Can you really predict wildfire hotspots using California wildfire data?

Wildfires are a global problems for several countries and threaten lives, communities, wildlife, and forests every year. Wildfire also cause significant changes into out climate.
With this wildfire project my objective is to make everyone knowledgeable about how to get historical and latest wildfire data for any country and then use data engineering, visualization and machine learning technology to provide assistance in solving this problem at their own level.
This tutorial is divided into 2 parts as below:
Part 1: Global Wildfire Data Collection
- Harvest data from NASA website
- Create USA/CA/Nepal dataset
- Create any country wildfire dataset
- Visualize any wildfire data with mapboxgl, matplotlib and Kepler.gl
- Build streamlit app to show wildfire
for any country/region by year
Part 2: Wildfire Hotspot Prediction (This tutorial)
- Use California wildfire dataset as source for this project
- Develop wildfire hotspot detection strategy
- feature engineering
- Build ML model with various algorithms/libraries (LightGBM, xgboost, H2O.ai open source ML platform)
- Validate and Test ML Models
- Predict wildfire hotspots
Content Timeline:
----------------------------
- (00:00) Content start
- (00:16) Topic introduction
- (02:12) Data Collection part-1 recap
- (03:05) California Wildfire Dataset
- (04:55) Tutorial Starts
- (05:18) Machine Learning Strategy
- (10:29) Feature Engineering
- (29:20) Final ML Ready Dataset
- (30:47) Wildfire Hotspot Visualization
- (34:38) Google colab notebooks
- (35:22) Wildfire Hotspot Model by LightGBM
- (41:50) Wildfire Hotspot Model by xgboost
- (45:16) Wildfire Hotspot Model by H2O.ai
- (51:36) Recap
- (54:38) Project Completion
How to create wildfire dataset for any country or region:
github.com/prodramp/wildfire/...
Wildfire Hotspot Prediction ML Models:
github.com/prodramp/wildfire/...
Streamlit application to show wildfire data for any country by year:
github.com/prodramp/wildfire/...
Download California Wildfire Dataset from Kaggle:
www.kaggle.com/datasets/avkas...
Please visit:
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Prodramp LLC | prodramp.com | @prodramp
/ prodramp
Content Creator: Avkash Chauhan (@avkashchauhan)
/ avkashchauhan
Tags:
#ai #aicloud #h2oai #driverlessai #machinelearning #cloud #mlops #model #collaboration #deeplearning #modelserving #modeldeployment #keras #tensorflow #pytorch #datarobot #datahub #aiplatform #aicloud #modelperformance #modelfit #modeleffect #modelimpact #modelbias #modeldeployment #modelregistery #modelpipeline #neptuneai #pythondsp #pythonaudio #streamlit #pythonapps #deepchecks #modeltesting #codeartifact #dataartifact #modelartifact #onnx #aws #supervisor #ec2 #supervisord #kaggle #wildfire #naturaldisaster #kepler.gl #mapbox mapboxgl #wildfireml #lightgbm #xgboost #classification #regression

[@souravkarmakar1]

Sir kindly share the weather data with the wildfire, video, Thank you

[@charithanagamalla9049]

Sir, is there any reason that you have decided to take xgbc and lightbgm models for Modeling here !? What are the reasons for that to choose these algorithms !?