Said Saif, Khan Shadab Ali
Civil Engineering Department, Aligarh Muslim University, (AMU), Aligarh, India.
Environ Dev Sustain. 2021;23(12):18252-18277. doi: 10.1007/s10668-021-01437-6. Epub 2021 Apr 21.
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 , RMSE and MAE. Results revealed that WQI estimates ranged between 203.7 and 262.33 and rated as "" status. Results further indicated that GEP performed better than BPNN and MLR approaches and predicted WQI estimates with high 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 ~ 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 -statistic ( = 0.01) and -value ( = 0.994 > 0.05) revealed WQI estimates as "," reasoned to the small water sample size (i.e., = 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.
本研究利用高分辨率的印度遥感卫星6号（IRS P6）线性成像自扫描传感器-4（LISS IV）影像，评估了流经印度北方邦阿里格尔区的卡里河6公里河段的水质状况。在40个随机地点采集了原位河水样本，分析了7种物理化学指标和4种重金属浓度，并计算了每个采样点的水质指数（WQI）。制定了一组11种光谱反射率波段组合，以确定与每个采样点观测到的WQI相关的最显著波段组合。采用多元线性回归（MLR）、反向传播神经网络（BPNN）和基因表达式编程（GEP）三种方法，将WQI作为最显著波段组合的函数进行关联。通过诸如 、均方根误差（RMSE）和平均绝对误差（MAE）等定量指标，对所采用的三种方法进行了比较评估。结果显示，WQI估计值在203.7至262.33之间，水质评级为“ ”状态。结果还表明，GEP的性能优于BPNN和MLR方法，其预测的WQI估计值具有较高的 值（即校准数据为0.94，验证数据为0.91）、较低的RMSE和MAE值（即校准数据为2.49和2.16，验证数据为4.45和3.53）。此外，GEP和BPNN在性能上均优于MLR方法，MLR方法在校准和验证数据中得出的WQI的 值分别约为0.81和0.67。三种方法生成的WQI地图证实了该河段沿线现有的污染水平。为了检验三种方法得出的WQI估计值之间的显著差异，进行了单因素方差分析，结果在F统计量（F = 0.01）和p值（p = 0.994 > 0.05）方面显示，WQI估计值为“ ”，原因是水样数量较少（即n = 40）。因此，该研究建议GEP更为合理，是通过生成简化数学表达式对地表水体进行精确水质监测的更好选择。
