Fire Hotspots Prediction AI Model
Budget: $250 – $750 USD
Seeking a dedicated professional to develop a machine learning model aimed at early detection of fire hotspots.
Requirements:
1. Deep Knowledge of Machine Learning: The task entails developing a predictive model using AI, hence profound expertise in machine learning will be key.
2. Python Programming: The preferred language for implementing this task is Python.
3. Geospatial Analysis: As the model will deal with region-specific data, familiarity and understanding of geospatial analysis is crucial.
Work Plan:
• Understanding the nature of MODIS fire hotspots
• Training the ML model on the data specific to a certain country or region
• Output should be a Python-based machine learning model that can successfully predict early fire hotspots
Input data provided:
- MODIS csv daily fire hotspots over southeast asia since the year 2000
- MODIS NetCDF monthly burn scars over southeast asia since the year 2000
-ERA5 NetCDF daily meteorology included precipitation, maximum temperature, minimum temperature, relative humidity, plenary boundary layer hight, wind speed and direction.
Key Project Requirement:
- Real-time data processing: The AI system should be capable of predicting fire hotspot trends (date, Lon, Lat) in real-time, processing large amounts of MODIS data swiftly and accurately.
Applicant's Profile: - Experience: Ideally, the freelancer should have prior experience in working with predictive AI models, preferably in environmental, geospatial or disaster management contexts. In your application, please highlight your relevant experience and explain how your expertise would benefit this project's real-time processing requirements. Your proven track-record and expertise in similar projects will be a considerable advantage.
If you're the professional who thrives in challenging environment and has a knack in machine learning and geospatial analysis, we need you for this impactful project.
Requirements:
1. Deep Knowledge of Machine Learning: The task entails developing a predictive model using AI, hence profound expertise in machine learning will be key.
2. Python Programming: The preferred language for implementing this task is Python.
3. Geospatial Analysis: As the model will deal with region-specific data, familiarity and understanding of geospatial analysis is crucial.
Work Plan:
• Understanding the nature of MODIS fire hotspots
• Training the ML model on the data specific to a certain country or region
• Output should be a Python-based machine learning model that can successfully predict early fire hotspots
Input data provided:
- MODIS csv daily fire hotspots over southeast asia since the year 2000
- MODIS NetCDF monthly burn scars over southeast asia since the year 2000
-ERA5 NetCDF daily meteorology included precipitation, maximum temperature, minimum temperature, relative humidity, plenary boundary layer hight, wind speed and direction.
Key Project Requirement:
- Real-time data processing: The AI system should be capable of predicting fire hotspot trends (date, Lon, Lat) in real-time, processing large amounts of MODIS data swiftly and accurately.
Applicant's Profile: - Experience: Ideally, the freelancer should have prior experience in working with predictive AI models, preferably in environmental, geospatial or disaster management contexts. In your application, please highlight your relevant experience and explain how your expertise would benefit this project's real-time processing requirements. Your proven track-record and expertise in similar projects will be a considerable advantage.
If you're the professional who thrives in challenging environment and has a knack in machine learning and geospatial analysis, we need you for this impactful project.