Know and understand computer modelling including personal finance, bridge and building design, flood water management, traffic management, weather forecasting

ICT 0417 – Computer Modelling Applications

Learning Objective

• Know and understand computer modelling in the following contexts:

  • Personal finance
  • Bridge and building design
  • Flood‑water management
  • Traffic management
  • Weather forecasting

How this topic fits into the whole ICT (0417) syllabus

What is computer modelling? (AO1)

Computer modelling is the creation of a simplified, mathematical or visual representation of a real‑world system so that its behaviour can be predicted, analysed, or compared with alternatives. The model uses input data (variables) to generate outputs that help decision‑makers evaluate possible solutions.

Common Modelling Process (Applicable to all five contexts) – Links to Assessment Objectives

1. Define the problem and objective – AO1: identify the real‑world situation and what you need to find out.
2. Collect reliable data – AO1: recognise sources of data and their limitations.
3. Identify key variables, inputs, outputs and assumptions – AO1.
4. Select appropriate ICT tools – AO2: choose software, set up formulas, and configure parameters.
5. Build the model, run simulations and record results – AO2.
6. Analyse the outputs – AO2: interpret tables, graphs and numerical results.
7. Evaluate the model – AO3: consider assumptions, data quality, sources of error and possible improvements.
8. Document and present findings – AO3: produce a clear report with tables, charts and a written conclusion.

Suggested ICT Tools for IGCSE (AO2)

Data Preparation, Validation, Documentation & e‑Safety (AO3)

• Data collection: Use reputable sources (bank statements, engineering standards, national meteorological services). Record the date, source, and any measurement uncertainty.
• Input validation: In spreadsheets set data‑validation rules (e.g., interest rate ≥ 0 % and ≤ 100 %). In specialised tools define acceptable ranges for loads, slopes, roughness coefficients, etc., to prevent unrealistic results.
• Documentation (required for every model):

  • Title and purpose
  • Assumptions and simplifications
  • List of variables with units
  • Version number and date of last modification
  • Source of each data set

• e‑Safety & privacy: Protect personal‑finance spreadsheets with passwords, back‑up files regularly, and never share sensitive data without consent. For cloud‑based tools ensure HTTPS connections and comply with school data‑protection policies.

1. Personal Finance Modelling

Purpose (AO1)

Predict the growth of savings, calculate loan repayments or compare investment options.

Key Variables, Outputs & Typical Assumptions

• Variables: principal (P, £), annual interest rate (r, % / year), compounding frequency (n, times / year), time (t, years), regular contribution (C, £ / period).
• Outputs: future amount (A, £), total interest earned, monthly repayment amount.
• Common assumptions: interest rate remains constant, contributions are made on schedule, no fees or taxes, interest is compounded exactly as specified.

Core Formula (Compound Interest)

\$A = P\left(1 + \frac{r}{n}\right)^{nt} + C\left[\frac{\left(1 + \frac{r}{n}\right)^{nt}-1}{\frac{r}{n}}\right]\$

Units: P, C, A in £; r as a decimal (5 % = 0.05); n and t dimensionless.

Worked Example (Spreadsheet steps)

1. Enter data in cells B2‑B6:
   

   P = 1200, C = 100, r = 0.03, n = 12, t = 5.

2. In B8 calculate the lump‑sum future value: =B2*(1+B4/B5)^(B5*B6).
3. In B9 calculate the contribution future value: =B3*((1+B4/B5)^(B5*B6)-1)/(B4/B5).
4. In B10 sum the two results: =B8+B9. The sheet now shows £7 862 (rounded).
5. Insert a line chart showing balance versus year to visualise growth.

Advantages & Disadvantages (AO3)

• Advantages: quick “what‑if” analysis; variables can be altered instantly; graphical output aids decision‑making.
• Disadvantages: assumes constant rate and regular contributions; ignores tax, inflation, fees, or unexpected withdrawals.

Evaluation Checklist (AO3)

• Are the interest rate and contribution figures up‑to‑date?
• Have all relevant fees or taxes been documented as assumptions?
• What rounding or spreadsheet‑precision errors could affect the total?
• Does the model allow sensitivity testing (e.g., change r, C, t) and is this recorded?

2. Bridge & Building Design

Purpose (AO1)

Assess whether a structural element can safely support expected loads and meet regulatory safety factors.

Key Variables, Outputs & Typical Assumptions

• Variables: span (L, m), uniform load (w, kN / m), material yield strength (σ_y, MPa), section modulus (S, cm³), safety factor (FoS, dimension‑less).
• Outputs: bending stress (σ, MPa), deflection (δ, mm), factor of safety.
• Common assumptions: load is evenly distributed, material properties are uniform, temperature effects are negligible, supports are simple and pin‑connected.

Core Formulae

Bending stress for a simply supported beam with uniform load:

\$\sigma = \frac{M{\max}}{S},\qquad M{\max}= \frac{wL^{2}}{8}\$

Factor of safety:

\$\text{FoS}= \frac{\sigma_{y}}{\sigma}\$

Worked Example (Spreadsheet steps)

1. Input data: L = 6 m, w = 20 kN/m, width b = 0.30 m, depth h = 0.60 m, σ_y = 30 MPa.
2. Calculate section modulus in B8: =B4*B5^2/6 → 0.018 m³ (18 000 cm³).
3. Maximum moment in B9: =B3*B2^2/8 → 90 kN·m.
4. Convert moment to N·mm (multiply by 10⁶) and compute stress in B10: =(B9*10^6)/B8 → 5 MPa.
5. Safety factor in B11: =30/B10 → 6.

Advantages & Disadvantages (AO3)

• Advantages: visualises stress distribution; enables material optimisation; reduces need for costly physical prototypes.
• Disadvantages: relies on idealised loading and material uniformity; complex geometries may require advanced FEA, which is beyond the IGCSE level.

Evaluation Checklist (AO3)

• Are material properties taken from the latest British Standards (e.g., BS 8110 for concrete)?
• Has the model considered combined loads (dead + live, wind, seismic) or only the uniform load?
• What simplifications (e.g., ignoring shear, temperature) could affect accuracy?
• Is the chosen safety factor appropriate for the structure’s intended use‑class?

3. Flood‑Water Management

Purpose (AO1)

Predict river‑flow rates, reservoir storage and the likelihood of flooding under different rainfall scenarios.

Key Variables, Outputs & Typical Assumptions

• Variables: rainfall intensity (I, mm / h), catchment area (A_c, km²), hydraulic radius (R, m), channel slope (S, m / m), Manning roughness (n, s m⁻¹⁄³), storage volume (V_s, m³).
• Outputs: flow velocity (V, m / s), discharge (Q, m³ / s), water level in storage, time to peak flow.
• Common assumptions: rainfall is uniformly distributed over the catchment, infiltration and evaporation are negligible during the storm, channel shape remains constant.

Core Formulae

Manning’s equation for open‑channel flow:

\$V = \frac{1}{n}R^{2/3}S^{1/2}\$

Discharge:

\$Q = A \times V\$ where A is the cross‑sectional area (m²).

Worked Example (Spreadsheet steps)

1. Data entry: I = 30 mm/h, A_c = 2 km², n = 0.035, S = 0.001, channel width = 10 m, depth = 2 m.
2. Convert catchment area to m² (2 km² = 2 × 10⁶ m²) and compute runoff volume: =I/1000 * A_c → 60 000 m³.
3. Hydraulic radius for a rectangular channel: =Depth*Width/(2*Depth+Width) → 1.43 m.
4. Velocity (B10): =1/n*(B9)^(2/3)*SQRT(S) → 1.84 m/s.
5. Cross‑sectional area (B11): =Width*Depth → 20 m².
6. Discharge (B12): =B11*B10 → 36.8 m³/s.

Advantages & Disadvantages (AO3)

• Advantages: enables scenario testing (e.g., climate‑change rainfall), supports design of flood‑defences and early‑warning systems.
• Disadvantages: heavily dependent on accurate topographic and roughness data; sudden debris blockages or underground flow are not captured.

Evaluation Checklist (AO3)

• Is the rainfall data sourced from a reliable gauge network or radar product?
• Have Manning’s n values been calibrated with field measurements for the specific river?
• Does the model account for storage losses (evaporation, infiltration) or assume they are zero?
• What is the level of uncertainty when predicting extreme (e.g., 1‑in‑100‑year) events?

4. Traffic Management

Purpose (AO1)

Analyse traffic flow, identify bottlenecks and evaluate the impact of signal timing or road‑capacity changes.

Key Variables, Outputs & Typical Assumptions

• Variables: traffic volume (q, vehicles / hour), road capacity (C, veh / hour), vehicle density (k, veh / km), average speed (v, km / hour), signal cycle length (L, s).
• Outputs: queue length (m), average travel time (s), Level of Service (LOS), estimated emissions.
• Common assumptions: driver behaviour is homogeneous, traffic demand is steady during the analysis period, lane‑changing and pedestrian interactions are ignored.

Core Relationship

Fundamental traffic‑flow equation:

\$q = k \times v\$

Worked Example (Spreadsheet steps)

1. Enter data: q = 1800 veh/h, road length = 0.5 km, signal cycle = 90 s with green time = 45 s.
2. Compute capacity per cycle: =C*(Green/L). Assuming C = 2000 veh/h, capacity = 2000 × 0.5 = 1000 veh per cycle.
3. Determine degree of saturation: =q/Capacity → 1.8 (over‑saturated).
4. Estimate average delay using Webster’s formula (simplified): = (L/2)*( (Degree‑of‑Saturation-1)^2 ) → 22.5 s per vehicle.
5. Calculate total queue length: =q*Delay/3600 → 11.25 veh ≈ 45 m (assuming 4 m per vehicle).

Advantages & Disadvantages (AO3)

• Advantages: virtual testing of road‑network changes before costly construction; supports real‑time traffic‑control strategies.
• Disadvantages: driver behaviour is stochastic; model may oversimplify lane‑changing, pedestrian crossings, or public‑transport interactions.

Evaluation Checklist (AO3)

• Are traffic counts taken during representative periods (peak, off‑peak, weekend)?
• Has the model been validated against observed travel times or queue lengths?
• What assumptions about driver compliance with signals are made?
• How could emerging technologies (autonomous vehicles, smart‑city IoT) affect the model’s relevance?

5. Weather Forecasting

Purpose (AO1)

Generate short‑ to medium‑range predictions of temperature, precipitation, wind and other atmospheric variables.

Key Variables, Outputs & Typical Assumptions

• Variables: temperature (T, °C), pressure (p, hPa), relative humidity (%), wind components (u, v, m / s), sea‑surface temperature, solar radiation.
• Outputs: forecast maps (temperature, precipitation), probability of precipitation, wind field visualisations.
• Common assumptions: the atmosphere behaves as a continuous fluid, physical laws (Navier‑Stokes, thermodynamics) are approximated on a grid, initial data are sufficiently accurate.

Core Approach (Numerical Weather Prediction – NWP)

1. Gather initial conditions from weather stations, satellites and radars.

2. Input the data into a numerical model (e.g., UK Met Office Unified Model) that solves the governing equations on a 3‑D grid.

3. Run the model for the desired forecast period (e.g., 0‑72 h).

4. Visualise outputs as contour maps or colour‑coded graphics.

Worked Example (Spreadsheet‑level illustration)

1. Download a CSV of the last 24 h of temperature observations for a city.
2. Calculate a simple moving‑average (3‑hour window) to smooth the data: =AVERAGE(B2:B4) and copy down.
3. Extrapolate a linear trend for the next 12 h using the FORECAST.LINEAR function.
4. Plot the observed, smoothed and forecast series on a line chart.

While this is a very simplified “forecast”, it demonstrates the principle of using recent data to predict near‑future conditions – a skill expected at IGCSE level.

Advantages & Disadvantages (AO3)

• Advantages: provides systematic, repeatable predictions; enables early warnings for severe weather.
• Disadvantages: accuracy drops rapidly beyond 3‑5 days; relies on high‑quality initial data and powerful computers; small‑scale phenomena (local thunderstorms) may be missed.

Evaluation Checklist (AO3)

• Are the initial observations sourced from calibrated, official stations?
• Has the model been compared with recent verified forecasts to assess bias?
• What uncertainties arise from grid resolution and parameterisation of clouds?
• How might climate‑change trends affect the reliability of historical‑data‑based forecasts?

Linking the Activities to Assessment Objectives

Quick Reference Checklist for Teachers

• Introduce the one‑sentence definition of modelling at the start of the lesson.
• For each context, present the “Key variables, outputs & assumptions” table before the formulae.
• Show a short screen‑capture (or live demo) of the spreadsheet set‑up – emphasise data‑validation and formula copying.
• Use the evaluation checklists to guide students’ AO3 written reflections.
• Remind learners to include the documentation block (title, purpose, assumptions, version, sources) in every model file.