saadaal-dev/saadaal-flood-forecaster
Nov 2024 – Jun 2025
2 Months active
Languages Used
CSVPythonBashSQL
Technical Skills
Data EngineeringData ScienceMachine LearningPandasPythonScikit-learn
maximepeter
PROFILE
Maximepeter
Maxime Peter developed end-to-end flood forecasting and risk assessment capabilities for the saadaal-dev/saadaal-flood-forecaster repository over a two-month period. He built a PCA-enabled linear regression model that predicts river levels using historical weather and river data, applying data preprocessing, time-shifted rainfall feature engineering, and dimensionality reduction with scikit-learn and pandas. Maxime also introduced a RiverStation data model and automated the forecasting workflow with Bash scripting and Python, integrating risk scoring logic and daily scheduling via cron. His work established a robust, automated pipeline for flood risk prediction, demonstrating depth in backend development, data engineering, and machine learning integration.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
3
Lines of code
14,237
Activity Months2
Your Network
10 peopleWork History
June 2025
5 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for saadaal-dev/saadaal-flood-forecaster: delivered end-to-end Flood Risk Assessment capabilities and automated forecasting orchestration, with ML integration improvements and deployment-oriented enhancements.
5 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for saadaal-dev/saadaal-flood-forecaster: delivered end-to-end Flood Risk Assessment capabilities and automated forecasting orchestration, with ML integration improvements and deployment-oriented enhancements.
June 2025
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024: Delivered a PCA-based feature reduction-enabled flood forecasting model in saadaal-dev/saadaal-flood-forecaster. Established a models directory and added a linear regression-based forecasting model that uses historical weather data from three Ethiopian stations and river level data from Jowhar to predict future river levels. Implemented data preprocessing, time-shifted rainfall feature engineering, PCA for dimensionality reduction, and persistence of the trained model and PCA components for inference. This work enables proactive flood risk forecasting to support planning, emergency response, and resource allocation.
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024: Delivered a PCA-based feature reduction-enabled flood forecasting model in saadaal-dev/saadaal-flood-forecaster. Established a models directory and added a linear regression-based forecasting model that uses historical weather data from three Ethiopian stations and river level data from Jowhar to predict future river levels. Implemented data preprocessing, time-shifted rainfall feature engineering, PCA for dimensionality reduction, and persistence of the trained model and PCA components for inference. This work enables proactive flood risk forecasting to support planning, emergency response, and resource allocation.
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture76.6%
Performance70.0%
AI Usage23.4%
Skills & Technologies
Programming Languages
BashCSVPythonSQL
Technical Skills
AutomationBackend DevelopmentConfiguration ManagementData EngineeringData ScienceDatabase InteractionDatabase ManagementDependency ManagementDevOpsMachine LearningObject-Oriented ProgrammingPandasPythonRefactoringSQLAlchemy
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