Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land–Water Nexus

Yashon O. Ouma (), Boipuso Nkwae, Phillimon Odirile, Ditiro B. Moalafhi, George Anderson, Bhagabat Parida and Jiaguo Qi
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Yashon O. Ouma: Department of Civil Engineering, University of Botswana, Gaborone Private Bag UB0061, Botswana
Boipuso Nkwae: Department of Civil Engineering, University of Botswana, Gaborone Private Bag UB0061, Botswana
Phillimon Odirile: Department of Civil Engineering, University of Botswana, Gaborone Private Bag UB0061, Botswana
Ditiro B. Moalafhi: Faculty of Natural Resources, BUAN, Gaborone Private Bag 0027, Botswana
George Anderson: Department of Computer Science, University of Botswana, Gaborone Private Bag UB0061, Botswana
Bhagabat Parida: Department of Civil and Environmental Engineering, BIUST, Palapye Private Bag 16, Botswana
Jiaguo Qi: Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48824, USA

Sustainability, 2024, vol. 16, issue 4, 1-30

Abstract: 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.

Keywords: land-use land-cover (LULC) change; logistic regression; artificial neural network; cellular automata; random forest regression; sustainable land–water nexus (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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