Progressive improvement of DRASTICA and SI models for groundwater vulnerability assessment based on evolutionary algorithms.

Groundwater management is essential in water and environmental engineering from both quantity and quality aspects due to the growing urban population. Groundwater vulnerability evaluation models play a prominent role in groundwater resource management, such as the DRASTIC model that has been used successfully in numerous areas. Several studies have focused on improving this model by changing the initial parameters or the rates and weights. The presented study investigated results produced by the DRASTIC model by simultaneously exerting both modifications. For this purpose, two land use-based DRASTIC-derived models, DRASTICA and susceptibility index (SI), were implemented in the Shiraz plain, Iran, a semi-arid region and the primary resource of groundwater currently struggling with groundwater pollution. To develop the novel proposed framework for the progressive improvement of the mentioned rating-based techniques, three main calculation steps for rates and weights are presented: (1) original rates and weights; (2) modified rates by Wilcoxon tests and original weights; and (3) adjusted rates and optimized weights using the genetic algorithm (GA) and particle swarm optimization (PSO) algorithms. To validate the results of this framework applied to the case study, the concentrations of three contamination pollutants, NO3, SO4, and toxic metals, were considered. The results indicated that the DRASTICA model yielded more accurate contamination concentrations for vulnerability evaluations than the SI model. Moreover, both models initially displayed well-matched results for the SO4 concentrations, specifically 0.7 for DRASTICA and 0.58 for SI, respectively. Comparatively, the DRASTICA model showed a higher correlation with NO3 concentrations (0.8) than the SI model (0.6) through improved steps. Furthermore, although both original models demonstrated less correlation with toxic metal concentrations (0.05) compared to SO4 and NO3 concentrations, the DRASTICA and SI models with modified rates and optimized weights exhibited enhanced correlation with toxic metals of about 0.7 and 0.2, respectively.

Design of a high-coverage ground-based CO2 monitoring layout using a novel information theory-based optimization model.

Over the past decade, monitoring of the carbon cycle has become a major concern accented by the severe impacts of global warming. Here, we develop an information theory-based optimization model using the NSGA-II algorithm that determines an optimum ground-based CO2 monitoring layout with the highest spatial coverage using a finite number of stations. The value of information (VOI) concept is used to assess the efficacy of the monitoring stations given their construction cost. In conjunction with VOI, the entropy theory-in terms of transinformation-is adopted to determine the redundant (overlapping) information rendered by the selected monitoring stations. The developed model is used to determine a ground-based CO2 monitoring layout for Iran, the eighth-ranked country emitting CO2 worldwide. An NSGA-II optimization model provides a tradeoff curve given the objectives of (1) minimizing the size of monitoring network; (2) maximizing VOI, i.e., spatial coverage; and (3) minimizing transinformation, i.e., overlapping information. Borda count method is then employed to select the most appropriate compromise monitoring layout from the Pareto-front solutions given regional priorities and concerns.