Dam Water Level Prediction Using Vector AutoRegression, Random Forest Regression and MLP-ANN Models Based on Land-Use and Climate Factors

Yashon O. Ouma (), Ditiro B. Moalafhi, George Anderson, Boipuso Nkwae, Phillimon Odirile, Bhagabat P. Parida and Jiaguo Qi
Additional contact information
Yashon O. Ouma: Department of Civil Engineering, University of Botswana, Private Bag UB 0061, Gaborone, Botswana
Ditiro B. Moalafhi: Faculty of Natural Resources, Botswana University of Agriculture and Natural Resources (BUAN), Private Bag 0027, Gaborone, Botswana
George Anderson: Department of Computer Science, University of Botswana, Private Bag UB 0061, Gaborone, Botswana
Boipuso Nkwae: Department of Civil Engineering, University of Botswana, Private Bag UB 0061, Gaborone, Botswana
Phillimon Odirile: Department of Civil Engineering, University of Botswana, Private Bag UB 0061, Gaborone, Botswana
Bhagabat P. Parida: Department of Civil and Environmental Engineering, Botswana International University of Science and Technology (BIUST), Private Bag 16, Palapye, Botswana
Jiaguo Qi: Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI 48824, USA

Sustainability, 2022, vol. 14, issue 22, 1-31

Abstract: To predict the variability of dam water levels, parametric Multivariate Linear Regression (MLR), stochastic Vector AutoRegressive (VAR), Random Forest Regression (RFR) and Multilayer Perceptron (MLP) Artificial Neural Network (ANN) models were compared based on the influences of climate factors (rainfall and temperature), climate indices (DSLP, Aridity Index (AI), SOI and Niño 3.4) and land-use land-cover (LULC) as the predictor variables. For the case study of the Gaborone dam and the Bokaa dam in the semi-arid Botswana, from 2001 to 2019, the prediction results showed that the linear MLR is not robust for predicting the complex non-linear variabilities of the dam water levels with the predictor variables. The stochastic VAR detected the relationship between LULC and the dam water levels with R 2 > 0.95; however, it was unable to sufficiently capture the influence of climate factors on the dam water levels. RFR and MLP-ANN showed significant correlations between the dam water levels and the climate factors and climate indices, with a higher R 2 value between 0.890 and 0.926, for the Gaborone dam, compared to 0.704–0.865 for the Bokaa dam. Using LULC for dam water predictions, RFR performed better than MLP-ANN, with higher accuracy results for the Bokaa dam. Based on the climate factors and climate indices, MLP-ANN provided the best prediction results for the dam water levels for both dams. To improve the prediction results, a VAR-ANN hybrid model was found to be more suitable for integrating LULC and the climate conditions and in predicting the variability of the linear and non-linear time-series components of the dam water levels for both dams.

Keywords: Bokaa and Gaborone dams (Botswana); dam water levels; land-use land-cover; climate change; multivariate linear regression; Vector AutoRegressive (VAR); Random Forest Regression; Multilayer Perceptron ANN; VAR-Neural Network hybrid model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/22/14934/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/22/14934/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:22:p:14934-:d:970015

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().