Development of new computational machine learning models for longitudinal dispersion coefficient determination: case study of natural streams, United States

Numerical simulation of vegetation evolution in compound channels

The study of vegetation evolution is essential for further understanding of biogeographic feedback and ecological restoration. In this paper, a vegetation evolution model based on velocity threshold (scaled by the average flow velocity of the bare bed) was established to simulate the vegetation evolution process in compound channels. In this model, the effect of vegetation on water flow was generalized as equivalent Manning coefficient, and the velocity field was obtained by solving two-dimensional shallow water equations. The model defined that new vegetation was added in areas where the velocity was less than the velocity threshold, and conversely, vegetation was destroyed in areas where the flow velocity exceeded the velocity threshold. The model was used to explore the effect of velocity threshold, initial vegetation coverage, and relative water depth (the ratio of the flow depth in the floodplain to that over main channel) on final vegetation coverage and longitudinal dispersion coefficient (K_e) in compound channels, and compare the difference of vegetation evolution between rectangular channels and compound channels. Results showed that the velocity threshold played a decisive role in vegetation evolution, and the effect of relative water depth and cross section type on vegetation evolution was only reflected when the velocity threshold was small. The longitudinal dispersion coefficient gradually increased with the expansion of vegetation, and tended to a constant value (K_f) when a stable vegetation landscape was reached. As the relative water depth decreased, the longitudinal dispersion coefficient presented an increasing trend. Regular distribution of initial vegetation patches can produce larger longitudinal dispersion coefficient compared to the cases of random distribution in compound and rectangular channels, and the increasing effect was more significant in compound channels.

Geochemical and Spatial Distribution of Topsoil HMs Coupled with Modeling of Cr Using Chemometrics Intelligent Techniques: Case Study from Dammam Area, Saudi Arabia

Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of topsoil materials’ contamination with heavy metals (HMs) was conducted. The material’s representative spatial samples were taken from various sources: agricultural, industrial, and residential areas. The materials include topsoil, eolian deposits, and other unconsolidated earthen materials. The samples were analyzed using the ICP-OES. The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 ± 0.69 mg/kg), Ba (110.62 ± 262 mg/kg), Hg (0.08 ± 0.18 mg/kg), Pb (6.34 ± 14.55 mg/kg), Ni (8.95 ± 5.66 mg/kg), V (9.98 ± 6.08 mg/kg), Cd (1.18 ± 4.33 mg/kg), Cr (31.79 ± 37.9 mg/kg), Cu (6.76 ± 12.54 mg/kg), and Zn (23.44 ± 84.43 mg/kg). Subsequently, chemometrics modeling and a prediction of Cr concentration (mg/kg) were performed using three different modeling techniques, including two artificial intelligence (AI) techniques, namely, generalized neural network (GRNN) and Elman neural network (Elm NN) models, as well as a classical multivariate statistical technique (MST). The results indicated that the AI-based models have a superior ability in estimating the Cr concentration (mg/kg) than MST, whereby GRNN can enhance the performance of MST up to 94.6% in the validation step. The concentration levels of most metals were found to be within the acceptable range. The findings indicate that AI-based models are cost-effective and efficient tools for trace metal estimations from soil.

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

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.