Developing the Computer Model and Simulations

In this lesson, you will build a complete Python computer model for forest disaster risk, incorporating real data from your embedded system and additional datasets. You will create two what-if scenario simulations, implement an adaptive feedback mechanism, and visualise results with graphs to meet all advanced requirements.

Learning Goals Learning Outcomes Teacher Notes

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1 Objectives
2 Exact Advanced Requirements You Must Meet
3 Data You Must Use
4 Required Python Libraries
5 Core Structure of Your Model File
6 Two Types of Adaptive Feedback (Choose One)
7 Practical Coding Time: Step-By-Step Tasks
8 Example Output You Should See
9 Evidence to Save
10 Final Checklist – Advanced Requirements Met

1 - Objectives

By the end you will have a complete, working Python computer model that fully meets the three advanced requirements on page 5 of the 2026 brief:

Develop a Python-based computer model of a chosen forest-related disaster risk (e.g., wildfire, drought, pest outbreak, air quality, landslides, flooding).
Two different “what-if” scenario simulations using real data from your embedded system combined with open-source or simulated data
A working feedback mechanism (physical or virtual) that makes the system adaptive

2 - Exact Advanced Requirements You Must Meet

From page 5 of the official brief:

Using Python, develop a computer model of a chosen disaster risk scenario related to forests (e.g., wildfire, drought, pest outbreak, air quality, landslides, flooding).
Your model must use some data collected from your embedded system and can be combined with open-source data or simulated data.
Explore how your model behaves under different conditions by creating and testing two “what-if” scenario simulations. Each scenario must change one or more key variables.
Extend your system to include a feedback mechanism that enables it to adapt automatically (physical or virtual).

All four points must be fully working.

3 - Data You Must Use

Your model must include:

Real data collected by your embedded system (CSV file from Lesson 5)
At least one additional dataset (open-source or simulated by you)

Example Data Sources	Possible Use
Met Éireann historical rainfall/temperature	Long-term drought prediction
Irish Forest Strategy tree density data	Afforestation what-if
Canadian Fire Weather Index formula	Wildfire risk calculation
Your own simulated future climate data	+2 °C or –40 % rainfall scenarios

All sources must be cited in your code comments per brief guidelines (page 6).

4 - Required Python Libraries

Add the required libraries to the top of your model file, for example:

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime

5 - Core Structure of Your Model File

Your final model.py must contain all of these sections:

Load your real embedded data (CSV)
Load or generate additional dataset
Define the disaster risk calculation (e.g., fire risk index, drought stress score)
Scenario 1 – change one or more variables and recalculate
Scenario 2 – different changes
Display results (e.g., tables via pandas and graphs via matplotlib) comparing baseline, Scenario 1, and Scenario 2
Adaptive feedback mechanism (see next step)

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Developing the Computer Model and Simulations