W207 Final Project

Forest Cover Type Prediction

Berkeley MIDS Program

Team: Blair Witch Project

Sharon Wu, Kevin Ngo, Vish Pillai, Blair Jones

Date: 11 April 2021

Contents

Folder	Description
0_experiments	Individual team folders for EDA and model development.
2021_03_09	Initial draft of report.
2021_04_11_Final	Final version of report and team notebook.

Final Report Presentation

Presentation

Introduction

The challenge is to predict seven different forest cover types in four different wilderness areas of the Roosevelt National Forest of Northern Colorado with the best accuracy. These areas represent forests with minimal human-caused disturbances, so that existing forest cover types are more a result of ecological processes rather than forest management practices.

Each 30 x 30 square meter section of land is described by 54 attributes (ex. elevation, area, soil type, distance to water, aspect, etc.).

The 4 wilderness areas:

• Rawah Wilderness Area
• Neota Wilderness Area
• Comanche Peak Wilderness Area
• Cache la Poudre Wilderness Area

The 7 forest cover types:

1. Spruce/Fir
2. Lodgepole Pine
3. Ponderosa Pine
4. Cottonwood/Willow
5. Aspen
6. Douglas-fir
7. Krummholz

Data

• Elevation - Elevation in meters
• Aspect - Aspect in degrees azimuth
• Slope - Slope in degrees
• Horizontal_Distance_To_Hydrology - Horz Dist to nearest surface water features
• Vertical_Distance_To_Hydrology - Vert Dist to nearest surface water features
• Horizontal_Distance_To_Roadways - Horz Dist to nearest roadway
• Hillshade_9am (0 to 255 index) - Hillshade index at 9am, summer solstice
• Hillshade_Noon (0 to 255 index) - Hillshade index at noon, summer solstice
• Hillshade_3pm (0 to 255 index) - Hillshade index at 3pm, summer solstice
• Horizontal_Distance_To_Fire_Points - Horz Dist to nearest wildfire ignition points
• Wilderness_Area (4 binary columns, 0 = absence or 1 = presence) - Wilderness area designation
• Soil_Type (40 binary columns, 0 = absence or 1 = presence) - Soil Type designation
• Cover_Type (7 types, integers 1 to 7) - Forest Cover Type designation

Summary

After thorough EDA, we decided to apply several transformations to the data to highlight key differences between the cover types across the explanatory variables.

With the transformed data, we analyzed the characteristics of several different machine learning models for this type of classification problem.

After some iterations to tune the features and hyperparameter, we conducted a final comparison, shown below. The ExtraTrees Classifier achieved the best performance in terms of accuracy and execution time.

We also experimented with a hybrid model, using the Voting Classifier, which takes the output from 2 or more models and determines the best prediction for each sample from across the input models. The Voting Classifier with 8 ExtraTrees models increased the accuracy by 0.2%, but took 9 times longer to train and predict.

The full analysis is described in the final report notebook and presentation.

Model	Accuracy	Time	Comments
KNN	0.811	-	k=1
Decision Tree	0.771	-	trees=19
Random Forest	0.868	12 sec	-
ExtraTrees	0.911	5 sec	random_state=1189
ExtraTrees Voting	0.913	45 sec	trees=100, random_state=[1189,883,2153,5568,2077,769,2223,2675]
XGBoost	0.865	193 sec	-
LightGBM	0.897	72 sec	boosting_type='gbdt', trees=200
CatBoost	0.876	96 sec	-
Neural Network	0.865	192 sec	layers=6