Authors:
Yashon Ouma
1
;
Phillimon Odirile
1
;
Boipuso Nkwae
1
;
Ditiro Moalafhi
2
;
George Anderson
3
;
Bhagabat Parida
4
and
Jiaguo Qi
5
Affiliations:
1
Department of Civil Engineering, University of Botswana, Gaborone, Botswana
;
2
DWAR, Botswana University of Agriculture and Natural Resources, Gaborone, Botswana
;
3
Department of Computer Science, University of Botswana, Gaborone, Botswana
;
4
Department of Civil and Environmental Engineering, BIUST, Palapye, Botswana
;
5
Center for Global Change and Earth Observations, Michigan State University, U.S.A.
Keyword(s):
Multispectral UAV-Drone, Sentinel-2 MSI Satellite, Water Quality, Gaborone Dam (Botswana), Turbidity, Total Suspended Solids (TDS), Empirical Linear Regression, XGBoost (eXtreme Gradient Boosting), Random Forest Regression.
Abstract:
This study presents results on the utility of DJI P4 Multispectral (DJI-PH4) UAV-Drone and Sentinel-2 MSI
(S2-MSI) satellite datasets for the retrieval of Turbidity and Total Dissolved Solids (TDS) using empirical
linear regression (ELR), XGBoost (eXtreme Gradient Boosting) and Random Forest Regression (RFR)
machine learning (ML) models. For the case study of Gaborone dam in Botswana, 21 water sampling points
were correlated with the corresponding spectral reflectances from DJI-PH4 and S2-MSI imagery. For the
estimation of Turbidity, XGBoost gave the best prediction results with average training accuracy of R2 = NSE
= 0.999, MAE=0.001 NTU, RMSE = 0.001 NTU and PBIAS = 0.1% for both the DJI-PH4 and S2-MSI
sensors. XGBoost performed better than ELR and RFR at the model training phases, however its prediction
of Turbidity in testing was lower than ELR but nearly same as RFR. In predicting TDS from both sensors,
XGBoost had the highest performance with equivalent accuracy measur
es as for the prediction of Turbidity.
Both the training and testing results for the estimation of TDS is accurate from the sensors, with ELR
marginally outperforming the XGBoost and RFR in the testing phase with R2 = 0.998, MAE=0.338 mg/L,
RMSE = 0.435 mg/L and NSE = 0.858. For the prediction of Turbidity, all the ML models gave good training
results from the drone and Sentinel-2 data except for RFR in the case of Sentinel-2. The introduction of
ensemble ELR-XGBoost model significantly improved the prediction of the water quality parameters from
the drone and Sentinel-2 datasets. With the potential of providing high-frequency and large spatial coverage
observational data in the near-real-time mode, the results of this study demonstrate the applicability of UAVdrone for the retrieval of Turbidity and TDS physical water quality parameter in dam reservoirs.
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