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. 2021;23(12):18252-18277.
doi: 10.1007/s10668-021-01437-6. Epub 2021 Apr 21.

Remote sensing-based water quality index estimation using data-driven approaches: a case study of the Kali River in Uttar Pradesh, India

Saif Said  1 , Shadab Ali Khan  1
Affiliations

Remote sensing-based water quality index estimation using data-driven approaches: a case study of the Kali River in Uttar Pradesh, India

Saif Said et al. Environ Dev Sustain. 2021.

Abstract

The present study evaluates the water quality status of 6-km-long Kali River stretch that passes through the Aligarh district in Uttar Pradesh, India, by utilizing high-resolution IRS P6 LISS IV imagery. In situ river water samples collected at 40 random locations were analyzed for seven physicochemical and four heavy metal concentrations, and the water quality index (WQI) was computed for each sampling location. A set of 11 spectral reflectance band combinations were formulated to identify the most significant band combination that is related to the observed WQI at each sampling location. Three approaches, namely multiple linear regression (MLR), backpropagation neural network (BPNN) and gene expression programming (GEP), were employed to relate WQI as a function of most significant band combination. Comparative assessment among the three utilized approaches was performed via quantitative indicators such as R 2, RMSE and MAE. Results revealed that WQI estimates ranged between 203.7 and 262.33 and rated as "very poor" status. Results further indicated that GEP performed better than BPNN and MLR approaches and predicted WQI estimates with high R 2 values (i.e., 0.94 for calibration and 0.91 for validation data), low RMSE and MAE values (i.e., 2.49 and 2.16 for calibration and 4.45 and 3.53 for validation data). Moreover, both GEP and BPNN depicted superiority over MLR approach that yielded WQI with R 2 ~ 0.81 and 0.67 for calibration and validation data, respectively. WQI maps generated from the three approaches corroborate the existing pollution levels along the river stretch. In order to examine the significant differences among WQI estimates from the three approaches, one-way ANOVA test was performed, and the results in terms of F-statistic (F = 0.01) and p-value (p = 0.994 > 0.05) revealed WQI estimates as "not significant," reasoned to the small water sample size (i.e., N = 40). The study therefore recommends GEP as more rational and a better alternative for precise water quality monitoring of surface water bodies by producing simplified mathematical expressions.

Keywords: ANN; GEP; Kali River; MLR; Spectral reflectance; WQI.

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Figures

Fig. 1
Fig. 1
Location map of the study area (map not to scale)
Fig. 2
Fig. 2
Subset image of study area with sampling locations along the river stretch
Fig. 3
Fig. 3
Neural network architecture with input variables as bands/band combinations and WQI as target variable
Fig. 4
Fig. 4
An example of gene ET
Fig. 5
Fig. 5
Flowchart illustrating the process of GEP model building
Fig. 6
Fig. 6
Expression trees for the optimal GEP model with 4 spectral bands
Fig. 7
Fig. 7
Scatter plots between observed and estimated WQIs from a MLR approach for band combination 5; 4 inputs, b BPNN approach for band combination 4; 3 inputs and c GEP approach for band combination 5; 4 inputs
Fig. 8
Fig. 8
Comparative line plot of observed WQI and estimated WQI from the three employed approaches
Fig. 9
Fig. 9
WQI maps of the river stretch generated from a MLR, b BPNN and c GEP analysis
Fig. 10
Fig. 10
Boxplot of WQI estimates from the three employed approaches
See this image and copyright information in PMC

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