🌍 Climate Change Project

📋 Table of Contents

• Introduction
• Data Overview
• Problem Statement
• Objectives
• Alignment with Egypt Vision 2030
• Project Steps
  • 1️⃣ Collecting and Cleaning Climate Data
  • 2️⃣ Creating 5 Interactive Dashboards
  • 3️⃣ Predictive and Analytical Models 🤖
• Project Structure
• Tech Stack
• Getting Started
• Useful Links
• Limitations and Future Work
• Team
• Acknowledgments
• License

💡 Introduction

Egypt has a unique and diverse climate — from rich soil types to varying temperatures and high solar radiation. This natural diversity opens doors to incredible opportunities in *agriculture, **renewable energy, and *sustainable development.

However, unlocking this potential starts with one key factor: understanding our climate accurately and in-depth. That's where our project comes in — transforming climate data into actionable insights.

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📊 Data Overview

Climate Data Features

Our dataset includes 48 climate features from NASA POWER API, covering:

Temperature Data

• Surface temperature (T2M)
• Maximum/minimum temperatures
• Soil temperatures at different depths

Solar Radiation

• Surface solar radiation
• UV index
• Cloud amount

Wind Data

• Wind speed at different heights
• Wind direction
• Wind gusts

Precipitation

• Total precipitation
• Snow precipitation
• Relative humidity

Soil Data

• Soil moisture at different depths
• Soil temperature profiles
• Land surface temperature

Data Coverage

• Time Period: 5 years of historical data
• Geographic Coverage: All of Egypt
• Spatial Resolution: 0.5° x 0.5° grid
• Temporal Resolution: Daily measurements

Data Sources

• Primary Source: NASA POWER API
• Supplementary Data:
  • Elevation data from Open Elevation API
  • Geographic features from Natural Earth
  • Water bodies from OpenStreetMap
  • Agricultural data from FAO

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📌 Problem Statement

Despite the richness of Egypt's environment, the lack of accurate, up-to-date, and connected climate data leads to poor decision-making, particularly in agriculture.

Many Egyptian farmers still rely on outdated information or personal intuition. They often lack tools to:

• Track and understand weather changes
• Analyze how climate affects soil quality
• Choose the best crops for each season

This gap in accessible climate intelligence also limits renewable energy planning and sustainable building design.

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🎯 Objectives

Our project aims to:

• Decode Egypt's climate using real data and advanced analytics
• Empower farmers with smart agricultural insights
• Help identify optimal regions for solar and wind energy
• Support sustainable urban planning decisions
• Align with Egypt Vision 2030 in food security, clean energy, and sustainable land use

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🧭 Alignment with Egypt Vision 2030

✅ Environmental Pillar

Supports sustainable use of Egypt's natural resources in agriculture and energy.

✅ Economic Pillar

Boosts agricultural productivity, reduces waste, and promotes renewable energy investment.

✅ Urban Development Pillar

Enables smart, climate-adaptive building and city planning through accurate weather-based insights.

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📊 Project Steps

1️⃣ Collecting and Cleaning Climate Data

We gathered climate data from trusted sources, covering:

• Wind
• Rainfall
• Temperature
• Soil types
• Solar radiation

All datasets were cleaned and pre-processed for accurate analysis.

2️⃣ Creating 5 Interactive Dashboards

Each dashboard visualizes a different climate factor across time and space:

Figure 1: Temperature Analysis Dashboard

Figure 2: Solar Radiation Analysis Dashboard

Figure 3: Wind Analysis Dashboard

Figure 4: Rainfall Analysis Dashboard

Figure 5: Soil Analysis Dashboard

These dashboards offer insights into how each factor behaves seasonally and geographically.

3️⃣ Predictive and Analytical Models 🤖

Climate Forecast Model

Uses LSTM to predict future climate conditions and support future planning.

Figure 6: LSTM Model Interface

Figure 7: LSTM Model Predictions

Geographic Clustering Model

Uses K-Means to group areas with similar climate traits, providing:

• Best planting seasons
• Suggested crops for each area

Figure 8: Agricultural Clusters Overview

Figure 9: Detailed Agricultural Clusters

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📁 Project Structure

DEPI_DATA/ ├── data ├── notebooks/ # Jupyter notebooks for analysis │ ├── EDA/ # Exploratory data analysis │ ├── Models/ # Model development notebooks │ └── Visualization/ # Visualization notebooks ├── src/ # Source code │ ├── models/ # ML models │ │ ├── lstm/ # LSTM model implementation │ │ └── clustering/ # K-means clustering │ └── visualization/ # Visualization code ├── StreamlitPage/ # Streamlit application │ └──application.py # Main Streamlit app ├── requirements.txt # Project dependencies └── README.md # Project documentation

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🧠 Tech Stack

📡 Data Sources

• – 48 climate features (5 years)
• – Elevation data
• – Coastline distances
• – Nile River data
• – Land cover and crop suitability

📊 Data Analysis & Modeling

• (Pandas, NumPy, ydata-profiling) – EDA and data cleaning
• – Map visualizations
• – Interactive web interface
• – Time series forecasting
• – Geographical clustering
• – Temporal and spatial features

📈 Visualization

• – Interactive dashboards
• – Final visualization layer

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🚀 Getting Started

Installation

bash pip install streamlit

Running the Application

1. Navigate to the project directory: bash cd DEPI_DATA

2. Run the Streamlit app: bash streamlit run app.py

Key Features

• Interactive Maps: Visualize climate data across Egypt
• Time Series Analysis: Track climate changes over time
• Predictive Models: View forecasts and predictions
• Data Export: Download processed data and visualizations

Figure 10: Streamlit Application Overview

Figure 11: Interactive Map Features

Figure 12: Data Analysis Features

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🔗 Useful Links

• NASA POWER API Documentation
• Open Elevation API
• Natural Earth Data
• OpenStreetMap
• FAO Data
• Streamlit Documentation
• Egypt Vision 2030

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🚧 Limitations and Future Work

Limitations: Simplified Assumptions in Clustering: The clustering model is based solely on climatic features; it doesn't yet include socio-economic or infrastructure data that may affect implementation. Limited Vegetation Recommendations: The plant suggestions are based on basic climate compatibility and don't yet account for market demand, soil nutrients, or water availability.

Future Work: Incorporate Socio-economic Data: Combine environmental insights with socio-economic indicators to support more realistic planning (e.g., cost, labor availability). Mobile Dashboard: Create a mobile-friendly version of the dashboard for use by farmers and field engineers.

👥 Team

This project was developed by a team of data analysis trainees as part of the final capstone project for the Digital Egypt Pioneers Initiative. The team members are:

• Ahmed Ashraf Labib
• Abdullah Saleh Mahmoud
• Mariam Ehab Mostafa
• Mohamed Ragab Attia
• Sara Ahmed Omar Ali
• Mohamed Sameh Abozaid

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🙏 Acknowledgments

This project was delivered as the final graduation project for the Digital Egypt Pioneers Initiative — an initiative by the Ministry of Communications and Information Technology (MCIT) in Egypt.

Special thanks to:

• CLS (Creative Learning Solutions) – Our training partner who supervised our learning journey and project development.
• Dr. Alaa Abdel-Moaty – Our lead instructor, whose guidance and support were fundamental to our success.

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.