PDAT 617: Streamflow Prediction Using Machine Learning

Project Overview

This project applies machine learning techniques in Python to predict streamflow using catchment attribute data. The analysis leverages decision trees and random forests to model and forecast water flow patterns based on watershed characteristics.

Research Objective

Develop predictive models for streamflow using catchment attributes such as topography, land cover, soil properties, and climate variables to accurately forecast water flow in different watersheds.

Data

• Catchment Attribute Data: Physical and environmental characteristics of watersheds
• Streamflow Measurements: Historical flow data for model training and validation

Methodology

Machine Learning Approaches

1. Decision Trees: Single tree models for interpretable streamflow predictions
2. Random Forests: Ensemble method combining multiple decision trees for improved accuracy and robustness

Workflow

• Data preprocessing and feature engineering
• Model training and hyperparameter tuning
• Cross-validation and performance evaluation
• Feature importance analysis
• Model comparison and selection

Technologies Used

• Python: Primary programming language
• scikit-learn: Machine learning algorithms
• pandas: Data manipulation and analysis
• NumPy: Numerical computing
• Matplotlib/seaborn: Data visualization

Key Findings

The project demonstrates the effectiveness of tree-based machine learning methods for hydrological prediction, with random forests generally providing superior performance due to their ensemble nature and ability to capture complex relationships in catchment data.

Academic Context

• Program: Master of Data Science
• Course: PDAT 617 - Python for Data Science
• Author: Brian Bruxvoort

Repository Contents

• Jupyter notebook with complete analysis
• Project report with methodology and results
• Data preprocessing scripts
• Model evaluation metrics

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This project showcases Python programming skills and machine learning expertise applied to environmental data science.