Version 1
: Received: 9 April 2024 / Approved: 10 April 2024 / Online: 10 April 2024 (08:18:12 CEST)
How to cite:
Bhungeni, O.; Ramjatan, A.; Gebreslasie, M. Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal. Preprints2024, 2024040713. https://doi.org/10.20944/preprints202404.0713.v1
Bhungeni, O.; Ramjatan, A.; Gebreslasie, M. Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal. Preprints 2024, 2024040713. https://doi.org/10.20944/preprints202404.0713.v1
Bhungeni, O.; Ramjatan, A.; Gebreslasie, M. Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal. Preprints2024, 2024040713. https://doi.org/10.20944/preprints202404.0713.v1
APA Style
Bhungeni, O., Ramjatan, A., & Gebreslasie, M. (2024). Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal. Preprints. https://doi.org/10.20944/preprints202404.0713.v1
Chicago/Turabian Style
Bhungeni, O., Ashadevi Ramjatan and Michael Gebreslasie. 2024 "Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal" Preprints. https://doi.org/10.20944/preprints202404.0713.v1
Abstract
Analysis of land use/land cover (LULC) in the catchment areas is the first action toward safeguarding the freshwater resources. The LULC information in the watershed has gained popularity in the natural science field as it helps water resource managers and environmental health specialists develop natural resource conservation strategies based on available quantitative information. Thus, remote sensing is the cornerstone in addressing environmental-related issues at the catchment level. In this study, the performance of four machine learning algorithms (MLAs), such as Random Forests (RF), Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Naïve Bayes (NB) was investigated to classify the catchment into nine relevant classes of the undulating watershed landscape using Landsat 8 Operational Land Imager (L8-OLI) imagery. The assessment of the MLAs were based on the visual inspection of the analyst and the commonly used assessment metrics, such as user’s accuracy (UA), producers’ accuracy (PA), overall accuracy (OA), and kappa coefficient. The MLAs produced good results, where RF (OA= 97.02%, Kappa= 0.96), SVM (OA= 89.74 %, Kappa= 0.88), ANN (OA= 87%, Kappa= 0.86), and NB (OA= 68.64 Kappa= 0.58). The results show the outstanding performance of the RF model over SVM and ANN with a small margin. While NB yielded satisfactory results, which could be primarily influenced by its sensitivity to limited training samples. In contrast, the robust performance of RF could be due to an ability to classify high-dimensional data with limited training data.
Keywords
uMngeni River Catchment; Machine learning; LULC; Landsat 8; Remote sensing
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
