Explainability of MLP Based Species Distribution Models: A Case Study

Imane El Assari, Hajar Hakkoum, Ali Idri, Ali Idri

2023

Abstract

Species Distribution models (SDMs) are widely used to study species occurrence in conservation science and ecology evolution. However the huge amount of data and its complexity makes it difficult for professionals to forecast the evolutionary trends of distributions across the concerned landscapes. As a solution, machine learning (ML) algorithms were used to construct and evaluate SDMs in order to predict the studied species occurrences and their habitat suitability. Nevertheless, it is critical to ensure that ML based SDMs reflect reality by studying their trustworthiness. This paper aims to investigate two techniques: SHapley Additive exPlanations (SHAP) and the Partial Dependence Plot (PDP) techniques to interpret a Multilayer perceptron (MLP) trained on the Loxodonta Africana dataset. Results demonstrate the prediction process and how in- terpretability techniques could be used to explain misclassified instances and thus increase trust between ML results and domain experts.

Paper Citation

in Harvard Style

El Assari I., Hakkoum H. and Idri A. (2023). Explainability of MLP Based Species Distribution Models: A Case Study. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 690-697. DOI: 10.5220/0011745300003393

in Bibtex Style

@conference{icaart23,
author={Imane El Assari and Hajar Hakkoum and Ali Idri},
title={Explainability of MLP Based Species Distribution Models: A Case Study},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={690-697},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011745300003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Explainability of MLP Based Species Distribution Models: A Case Study
SN - 978-989-758-623-1
AU - El Assari I.
AU - Hakkoum H.
AU - Idri A.
PY - 2023
SP - 690
EP - 697
DO - 10.5220/0011745300003393