Ysagar-hub/KerichoClimateRiskpred-

Kericho Multi-Hazard Climate Risk Model is a Python tool that uses NASA POWER weather data to assess agricultural climate risk in Kericho, Kenya. It detects key hazards, builds a Climate Risk Index, estimates crop loss and economic impact. Author: Sagar Kumar KerichoClimateRiskpred is a Python project that estimates climate risk for agriculture in Kericho, Kenya using daily weather data from the NASA POWER API (2010 to present). The script identifies common weather hazards, combines them into a Climate Risk Index (CRI), and uses that index to approximate crop loss and economic impact. It also labels each year into a simple risk category (low/medium/high), calculates an example insurance premium, and trains a Random Forest model to predict yearly risk levels. Finally, it produces graphs showing CRI trends, estimated losses, and hazard importance. Location and data source Location: Kericho, Kenya Latitude: -0.367 Longitude: 35.283 Data source: NASA POWER (daily data for a single point) Variables used: T2M (average temperature) T2M_MAX (maximum temperature) T2M_MIN (minimum temperature) PRECTOTCORR (precipitation) WS2M (wind speed) RH2M (relative humidity) What the script does (step-by-step) Downloads daily weather data from NASA POWER. Cleans the dataset by replacing -999 with missing values and filling gaps. Creates hazard indicators: heavy rain (precipitation > 50 mm/day) dry day (precipitation 32°C) high wind (wind speed > 6 m/s) Detects hail events using a rule-based proxy (rain + cold minimum temperature + wind + high humidity). Builds a daily CRI score using weighted hazards, then aggregates it to an annual CRI (scaled 0–100). Estimates crop loss percentage and converts it into economic loss using farm area and value per hectare. Assigns a yearly risk class (LOW, MEDIUM, HIGH) and calculates an example insurance premium. Trains a Random Forest model using annual hazard totals to predict the yearly risk class. Saves results to CSV files and creates plots for CRI, loss trends, and feature importance. How to run Install dependencies: pip install -r requirements.txt Run the script: python weatherrisk0.py Replace weatherrisk0.py with whatever you saved the file as. Output files CSV outputs: …