Vulnerability Indexing to Saltwater Intrusion from Models at Two Levels using Artificial Intelligence Multiple Model (AIMM).

Unplanned groundwater exploitation in coastal aquifers results in water decline and consequently triggers saltwater intrusion (SWI). This study formulates a novel modeling strategy based on GALDIT method using Artificial Intelligence (AI) models for mapping the vulnerability to SWI. This AI-based modeling strategy is a two-level learning process, where vulnerability to SWI at Level 1 can be predicted by such models as Artificial Neural Network (ANN), Sugeno Fuzzy Logic (SFL), and Neuro-Fuzzy (NF); and their outputs serve as the input to the model at Level 2, such as Support Vector Machine (SVM). This model is applied to Urmia aquifer, west coast of Lake Urmia, where both are currently declining. The construction of the above four models both at Levels 1 and 2 provide tools for mapping the SWI vulnerability of the study area. Model performances in the paper are studied using RMSE and R2 metrics, where the models at Level 1 are found to be fit-for-purpose and the SVM at Level 2 is improved particularly with respect to the reduced scale of scatters in the results. Evaluating the result and groundwater samples by Piper diagram confirms the correspondence of SWI status with vulnerability index.

Assessment of landfill leachate in semi-arid climate and its impact on the groundwater quality case study: Hamedan, Iran.

To evaluate environmental impacts of solid waste landfilling, groundwater quality near the MSW landfill in a semi-arid climate of Iran (Hamedan) and its leachates were analyzed. To this aim, heavy metal concentrations, COD, BOD5, TOC, EC, NO3-, Cl-, TDS, and pH of two leachate ponds (active and closed sites) as the sources of contamination as well as the shallow groundwater of the area were measured. Monthly and seasonal monitoring program of 13 sampling points in the area were designed during the period of 2014-2016. Principal components analysis has been carried out using chemical data to deduce relationship between the samples. A special statistical approach including a main factor (age of leachate) and a subfactor (distance from the source of pollutant) was designed in order to identify the landfill role on the groundwater contamination. The physicochemical analysis of the leachate characteristics confirmed a high variation in the contaminants (i.e., organic compounds, salts, and heavy metals) related to leachate age. The BOD5/COD ratio of the active (0.73) and closed (0.77) sites ponds indicated that the leachates were in a biodegradable and unstabilized condition. The seasonal physicochemical analysis of the leachates showed that rainfall events increase the decomposition rate of the waste and affect pollutant concentration of the leachate. The proposed statistical analysis illustrated a direct relationship between the groundwater quality parameters and the leachates physicochemical characteristics.

Toward a combined Bayesian frameworks to quantify parameter uncertainty in a large mountainous catchment with high spatial variability.

Although hydrological models play an essential role in managing water resources, quantifying different sources of uncertainties is a challenging task. In this study, the application of two parameter uncertainty quantification methods and their performances for predicting runoff was investigated. Sequential Uncertainty Fitting version 2 (SUFI-2) and DiffeRential Evolution Adaptive Metropolis (DREAM-ZS) algorithms were employed to explore the output uncertainty of Soil and Water Assessment Tool (SWAT) at a multisite flow gauging station. In order to optimize the model and quantify the parameter uncertainty, S1 and S2 strategies, which belong to the SUFI-2 and DREAM-ZS algorithms, were defined. The prior ranges of the S1 were adopted from SWAT manual, and the prior ranges of the S2 were selected using a compromising approach between the prior and posterior ranges extracted from S1. P-factor, d-factor, Nash-Sutcliffe coefficient (NS), the dimensionless variant of average deviation amplitude (S), and the average relative deviation amplitude (T), as performance criteria, were assessed. The NS, S, and T for total uncertainty ranged 0.60-0.71, 0.46-0.51, and 0.94-1.01 under S1 strategy and 0.64-0.78, 0.07-0.22, and 0.39-0.64 under S2, respectively. In parameter uncertainty analysis, S and T indices ranged from 1.51 to 1.88 and 2.20 to 2.60, correspondingly. The results showed that the DREAM-ZS algorithm improved model calibration efficiency and led to more realistic values of the parameters for runoff simulation in SWAT model. However, the S2 strategy, which implicitly takes advantage of both formal and informal Bayesian approaches simultaneously, will be able to outperform the S1 for reducing the prediction uncertainties.