Ramaraj M, Sivakumar Ramamoorthy
Department of Civil Engineering, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Chennai, TN, 603 203, India.
Bull Environ Contam Toxicol. 2022 Dec 27;110(1):28. doi: 10.1007/s00128-022-03646-9.
In recent days, the quality of water in inland water bodies has been threatened by various natural and anthropogenic activities. Henceforth, the continuous monitoring of water quality is mandatory to control the pollution level in surface water bodies. In this work, remote sensing technology integrated with an Artificial Intelligence (AI) algorithm, a new technique called Spatio-Temporal Hybrid Novel Technique (STHNT), was used to predict, and monitor the chemical water quality pollution level through the Water Quality Index (WQI). The Two Bands Regression Empirical (TBRE) water quality model has been used to retrieve water quality parameters from multi-resolution satellite imagery (Sentinel-2 MSI). The Nonlinear Auto-regressive Neural Network (NARNET), which is an Artificial Neural Network (ANN), was set up to predict the water quality index. Based on the model performed on the remote sensing water quality data, it is inferred that NARNET (Coefficient of determination-R:0.9911, Root Mean Square Error-RMSE:1.693 and Sum of Squares of Error-SSE:14.33) provides significant results in predicting WQI. Therefore, the combined remote sensing technology with artificial intelligence plays a pivotal role in water resource management, which helps in reducing the pollution level in surface water bodies.
近年来，内陆水体水质受到各种自然和人为活动的威胁。从今往后，持续监测水质对于控制地表水水体的污染水平至关重要。在这项工作中，将遥感技术与人工智能（AI）算法相结合，即一种名为时空混合新技术（STHNT）的新技术，用于通过水质指数（WQI）预测和监测化学水质污染水平。双波段回归经验（TBRE）水质模型已用于从多分辨率卫星图像（哨兵 - 2 MSI）中反演水质参数。作为一种人工神经网络（ANN）的非线性自回归神经网络（NARNET）被建立起来以预测水质指数。基于对遥感水质数据执行的模型推断，NARNET（决定系数 - R：0.9911，均方根误差 - RMSE：1.693，误差平方和 - SSE：14.33）在预测WQI方面提供了显著结果。因此，遥感技术与人工智能的结合在水资源管理中起着关键作用，有助于降低地表水水体的污染水平。
