟悉了地铁车辆运行的一些基本的知识,如何出乘、出厂、正线运行、站台作业、折返作业、列车退乘等,通过几天的反复训练同学们在电脑上的模拟驾驶基础操作都取得了比较令人满意的成绩,接下来老师又带我们进一步的熟练驾驶环节,培养了我们对速度控制的力度,要求我们对标停车。对标停车是一项非常需要技术和熟练度的基本操作,对速度快慢的控制近于苛刻。老师要求我们做到零标位到达车站,这使得我们的任务难上加难,但是这并不是影响我们完成任务的因素,相反这样大大提高了我们的积极性。对于有挑战的任务同学们总的争先恐后,同学们关于速度控制的问题多了起来,老师的工作变的忙碌起来了。在老师的指导下,我们经过了几天的反复训练取得了一定的效果!虽然不是每个同学都可以百分之百做到零标位到达车站,但是未达标而停止的现象少了,冲标过站的现象也少了。大部分的同学都可以到达车站打开车门,对此老师也比较欣慰。一周的时间弹指即过,但留给我们的映像却是深刻的!从一开始的基础到有挑战的任务,都让我们难忘,使我明白了许多道理。生疏的事物熟能生巧,做任何事情都要精益求精。

驾驶员理论考试网上模拟系统

驾驶员理论考试网上模拟系统 摘要:在当今的电子化时代,科技越来越重要,已经深入的应用到人们的生活中。其中驾校在线模拟考试系统以方便、快捷等优点得到了广泛应用。驾驶员理论考试就是在线考试的一个实际应用,对用户来说,不仅可以减少人力、物力和财力资源的浪费,更重要的是有助于提高学员考试的通过率。这和以往单机版的驾驶员理论考试系统相比,系统不需要安装,节约了本地计算机资源,方便了用户的接入,只要能上网就能随时模拟练习。该系统经过试运行及测试,能符合当今此类系统的先进性、实用性、可靠性等特点,将引领驾驶员理论考试网上模拟系统的新模式。 关键词:驾驶员;模拟考试系统; ASP ;NET 一、驾驶员理论网上模拟系统的发展起源和前景 在当今社会,科学技术飞速的发展,19世纪发明的计算机也越来越日益显露出举足轻重的地位。如今社会处于信息社会,知识经济即将或已经成为新世纪的主导产业。随着计算机的逐步推广和应用,它已在科研、生产、商业、服务等许多方面创造了提高效率的途径。Internet是目前世界上最大的计算机互联网络,它遍布全球,将世界各地各种规模的网络连接成一个整体。据估计,目前Internet上已有上百万个Web站点,其内容范围跨越了教育科研、文化事业、金融、商业、新闻出版、娱乐、体育等各个领域,其用户群十分庞大,因此,建设一个好的Web站点对于一个机构的发展十分重要。近年来,随着网络用户要求的不断提高及计算机科学的迅速发展,特别是数据库技术在Internet中的广泛应用,Web站点向用户提供的服务将越来越丰富,越来越人性化。 驾驶员模拟考试系统则是以计算机为操作工具,按照驾驶理论考试的流程,把驾驶的理论试题保存于数据库中,通过计算机可以很方便地查询使用所需要的数据,而且这些操作全部由系统内部的编程代码完成。考生和系统管理员通过系统的特定界面,输入相应的数据便可完成操作。该系统采用B/S模式进行设计,有网络的地方就可以进行在线模拟考试。 基于Web的驾驶员理论考试网上模拟系统,采用了当今流行的B/S结构,适应了驾驶员培训教育发展的新需要,对用户来说,不仅可以减少人力、物力和财力资源的浪费,更重要的是有助于提高学员考试的通过率。这和以往单机版的驾驶员理论考试系统相比,系统不需要安装,节约了本地计算机资源,方便了用户的接入,只要能上网就能随时模拟练习,也满足了当今驾校学员爆炸式增长的需求。与现行的一些基于B/S的驾驶员理论考试网上模拟系统相比,该系统提供了更为逼真的模拟考

基于模拟驾驶系统的动力学仿真模型分析与构建

基于模拟驾驶系统的动力学仿真模型分析与构建 汽车动力学模型在模拟驾驶系统中的作用很大,它可以给学员提供有如真实驾车的感觉,要达到逼真的视景仿真和操纵仿真结果,就需要建立仿真模型并对模型进行仿真实验。分析介绍了在设计基于虚拟现实技术的汽车模拟驾驶系统中 进行动力学分析和仿真所需建立的相关动力学模型。 标签:虚拟现实;汽车模拟驾驶;汽车动力学;模型;仿真 1 引言 开发汽车模拟驾驶系统具有重大的社会效益和经济效益。它可取代驾驶培训中学员实车训练中的部分科目和内容以及研究道路行驶的安全状况,有利于驾驶培训正规化、科学化和规范化,减少交通事故的发生率,并具有节能、安全、经济、高效等优点。在汽车模拟驾驶系统的开发中,为了尽量达到实车的驾驶效果,则利用计算机技术、控制技术和声像技术来模拟汽车驾驶及其行驶环境,所以需要建立逼真的仿真模型。模拟驾驶系统中要求仿真的内容很多,而建立并实现汽车模拟驾驶的汽车动力学模型是研制汽车模拟驾驶系统的前提。要做好汽车动力学仿真,建立正确的动力学模型是关键。笔者在汽车模拟驾驶系统研究过程中,基于汽车动力学知识和计算机控制技术,采用动力学和运动学分析方法对汽车所受的力进行研究,将得出的动力学数学模型通过实验建模用于汽车模拟驾驶系统的仿真,开发了适合我国交通国情和道路状况的汽车模拟驾驶系统。此系统主要通过模拟驾驶舱和计算机生成汽车行驶过程中虚拟的视景、音响等驾驶环境,将仿真的实验数据发送给视景计算机,驱动视景变化,视景计算机对车辆运动进行碰撞检测,将碰撞结果反馈给主机进行所受外力的计算,将计算结果以脉冲信号的形式发送给下位机,下位机通过电机控制模拟驾驶座椅摇动及振动来达到模拟驾 驶的效果。下面仅就动力学仿真方面内容进行论述。 2 汽车运行状态的受力分析 汽车在行驶过程中,不仅受发动机驱动力影响,还要克服各种阻力等,而汽车的动力性又是汽车各种性能中最基本、最重要的一种性能,要建立汽车模拟驾驶系统的动力学模型需应用计算机对汽车的动力学性能进行模拟仿真,最关键的问题就在于计算是否与实际情况相符合。通过数学公式所以建立适当的、符合实际的模型,对于精确描述汽车动力系统的运动状态,提高仿真模拟精度是非常重要 的。以下是对汽车在不同情况下的受力分析。

虚拟驾驶模拟系统---特殊效果

毕业设计 题目虚拟驾驶系统 ---特殊效果 学院机械工程学院 专业机械工程及其自动化 班级机自0701 学生徐晓卿 学号20070403222 指导教师王玉增 二〇一一年五月三十日

1前言 1.1虚拟驾驶系统的背景 随着我国经济的不断发展,越来越多的汽车作为代步工具进入大众化家庭,汽车的普及催生了大批的非职业化驾驶员,汽车驾驶训练的工作量有了很大程度的提高。驾校需要购置更多的车辆提供驾驶训练,并且加大教师的配备,来满足市场的需求,这与资金的投入产生了矛盾;采用实车进行汽车驾驶训练存在着污染、高成本、危险性高、场地不足等限制,市场的供应和需求的矛盾促使人们寻求新的驾驶训练方式。 计算机性能的提高和虚拟现实技术的发展,为在计算机上模拟汽车驾驶环境,进行驾驶训练提供了可能。计算机仿真技术、实时图形图像处理技术的飞速发展,为汽车仿真的研究提供了有力的工具和帮助。利用仿真技术可以进行不同虚拟环境的开发和多种车辆模型的设计,为汽车驾驶训练开辟了新方向。利用虚拟驾驶系统进行训练,不受时间、场地和气候的限制,在达到培训质量的前提下,具有经济、环保的优点,因此,利用计算机来开发汽车虚拟驾驶系统是一种有效的手段。 虚拟现实技术的提出和发展,为汽车虚拟驾驶系统的研究和开发提供了新手段。虚拟现实技术又称灵境技术,是一种先进的计算机界面技术,通过给用户提供视觉、听觉、触觉的交互手段,使用户产生强烈的沉浸感,带有实时交互功能的操作,能减轻用户的负担,提高系统的工作效率[1-3]。美国科学家自1989年首次提出虚拟现实技术以来[4],这项技术发展十分迅速,并广泛应用于军事、航空航天、自动控制、医疗、娱乐、教育等领域。将虚拟现实技术应用于汽车训练,即利用计算机构建用于汽车驾驶训练的虚拟环境和用于训练的车辆,产生“人-车-环境”闭环系统,在这一闭环系统中驾驶汽车,可根据车辆的行驶不断变换相应的虚拟视景、场景音效和车辆的运动仿真,使驾驶员沉浸到这一环境中,并根据虚拟环境中产生的触觉,听觉和视觉,变换相应的驾驶动作,使得虚拟驾驶车辆的位置在行驶环境中不断变化,以此产生驾驶员和虚拟环境的交互,达到训练驾驶员动作的目的。这种能够正确模拟汽车驾驶动作,获得实车驾驶感觉的仿真系统就是汽车虚拟驾驶系统,它是既能进行汽车驾驶训练,提高驾驶员水平,又能降低各种费用的汽车训练装置。运用这种装置进行汽车驾驶训

汽车仿真模拟驾驶器

利用各种高科技手段,让有驾驶需求的人员处在一个虚拟的驾驶环境中,感受到接近真实效果的视觉、听觉和体感的汽车驾驶体验。 汽车仿真模拟驾驶器 汽车仿真模拟驾驶器简介 0 汽车仿真虚拟驾驶器的关键技术 0 软件技术方面 (1) 1.三维图像即时生成技术 (1) 2.汽车动力学物理仿真技术 (1) 硬件技术方面 (1) 1.六自由度运动平台 (1) 2.大视场显示技术 (1) 3.用户输入和座椅硬件系统 (1) 汽车仿真模拟驾驶器简介 汽车仿真模拟驾驶器是指利用各种现代化的高科技手段,让驾驶者或有驾驶需求的人员处在一个虚拟的驾驶环境中,感受到接近真实效果的视觉、听觉和体感的汽车驾驶体验。中视典数字科技有限公司是目前国内比较知名的汽车仿真模拟驾驶器的提供商。 汽车仿真虚拟驾驶器的关键技术 汽车仿真虚拟驾驶器的关键技术包括软件技术和硬件技术两类。 其中 软件技术包括:三维图像即时生成技术、汽车动力学仿真物理系统; 硬件技术包括:大视场显示技术(如多通道立体投影系统)、六自由度运动平台(或三自由度运动平台)、用户输入硬件系统、立体声音响、中控系统等。

软件技术方面 1.三维图像即时生成技术 中视典VRP虚拟现实系统,不仅可以模拟道路环境如各类建筑、桥梁、隧道、水域、植被绿化等,还能模拟各种天气环境如早晨、中午、黄昏;大雾、下雨、下雪等。另外,VRP特有的高画质渲染技术,也为三维数字汽车原型设计成为了可能,使得汽车具有非常逼真的外观。 2.汽车动力学物理仿真技术 汽车动力学仿真物理系统,做为汽车运动仿真中最核心的环节,成为模拟驾驶中最为关键的部分。中视典VRP-PHYSICS物理系统,为这个功能提供了良好的技术支撑。可以模拟逼真的刚体动力学特性,如运动物体具有密度、质量、速度、加速度、旋转角速度、冲量等各种现实的物理动力学属性。在发生碰撞、摩擦、受力的运动模拟中,不同的动力学属性能得到不同的运动效果。( 详情可参照VRP-PHYSICS物理模拟系统介绍https://www.sodocs.net/doc/755033563.html,/article/html/vrplatform_17.html ) 硬件技术方面 1.六自由度运动平台 六自由度运动平台是由六支油缸,上、下各六只万向铰链和上、下两个平台组成,下平台固定在基础上,借助六只油缸的伸缩运动,完成上平台在空间六个自由度(X,Y,Z,α,β,γ)的运动,从而可以模拟出各种空间运动姿态。六自由度平台是各种飞行及航海等领域操作模拟器的重要组成部分,可由数字计算机实时控制提供俯仰、偏航、滚转、升降、纵向和横向平移的六自由度瞬时运动仿真。 2.大视场显示技术 多通道环幕(立体)投影系统是指采用多台投影机组合而成的多通道大屏幕展示系统,它比普通的标准投影系统具备更大的显示尺寸、更宽的视野、更多的显示内容、更高的显示分辨率,以及更具冲击力和沉浸感的视觉效果。( 详见多通道环幕立体投影系统介绍 https://www.sodocs.net/doc/755033563.html,/article/2008/0905/article_277.html ) 3.用户输入和座椅硬件系统 舒适安全的座椅,仿真的方向盘、档位、油门和刹车,这些也是模拟驾驶系统必不可少的元件。中视典能够根据客户需求,提供各种用户输入和座椅等配套硬件。

3D驾驶学校真实模拟开车的软件

3D驾驶学校真实模拟开车的软件)3.1 严格的说,用教学软件来定义这个程序或许更合适一点:严谨的交规,复杂的路况,完善的驾照考试系统,人性化的辅导教练,多款真实汽车和摩托车......但是精细的3D画面,中等的配置要求(机龄3年以内的机器基本合格),不算太复杂的操作(和真实开车相比)你有完全可以把它当作一款游戏。 操作方法如下: 1.游戏控制: --------------------------- F1: 设置菜单 调整声音、画面、按键等设置 ESC: 场景选择和退出游戏 2. 驾驶: --------------------------- 左/右方向键:左/右拐弯 上方向键:油门 下方向键:刹车 退格:急刹车 F:自动档的前进档 R:自动档的倒车当 P:停车档 3.观察: --------------------------- 空格:后视镜开关 Shift+左方向:向左看 Shift+右方向向右看 End 向后看 F7 调整视角 L 车灯开关 4. Signalling -------------------------- Ctrl 左转信号灯 Alt 右转信号灯 《孤胆枪手2重装上阵》可用秘籍 cheats:速度+50 cheath:生命+1000 cheatm / stmon:金钱+10000

PS :经测试+钱可以直接stm 就可以了 cheatexp / stexp:经验+1000 stgod:全部技能+100 金钱+50000 (技能最大值200,若反复输入,金钱增加,技能值在100和200之间转换) stammo:+各类弹药基数(含 Ammo_pistol/Ammo_shotgun/Ammo_minigun/Ammo_granate/Ammo_rocket/Ammo_ballon/Ammo_ener gy) stshop:开启商店 stdam:损伤玩家(破坏护甲,减少生命) stdeath:自杀 stloose:任务失败 stkillall:杀死所有异形 stwin / stwnn:过关 《魔域神兵》经验技巧总结 1.捡到的物品,如果不想卖,但是身上的物品又放满了,可以将它们扔到实验室的地面上,不会消失 2.鉴定物品之前,可以先保存一次,然后再鉴定。如果要,就留下;不要,就卖掉 3.有些未鉴定物品鉴定后,其价格会很高,一般是一些BOSS级怪物掉下来的,这时就可以在鉴定后再卖掉,能多赚不少钱

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