Climate Prediction with Tree Structure Based on Random Forest
DOI:
https://doi.org/10.62051/akf9ef10Keywords:
Climate Prediction; Data Frames; Random Forest; Mean Absolute Error Loss.Abstract
Climate prediction refers to the use of scientific methods and techniques to predict and analyze changes in the natural environment at a certain point in the future. This paper aims to improve climate prediction by analyzing its parameters and impacts using Random Forest (RF) techniques. Specifically, firstly, data frames (DF) are manipulated to explore data features, perform visualization, and perform preprocessing. Second, the paper introduces RF as a base model as a backbone network to accurately estimate climate change. RF is robust to outliers in the data using a tree-based approach. By integrating multiple decision trees and introducing stochasticity (e.g., randomly selecting features) during the training process, RFs are effective in reducing the risk of overfitting and improving the model's generalization ability. Third, mean absolute error loss (MAPE loss) is used to compare the errors that are evaluated on the data. The experimental outcomes demonstrate the efficacy of the proposed model in climate prediction, with the accuracy increased to 94%. Applying the proposed model in climate prediction in this paper provides valuable insights for climate prediction.
Downloads
References
K. Michael, et al. Integrating Biophysical Models and Evolutionary Theory to Predict Climatic Impacts on Species’ Ranges: The Dengue MosquitoAedes Aegyptiin Australia. Functional Ecology, 23(3), 2009, pp.528–538.
Tierney, E. Jessica, et al. Past Climates Inform Our Future. Science, 370(6517), 2020.
Boulesteix, A. Laure, et al. Overview of Random Forest Methodology and Practical Guidance with Emphasis on Computational Biology and Bioinformatics. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(6), 2012, pp. 493–507.
Gall, Juergen, et al. An Introduction to Random Forests for Multi-Class Object Detection. Lecture Notes in Computer Science, 2012, pp. 243–263.
L. Bingguo, et al. Scalable Random Forests for Massive Data. Lecture Notes in Computer Science, 2012, pp. 135–146.
Breiman, Leo. Random Forests. Machine Learning, 45(1), 2001, pp. 5–32, pp.1471-2105.
S. Carolin, et al. Conditional Variable Importance for Random Forests. BMC Bioinformatics, 9(11), 2008.
Information on: www.kaggle.com/code/anandhuh/climate-prediction-random-forest-94-accuracy/notebook.
Petersohn, Devin, et al. Towards Scalable Dataframe Systems. ArXiv.org, 2020.
V. Eliana, et al. A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score. Entropy, 22(12), 2020, p. 1412.
Information on: www.databricks.com/glossary/what-are-dataframes.
Information on: www.aporia.com/learn/a-comprehensive-guide-to-mean-absolute-percentage-error-mape.
Downloads
Published
Conference Proceedings Volume
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.