Heterogeneous Ensemble Learning for Modelling Species Distribution: A Case Study of Redstarts Habitat Suitability

Omar El Alaoui, Ali Idri, Ali Idri

2023

Abstract

Habitat protection is a critical aspect of species conservation, as restoring a habitat to its former state after it has been destroyed can be difficult. Species Distribution Models (SDMs), also known as habitat suitability models, are commonly used to address this issue. It finds ecological and evolutionary insights by linking species occurrences records to environmental data. Machine learning (ML) algorithms have been recently used to predict the distribution of species. Yet, a single ML algorithm may not always yield accurate predictions for a given dataset, making it challenging to develop a highly accurate model using a single algorithm. Therefore, this study proposes a novel approach to assess habitat suitability of three redstarts species based on ensemble learning techniques. Initially, eight machine learning algorithms, including MultiLayer Perceptron (MLP), Support Vector Machine (SVM), K-nearest neighbors (KNN), Decision Trees (DT), Gradient Boosting Classifier (GB), Random Forest (RF), AdaBoost (AB), and Quadratic Discriminant Analysis (QDA), were trained as base-learners. Subsequently, based on the performance of these base-learners, seven heterogeneous ensembles of two up to eight models, were constructed for each species dataset. The performance of the proposed approach was evaluated using five performance criteria (accuracy, sensitivity, specificity, AUC, and Kappa), Scott Knott (SK) test to statistically compare the performance of the presented models, and the Borda Count voting method to rank the best performing models based on multiple performance criteria. The findings revealed that the heterogeneous ensembles outperformed their singles in all three species datasets, underscoring the efficacy of the proposed approach in modelling species distribution.

Paper Citation

in Harvard Style

El Alaoui O. and Idri A. (2023). Heterogeneous Ensemble Learning for Modelling Species Distribution: A Case Study of Redstarts Habitat Suitability. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-664-4, SciTePress, pages 105-114. DOI: 10.5220/0012118100003541

in Bibtex Style

@conference{data23,
author={Omar El Alaoui and Ali Idri},
title={Heterogeneous Ensemble Learning for Modelling Species Distribution: A Case Study of Redstarts Habitat Suitability},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2023},
pages={105-114},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012118100003541},
isbn={978-989-758-664-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Heterogeneous Ensemble Learning for Modelling Species Distribution: A Case Study of Redstarts Habitat Suitability
SN - 978-989-758-664-4
AU - El Alaoui O.
AU - Idri A.
PY - 2023
SP - 105
EP - 114
DO - 10.5220/0012118100003541
PB - SciTePress