Advancing Weather Prediction with Machine Learning and Python

Local Chapter Karachi, Pakistan

Coordinated byPakistan ,

Talha Mehfooz Khurshaidi

Status: Ongoing

Project background.

– Economic losses: Between 1992 and 2021, climate- and weather-related disasters in Pakistan resulted in a total of US$29.3 billion of economic losses (inflation-adjusted to 2021 US dollars) from damage to property, crops, and livestock, equivalent to 11.1% of 2020 GDP.
– Loss of life: Over the same period, these disasters also caused the deaths of an estimated 20,000 people.
– Displacement: An estimated 10 million people have been displaced by weather-related disasters in Pakistan since 1992.
These statistics show that the impact of weather-related disasters in Pakistan is significant in terms of economic losses and loss of life. Accurate weather forecasts could help reduce these losses by providing early warning of hazardous weather conditions, allowing people to protect themselves and their property.

The problem.

Problem: Weather forecasts in Pakistan are often inaccurate, which can lead to problems for businesses, government agencies, and individuals.
Impact: A more accurate weather forecasting model would have a significant impact on the people of Pakistan, helping businesses make informed decisions, government agencies plan for natural disasters, and individuals protect themselves from extreme weather.
How the challenge will help: The challenge will help solve this problem by allowing participants to develop a more accurate weather forecasting model.

Project goals.

The goal of this project is to develop a machine learning model that can improve the accuracy of weather forecasts for Pakistan. The model will be trained on a dataset of historical weather data, and it will be able to predict future weather conditions with greater accuracy than current models.

Project plan.

  • Week 1

    Week 1: Data extraction and cleaning:
    – Identify and collect relevant weather data sources.
    – Clean and prepare the data for modeling.

  • Week 2

    Week 2: Model development, Explore different machine learning models for weather forecasting.

  • Week 3

    Week 3: Model evaluation, Train and evaluate different models.

  • Week 4

    Week 4: Deployment, Evaluate the performance of the best model on a holdout dataset.

  • Week 5

    Week 5: Visualization and communication:
    – Create visualizations of the results of the challenge.
    – Write a report summarizing the results of the challenge.

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