Comparative evaluation of machine learning algorithms for integrated wetland modelling within a geospatial big data environment in Driefontein Grasslands of Zimbabwe

Introduction The spatial dynamics of wetlands require advanced geospatial modelling approaches capable of capturing nonlinear ecological interactions across heterogeneous landscapes. However, many wetland studies in sub-Saharan Africa rely on single-classifier approaches and limited predictor variables, resulting in reduced classification reliability and weak ecological interpretability. Methods This study addresses this gap by comparatively evaluating Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Trees (CART) for integrated wetland modelling within a geospatial big data environment in the Driefontein Grasslands, Zimbabwe, a Ramsar-designated wetland ecosystem. Multi-temporal Landsat imagery acquired for 2015, 2020, and 2025 was processed using Google Earth Engine, Python 3.10, and QGIS 3.44.6 Solothurn within a scalable cloud-supported analytical framework. To improve wetland discrimination, a comprehensive suite of remotely sensed spectral indices was integrated into the modelling workflow, including NDVI, EVI, SAVI, OSAVI, MSAVI, SIPI, GCI, RECI, NDWI and MNDWI. Model performance was evaluated using Receiver Operating Characteristic (ROC) curves, Area Under the Curve (AUC) statistics, and inter-model Pearson correlation analysis. Results RF demonstrated superior predictive stability and discriminatory performance across all epochs (AUC = 0.880–0.891), followed by SVM (0.850–0.873), while CART exhibited comparatively lower performance (0.749–0.789) and structural divergence through negative inter-model correlations. Spectral-index analysis revealed progressive vegetation decline, hydrological fragmentation, increasing vegetation stress, and accelerated conversion of vegetated wetland surfaces to bare substrates by 2025, signalling intensifying anthropogenic disturbance and ecological degradation. Discussion The findings demonstrate that integrating multi-index remote sensing analytics with ensemble machine learning significantly enhances wetland detection accuracy and ecological interpretation, providing a transferable GeoAI framework for scalable wetland monitoring, ecosystem restoration planning, and evidence-based environmental policy in Africa.