Spatiotemporal Variability Assessment of Trace Metals Based on Subsurface Water Quality Impact Integrated with Artificial Intelligence-Based Modeling

Abstract

Increasing anthropogenic emissions due to rapid industrialization have triggered environmental pollution and pose a threat to the well-being of the ecosystem. In this study, the first scenario involved the spatio-temporal assessment of topsoil contamination with trace metals in the Dammam region, and samples were taken from 2 zones: the industrial (ID), and the agricultural (AG) area. For this purpose, more than 130 spatially distributed samples of topsoil were collected from residential, industrial, and agricultural areas. Inductively coupled plasma—optical emission spectroscopy (ICP-OES)—was used to analyze the samples for various trace metals. The second scenario involved the creation of different artificial intelligence (AI) models, namely an artificial neural network (ANN) and a support vector regression (SVR), for the estimation of zinc (Zn), copper (Cu), chromium (Cr), and lead (Pb) using feature-based input selection. The experimental outcomes depicted that the average concentration levels of HMs were as follows: Chromium (Cr) (31.79 ± 37.9 mg/kg), Copper (Cu) (6.76 ± 12.54 mg/kg), Lead (Pb) (6.34 ± 14.55 mg/kg), and Zinc (Zn) (23.44 ± 84.43 mg/kg). The modelling accuracy, based on different evaluation criteria, showed that agricultural and industrial stations showed performance merit with goodness-of-fit ranges of 51–91% and 80–99%, respectively. This study concludes that AI models could be successfully applied for the rapid estimation of soil trace metals and related decision-making.

Ensemble hybrid machine learning to simulate dye/divalent salt fractionation using a loose nanofiltration membrane

“…This is strange but not surprising owing to the powerful nature of NF models recorded in several science and engineering technical literature studies. 34,[61][62][63][64][65] The error performance in terms of MAE and RMSE is presented in Fig. 8.…”

“…Cross-validation is used to avoid overfitting, which happens when a model is too closely fitted to the training data and performs poorly on raw, untainted data. 34–36 Although there are various cross-validation techniques, the most well-known one is k -fold cross-validation, which involves splitting the data into k subsets, and training and evaluating the model k times, each time using a different subset as the test set and the remaining k − 1 subsets as the training set. 37 This allows the model to be evaluated on a variety of different data, giving a more accurate estimate of its performance on unseen data.…”

Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management

“…It uses an ultrasonic sensor for detection and monitoring the wastewater level in the septic tank and a GSM module to send SMS alerts to the users, avoiding spills of contaminants that can cause health problems. Seker (2022) developed a real-time smart, cost-effective waste collection system that employs IoT with technologies like radio frequency identification (RFID), GIS, and ground penetrating radar systems (GPRS). It is effective for municipal waste collection and transportation and has the goal of reducing environmental pollution.…”

“…They confirmed its efficiency, low investment, reduced energy consumption, and real-time data acquisition for surface water quality monitoring. Similarly, Ryu (2022) proposed an unmanned aircraft system (UAS) for water sampling and monitoring, which can be used in potentially hazardous environments, minimizing the involvement of humans. Its UASWQP platform can take water samples from various points and is effective in presenting realtime data regarding measurements of water parameters.…”