DATAGEEKN/Spatial-Analytics-El-Nino-Drought-Souther-Africa

# Predicting El Niño-Induced Drought Impacts on Food Security in Southern Africa Using Machine Learning and Spatial Analysis on Google Cloud Platform This project leverages machine learning and spatial analysis on Google Cloud Platform (GCP) to predict and mitigate the impacts of El Niño-induced drought on food security in Southern Africa. **Project Goals:** * Develop a predictive model to identify regions and populations at high risk of food insecurity due to drought. * Provide early warnings and actionable insights to support proactive interventions and build resilience. * Contribute to a better understanding of the complex dynamics between climate, agriculture, and food security in Southern Africa. **Methodology:** 1. **Data Acquisition and Preprocessing:** * Acquire and preprocess satellite imagery (Landsat, Sentinel-2) and climate data (CHIRPS, ERA5-Land) using Earth Engine. * Extract relevant spatial data from OpenStreetMap. * Gather socioeconomic data from the World Bank and national statistical offices. * Store and manage all data in BigQuery. 2. **Feature Engineering and Predictive Modeling:** * Create relevant features from the raw data, including drought indices and socioeconomic vulnerability indicators. * Train and evaluate machine learning models (e.g., Random Forests, Gradient Boosting Machines) using Vertex AI. 3. **Spatial Analysis and Visualization:** * Analyze the spatial distribution of predicted food insecurity risk using BigQuery GIS. * Create interactive maps to visualize risk levels and identify hotspots of vulnerability. 4. **Early Warning System and Decision Support:** * Define thresholds for food insecurity risk and develop an early warning system. * Provide actionable recommendations for interventions and policy actions. **Data Sources:** * Earth Engine: Landsat, Sentinel-2, CHIRPS, ERA5-Land * OpenStreetMap: Road networks, points of interest, administrative boundaries * World Bank: Socioeconomic data, agricultural production da …