article · Sustainability
Accurate prediction of land-use and land-cover change is essential for sustainable water resource management in dam catchments. This research evaluates a random forest regression model against logistic regression and artificial neural network cellular automata approaches to forecast catchment trends from 2019 to 2030 in the Gaborone dam catchment in Botswana. The models incorporated physiographic variables, such as elevation and slope, alongside proximity to roads, water bodies, and urban zones. In historical simulations between 1986 and 2019, random forest regression achieved 84.9 percent accuracy, substantially outperforming the alternative techniques. Projections to 2030 indicate an increase in bare soil and built-up land, alongside decreases in vegetation and cropland, while water body extent showed negligible change. The growth in built-up areas correlated with a decreasing dam water capacity, demonstrating the utility of predictive modelling for catchment monitoring.
Expanding urban settlements and shifting landscapes place growing stress on regional water reserves. By reliably anticipating changes in vegetation, soil, and built environments, this analytical approach clarifies how catchment alterations influence available dam storage. The findings provide critical evidence to support environmental authorities and regional planners seeking to protect vital water infrastructure against the pressures of population growth and landscape degradation.
This methodology offers practical utility for catchment management authorities, water utilities, and municipal planning departments seeking to formulate long-term monitoring and development strategies. Based on the abstract, the approach represents applied and tested research within a specific catchment setting. Deployment into commercial or operational environments would require packaging the analytical framework into specialised planning software or advisory services for environmental consultancies and public planning agencies.
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For sustainable water resource management within dam catchments, accurate knowledge of land-use and land-cover change (LULCC) and the relationships with dam water variability is necessary. To improve LULCC prediction, this study proposes the use of a random forest regression (RFR) model, in comparison with logistic regression–cellular automata (LR-CA) and artificial neural network–cellular automata (ANN-CA), for the prediction of LULCC (2019–2030) in the Gaborone dam catchment (Botswana). RFR is proposed as it is able to capture the existing and potential interactions between the LULC intensity and their nonlinear interactions with the change-driving factors. For LULCC forecasting, the driving factors comprised physiographic variables (elevation, slope and aspect) and proximity-neighborhood factors (distances to water bodies, roads and urban areas). In simulating the historical LULC (1986–2019) at 5-year time steps, RFR outperformed ANN-CA and LR-CA models with respective percentage accuracies of 84.9%, 62.1% and 60.7%. Using the RFR model, the predicted LULCCs were determined as vegetation (−8.9%), bare soil (+8.9%), built-up (+2.49%) and cropland (−2.8%), with water bodies exhibiting insignificant change. The correlation between land use (built-up areas) and water depicted an increasing population against decreasing dam water capacity. The study approach has the potential for deriving the catchment land–water nexus, which can aid in the formulation of sustainable catchment monitoring and development strategies.
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DOI: 10.3390/su16041699
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