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Water pollution escalates with rising waste discharge in river systems, as the rivers' limited pollution tolerance and constrained self-cleaning capacity compel the release of treated pollutants. Although several studies have shown that the non-dominated sorting genetic algorithm-II (NSGA-II) is an effective algorithm regarding the management of river water quality to reach water quality standards, to our knowledge, the literature lacks using a new optimization model, namely, the multi-objective cuckoo optimization algorithm (MOCOA). Therefore, this research introduces a new optimization framework, including non-dominated sorting and ranking selection using the comparison operator densely populated towards the best Pareto front and a trade-off estimation between the goals of discharges and environmental protection authorities. The suggested algorithm is implemented for a waste load allocation issue in Jajrood River, located in the North of Iran. The limitation of this research is that discharges are point sources. To analyze the performance of the new optimization algorithm, the simulation model is linked with a hybrid optimization model using a cuckoo optimization algorithm and non-dominated sorting genetic algorithms to convert a single-objective algorithm to a multi-objective algorithm. The findings indicate that, in terms of violation index and inequity values, MOCOA's Pareto front is superior to NSGA-II, which highlights the MOCOA's effectiveness in waste load allocation. For instance, with identical population sizes and violation indexes for both algorithms, the optimal Pareto front ranges from 1.31 to 2.36 for NSGA-II and 0.379 to 2.28 for MOCOA. This suggests that MOCOA achieves a superior Pareto front in a more efficient timeframe. Additionally, MOCOA can attain optimal equity in the smaller population size. [ABSTRACT FROM AUTHOR]

Urban drainage systems (UDSs) may experience failure encountering uncertain future conditions. These uncertainties arise from internal and external threats such as sedimentation, blockage, and climate change. In this paper, a new resilience-based framework is proposed to assess the robustness of urban flood management strategies under some distinct future scenarios. The robustness values of flood management strategies are evaluated by considering reliability, resiliency, and socio-ecological resilience criteria. The socio-ecologic resilience criteria are proposed considering the seven principles of building resilience proposed by Biggs et al. (2012). The evidential reasoning (ER) approach and the regret theory are utilized to calculate the total robustness of the flood management strategies. In this framework, the non-dominated sorting genetic algorithms III (NSGA-III) optimization model and the storm water management model (SWMM) simulation model are linked and run to quantify the criteria. The novelty of this paper lies in presenting a new framework to increase the sustainability and resilience of cities against floods considering the deep uncertainties in the main economic, social, and hydrological factors. This methodology provides policies for redesigning and sustainable operation of urban infrastructures to deal with floods. To evaluate the applicability and efficiency of the framework, it is applied to the East drainage catchment of the Tehran metropolitan area in Iran. The results show that real-time operation of existing flood detention reservoirs, along with implementing five new relief tunnels with a construction cost of 37.1 million dollars, is the most robust non-dominated strategy for flood management in the study area. Comparing the results of the proposed framework with those of a traditional framework shows that it can increase the robustness value by about 40% with the same implementation cost. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology for the optimal redesign of water quality monitoring networks in coastal aquifers. The GALDIT index is used to evaluate the extent and magnitude of seawater intrusion (SWI) in coastal aquifers. The weights of the GALDIT parameters are optimized using the genetic algorithm (GA). A SEAWAT-based simulation model, a spatiotemporal Kriging interpolation technique, and an artificial neural network surrogate model are then implemented to simulate total dissolved solids (TDS) concentration in coastal aquifers. To obtain more precise estimations, an ensemble meta-model is developed using the Dempster-Shafer's belief function theory (D-ST) to combine the results obtained from the three individual simulation models. The combined meta-model is then used for calculating more precise TDS concentration. Some plausible scenarios are defined for variation of water elevation and water salinity at the coastline to incorporate uncertainty through the concept of value of information (VOI). Finally, the potential wells with the highest values of information are taken into consideration to redesign coastal groundwater quality monitoring network under uncertainty. The performance of the proposed methodology is evaluated by applying it to the Qom-Kahak aquifer, north-central Iran, which is threatened by SWI. At first, the individual and ensemble simulation models are developed and validated. Then, several scenarios are defined regarding the plausible changes in TDS concentration and water level at the coastline. In the next step, the scenarios, the GALDIT-GA vulnerability map, and the VOI concept are used for redesigning the existing monitoring network. The results illustrate that the revised groundwater quality monitoring network containing 10 new sampling locations outperforms the existing one based on the VOI criterion. [ABSTRACT FROM AUTHOR]

Climate change has increased the severity and frequency of droughts over the last decades. To alleviate the adverse impacts of droughts, an effective planning and management framework requires high-resolution spatiotemporal data. TRMM multi-satellite precipitation analysis (TMPA) dataset provides sufficient accuracy with fine spatio-temporal resolution. However, it only covers a short temporal span, which limits its applicability for drought studies. This paper presents a methodology for efficient and accurate temporal extension of TMPA using four artificial intelligence (AI)-based models. To improve AI-based model precipitation estimations, fusion techniques including Orness, Orlike, and genetic algorithm (GA)-based weighting methods were employed. Results show that fusion approaches provide more accurate estimates of precipitation. Different timescales of n-SPI time series and drought spatial maps were prepared to visually evaluate the performance of long-term TMPA (LT-TMPA) alongside statistical error indices. The results confirm that this dataset is effective for meteorological drought monitoring over southern Iran. Finally, drought risk assessment was carried out to determine the spatiotemporal characteristics of droughts through severity-duration-frequency (SDF) contour maps. In contrast to the traditional SDF curves, SDF contour maps provide a superior understanding of drought for policymakers since they preserve spatial information. [ABSTRACT FROM AUTHOR]

Today, according to the occurrence of numerous disasters in allover over the world, designing the proper and comprehensive plan for relief logistics has received a lot of attention from crisis managers and people. Besides, considering resilience capability along with operational and disruption risks leads to the robustness of the humanitarian relief chain (HRC), and this comprehensive framework ensures the essential supplies delivery to the beneficiaries and is close to real-world problems. The resilience parameters used for the second objective are obtained by a strong Best Worst Method (BWM). Another supposition of the model is the consideration of uncertainty in all stages of the proposed problem. Moreover, the multiple disasters (sub-sequent minor post disasters) which can increase the initial demand are considered. Furthermore, the proposed model is solved using three well-known metaheuristic algorithms includes non-dominated sorting genetic algorithm (NSGA-II), network reconfiguration genetic algorithm (NRGA), and multi-objective particle swarm optimization (MOPSO), and their performance is compared by several standard multi-objective measure metrics. Finally, the obtained results show the robustness of the proposed approaches, and some directions for future researches are provided. [ABSTRACT FROM AUTHOR]

Hybrid and integrated techniques can be used to investigate intrinsic uncertainties of the overlay and index groundwater vulnerability assessment methods. The development of a robust groundwater vulnerability assessment framework for precise identification of susceptible zones may contribute to more efficient policies and plans for sustainable managements. To achieve an overall view of the groundwater pollution potential, the DRASTIC framework (Depth to the water table, net Recharge, Aquifer media, Soil media, Topography, Impact of the vadose zone, and hydraulic Conductivity) can be used for intrinsic vulnerability assessment. However, the unreliability of this index is because of its inherent drawbacks, including the weight and rating assignment subjectivity. To modify the rating range, this study recommended a new DRASTIC modification using a recently introduced Multi-Criteria Decision-Making (MCDM) method, namely the Stepwise Weight Assessment Ratio Analysis (SWARA); in addition, the Entropy and Genetic Algorithm (GA) methods were employed to alter the relative weights of DRASTIC parameters. To improve the DRASTIC index, nitrate concentration data from 50 observation wells in the study site were used. To assess the models' overall performance, the datasets obtained from new observation wells, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) were studied. The experiments were carried out in the aquifer of the Qazvin Plain in Iran. The results indicated the higher performance of the modified DRASTIC framework, manifested as an increase in the AUC value from 0.58 for generic DRASTIC to 0.68 for the SWARA-Ent framework and 0.74 for the SWARA-GA framework. The application of the SWARA technique, as an effective MCDM method, resulted in the DRASTIC rating system enhancement. The generic DRASTIC optimization by integrating SWARA and GA provided an effective framework to assess groundwater vulnerability to nitrate contamination in the Qazvin Plain. [ABSTRACT FROM AUTHOR]

In this research, to predict landslide susceptibility mapping (LSM), we have studied and optimized an artificial neural network (ANN) by utilizing the backtracking search algorithm (BSA) as well as the Cuckoo optimization algorithm (COA). Multiple research studies have shown that ANN-based techniques can be used to figure out the LSM. Still, ANN computing models have big problems, like slow system learning and getting stuck in their local minimums. Optimization strategies may improve ANN performance results. Existing uses of the BSA and COA models in ANN training have not been used to map landslides, nor have the best ways to set up networks or other factors that affect this problem been examined. Consequently, the present research focuses on predicting landslide susceptibility for hazardous mapping using hybrid BSA and COA-based ANN algorithms (BSA-MLP and COA). A large data set was provided from an area in the province of Kurdistan, west of Iran, to provide training and testing datasets for the algorithms. All of the BSA and COA algorithms' parameters and weights, for instance, were fine-tuned to make the utmost accurate maps of landslide risk. The input dataset consists of elevation, slope angle, slope orientation, NDVI, fault tolerance, profile curvature, plan curvature, distance to the river, rainfall, far from the road, SPI, STI, TRI, TWI, land use, and geology; the output is landslide susceptibility value. In the testing phase, the AUC rose significantly from 0.701 to 0.864 for BSA-MLP and 0.738 to 0.822 for COA-MLP after using the abovementioned techniques. We have used the area under the curve (AUC) to evaluate how well the probabilistic models worked. In addition, the computed AUCs for the BSA-MLP available databases and the actual AUCs were 0.864, 0.857, 0.833, 0.778, 0.777, 0.769, 0.763, 0.758, 0.727, and 0.701 and 0.822, 0.808, 0.807, 0.805, 0.804, 0.777, and 0.769 for the COA-MLP combination. The integrated models can produce beneficial results for this area of research. The results suggest that the BSA-ANN model is better than the COA-ANN in optimizing an artificial neural network model's structure and computational parameters. The collected landslide susceptibility maps are significant for figuring out how dangerous landslides are in the studied area. [ABSTRACT FROM AUTHOR]

One of the most effective ways to minimize polluted water consumption is to arrange quality sensors properly in the water distribution networks (WDNs). In this study, the NSGA-III algorithm is developed to improve the optimal locations of sensors by balancing four conflicting objectives: (1) detection likelihood, (2) expected detection time, (3) detection redundancy, and (4) the affected nodes before detection. The research procedure proposed the dynamic variations of chlorine between defined upper and lower bounds, which were determined utilizing the Monte Carlo simulation model. For selecting a contamination matrix with the same characteristics and effects of all possible events, a heuristic method was applied. The coefficients of importance are introduced in this study for the assessment of contamination events and network nodes. The Pareto fronts for each of the two sets of conflicting objectives were computed for benchmark and real water distribution networks using the proposed simulation–optimization approach. Results indicated that sensors should be installed downstream of the network to maximize sensor detection likelihood; however, this increases detection time. For the benchmark network, maximum and minimum detection likelihoods were calculated as 92.8% and 61.1%, respectively, which corresponded to the worst detection time of 11.58 min and the best detection time of 5.06 min. So, the position of sensors regarding the two objective functions conflicts with each other. Also, the sensitivity analysis related to the number of sensors illustrated that the Pareto fronts became a more efficient tool when the number of sensors increased. The best pollution detection likelihood in the real water network increased by 18.93% and 24.66% by incrementing the number of sensors from 5 to 10 and 5 to 15, respectively. Moreover, adding more than 10 sensors to the benchmark network and more than 15 to the real system will provide little additional detection likelihood. [ABSTRACT FROM AUTHOR]

Groundwater quality is typically measured through water sampling and lab analysis. The field-based measurements are costly and time-consuming when applied over a large domain. In this study, we developed a machine learning-based framework to map groundwater quality in an unconfined aquifer in the north of Iran. Groundwater samples were provided from 248 monitoring wells across the region. The groundwater quality index (GWQI) in each well was measured and classified into four classes: very poor, poor, good, and excellent, according to their cut-off values. Factors affecting groundwater quality, including distance to industrial centers, distance to residential areas, population density, aquifer transmissivity, precipitation, evaporation, geology, and elevation, were identified and prepared in the GIS environment. Six machine learning classifiers, including extreme gradient boosting (XGB), random forest (RF), support vector machine (SVM), artificial neural networks (ANN), k-nearest neighbor (KNN), and Gaussian classifier model (GCM), were used to establish relationships between GWQI and its controlling factors. The algorithms were evaluated using the receiver operating characteristic curve (ROC) and statistical efficiencies (overall accuracy, precision, recall, and F-1 score). Accuracy assessment showed that ML algorithms provided high accuracy in predicting groundwater quality. However, RF was selected as the optimum model given its higher accuracy (overall accuracy, precision, and recall = 0.92; ROC = 0.95). The trained RF model was used to map GWQI classes across the entire region. Results showed that the poor GWQI class is dominant in the study area (covering 66% of the study area), followed by good (19% of the area), very poor (14% of the area), and excellent (< 1% of the area) classes. An area of very poor GWQI was observed in the north. Feature analysis indicated that the distance to industrial locations is the main factor affecting groundwater quality in the region. The study provides a cost-effective methodology in groundwater quality modeling that can be duplicated in other regions with similar hydrological and geological settings. [ABSTRACT FROM AUTHOR]

The problem of traffic congestion and the environmental issues related to air pollution are among the essential problems of urban management that metropolitan cities are trying to mitigate. Given that the contribution of motor vehicles to air pollution is significant, both goals are achieved by managing urban transport. Among the various methods of travel demand management, congestion pricing is a very efficient measure. This study tried to simultaneously increase the efficiency of the transportation network and reduce the environmental effects by using a bi-level model for the multi-modal network. For this purpose, the upper-level model minimizes the objective function, i.e., pollution emission costs and overall commuting costs. The lower level also has a transportation network model that provides the condition of user equilibrium. The genetic and Frank-Wolfe algorithms have been used to solve the bi-level programming model. Two pricing schemes, cordon-based and link-based, are used to investigate and assist policymakers. The proposed algorithm is also applied to a real-world road network in Isfahan, Iran. The results of the proposed models for different pricing strategies were compared. According to the results, both pricing schemes mitigate traffic congestion and pollution, although the reduction in pollution outside the cordon is less than inside. Demand has also shifted from the private car mode to public transportation by an average of 15%. However, link-based pricing provides better performance than cordon-based pricing. This study indicated that a higher total collected toll in link-based pricing is accompanied by a sharper reduction in congestion and pollution mitigation, which can be spent on alternative facilities and infrastructure by the municipality, such as the development of public transportation and parking. [ABSTRACT FROM AUTHOR]

Due to the lack of surface water and groundwater resources, especially in the agricultural parts, the simultaneous and sustainable use of water resources to supply water demands is essential. In this study, a conjunctive use optimization model is developed to minimize the shortage of water demand. This model is implemented for the Mahabad study area in northwestern Iran to improve the conditions of surface water and groundwater resources and the reclamation of Urmia Lake. For this purpose, the current research is accomplished in three parts. At first, the Mahabad aquifer is numerically simulated to investigate the aquifer conditions. In the second part, the optimized model of conjunctive use obtained by the Harris hawk optimization (HHO) algorithm is investigated over a 20-year period in the study area. In the last part of this research, seven scenarios are developed to predict the optimized groundwater exploitation (OGE) using the results of HHO, meteorological data, and some input information on the dam reservoir. Then, the OGE values are predicted using the artificial neural network (ANN) and ANN-HHO machine learning models for the scenarios. The results showed that the scenario that includes all input variables and the ANN-HHO model outperformed other models. Furthermore, the HHO algorithm provides suitable allocation of the surface water and groundwater resources in optimized conjunctive use and also improves the performance of ANN in predicting the OGE values. The findings of this study also show that groundwater resources can be more applied to supply water demand, and in contrast, surface water resources can be used for supplying downstream environmental demands and reclamation of Urmia Lake. [ABSTRACT FROM AUTHOR]

Due to limited groundwater resources in arid and semi-arid areas, conjunctive use of surface water and groundwater is becoming increasingly important. In view of this, there are needs to improve the methods for conjunctive use of surface and groundwater. Using numerical models, optimization algorithms, and machine learning, we created a new comprehensive methodological structure for optimal allocation of surface and groundwater resources and optimal extraction of groundwater. The surface and groundwater system was simulated by MODFLOW to reflect groundwater transport and aquifer conditions. The important Marvdasht aquifer in the south of Iran was used as an experimental study area to test the methodology. In this context, we developed an optimal conjunctive exploitation model for dry and wet years using two new evolutionary algorithms, i.e., whale optimization algorithm (WOA) and firefly algorithm (FA). These were used in combination with the group method of data handling (GMDH) and least squares support vector machine (LS-SVM) to estimate sustainable groundwater withdrawal. The results show that the FA is more efficient in calculating optimal conjunctive water supply so that about 61% of water needs were met in the worst scenario for surface water resources, while it was 52% using the WOA. By applying the optimal conjunctive model during the simulation period, the groundwater level increased by about 0.4 and 0.55 m using the WOA and FA, respectively. The results of Taylor's diagram, box plot diagram, and rock diagram with error evaluation criteria, i.e., root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE), showed that the GMDH (RMSE = 6.04 MCM, MAE = 3.89 MCM, and NSE = 0.99) was slightly better than LS-SVM (RMSE = 6.36 MCM, MAE = 4.50 MCM, and NSE = 0.98) to estimate optimal groundwater use. The results show that machine learning models are cost- and time-effective solutions to estimate optimal exploitation of groundwater resources in complex combined surface and groundwater supply problems. The methodology can be used to better estimate sustainable exploitation of groundwater resources by water resources managers. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recognizing the vulnerable areas for contamination is a feasible way to protect groundwater resources. The main contribution of the paper is developing a hybrid statistical decision-making model for evaluating the vulnerability of Shiraz aquifer, southern Iran, with modified DRASTIC (depth to the water table, net recharge, aquifer media, soil media, topography, impact of the vadose zone, and hydraulic conductivity) by using the genetic algorithm (GA), the analytical hierarchy process (AHP) method, and factorial analysis (FA). First, considering the variation of the uncertain parameters, 32 scenarios were defined to perform factorial analysis. Then using the AHP method and GA, DRASTIC parameters were rated and weighted in all scenarios. To achieve the optimal weights for parameters, the objective function in GA was maximizing the correlation coefficient between the vulnerability index and the nitrate concentration. The single and interactive effects of parameters on groundwater vulnerability were analyzed by factorial analysis. The results revealed that the net recharge had the highest single effect, and the resulted effect between net recharge and hydraulic conductivity was the most significant interactive effect on the objective function. Besides, the variation of aquifer media does not change the objective function. The application of the proposed method leads to a precise groundwater vulnerability map. This research provides valuable knowledge for assessing groundwater vulnerability and enables decision-makers to apply groundwater vulnerability information in future water resources management plans. [ABSTRACT FROM AUTHOR]

The purpose of this study is to determine energy use patterns, evaluating the environmental impacts and economic evaluation of smoked fish production in the Guilan Province. The initial data were collected from seven smokehouses in the north of Iran through face-to-face questionnaire method, while the required data related to the background system were extracted from the Ecoinvent 2.2 database. The total input and output energy result showed the use is examined to be 98,143.29 and 60,048 MJ ton−1, respectively. About 79.31% of this was generated by fish, 7.7% from electricity, and 6.7% from wood chip. Life cycle assessment results showed that fish and salt emissions have the most notable effects. Among damage categories, the largest emissions were related to human health (37.77%). Climate change (30.35%), resource (28.78%), and ecosystem quality (0.92%) are next phases. The rates of nonrenewable, fossil in CExD method calculated 35,426.61 MJ ton−1. Economic analysis of production was carried out. Some economic indicators have been calculated, and the benefit to cost ratio was obtained as 3.09. Due to the justification of fisheries management, these issues merit further exploration. [ABSTRACT FROM AUTHOR]

The suspended sediment load (SSL) prediction is one of the most important issues in water engineering. In this article, the adaptive neuro-fuzzy interface system (ANFIS) and support vector machine (SVM) were used to estimate the SLL of two main tributaries of the Telar River placed in the north of Iran. The main Telar River had two main tributaries, namely, the Telar and the Kasilian. A new evolutionary algorithm, namely, the black widow optimization algorithm (BWOA), was used to enhance the precision of the ANFIS and SVM models for predicting daily SSL. The lagged rainfall, temperature, discharge, and SSL were used as the inputs to the models. The present study used a new hybrid Gamma test to determine the best input scenario. In the next step, the best input combination was determined based on the gamma value. In this research, the abilities of the ANFIS-BWOA and SVM-BWOA were benchmarked with the ANFIS-bat algorithm (BA), SVM-BA, SVM-particle swarm optimization (PSO), and ANFIS-PSO. The mean absolute error (MAE) of ANFIS-BWOA was 0.40%, 2.2%, and 2.5% lower than those of ANFIS-BA, ANFIS-PSO, and ANFIS models in the training level for Telar River. It was concluded that the ANFIS-BWOA had the highest value of R2 among other models in the Telar River. The MAE of the ANFIS-BWOA, SVM-BWOA, SVM-PSO, SVM-BA, and SVM models were 899.12 (Ton/day), 934.23 (Ton/day), 987.12 (Ton/day), 976.12, and 989.12 (Ton/day), respectively, in the testing level for the Kasilian River. An uncertainty analysis was used to investigate the effect of uncertainty of the inputs (first scenario) and the model parameters (the second scenario) on the accuracy of models. It was observed that the input uncertainty higher than the parameter uncertainty. [ABSTRACT FROM AUTHOR]

One of the appropriate ways to prevent groundwater contamination is identifying the vulnerable areas of the aquifers. The DRASTIC framework, for assessing the intrinsic vulnerability of the aquifer, is a common method which uses a specific parameter's weight and a uniform distributed contaminant in overall the aquifer. Therefore, it should be calibrated for specific aquifer and contaminant distribution conditions. In this research, random forest (RF) and genetic algorithm (GA) methods were used for DRASTIC framework optimization in Miandoab plain (NW of Iran). In optimizing the basic DRASTIC framework (BDF) using GA, decision variables are the weight of DRASTIC parameters and weight values for each data layer are the outputs of the optimization. The final optimized map (BDF-GA map) was obtained using overlaying the layers with optimized weights based on the GA method. In optimization of BDF using RF, the model is made up of from 1 to 100 trees and the m parameter or split variables was optimized by changing the number of variables between one and the maximum variables of each subset. Also, the feature selection method is used to reduce the dimensions and increase the accuracy of the model. To induct the nitrate contaminant model, raster layer data of 7 BDF parameters, together with the target variable (VI of BDF map), were used. In the final step, variables' importance was identified by the RF method and then, the vulnerability map was obtained based on variable importance. In validation and comparison of methods with CI and ROC methods, the BDF-RF method with the higher CI and ROC values was ranked as the most accurate approach in groundwater vulnerability evaluation. The optimized map using the RF method (BDF-RF map) showed that 14.5, 13, 18, 26.5, and 28% of the plain are located in areas with very low, low, moderate, high, and very high vulnerability categories, respectively. [ABSTRACT FROM AUTHOR]

This study reports on BTEX concentrations in one of the largest parking garages in Iran with a peak traffic flow reaching up to ~9300 vehicles in the last few days of the Nowruz holidays. Samples were obtained on different days of the week at three main locations in the Zaer Parking Garage. A novel wavelet-based random forest model (WRF) was trained to estimate BTEX concentrations by decomposing temperature, day of the week, sampling location, and relative humidity data with a maximal overlap discrete wavelet transform (MODWT) function and subsequently inputted into the WRF model. The results suggested that the WRF model can reasonably estimate BTEX trends and variations based on high R2 values of 0.96, 0.95, and 0.98 for training, validation, and test data subsets, respectively. The carcinogenic (LTCR) and non-carcinogenic health risk (HI) assessment results indicated a definite carcinogenic risk of benzene (LTCR = 2.22 × 10−4) and high non-carcinogenic risk (HI = 4.51) of BTEX emissions. The results of this study point to the importance of BTEX accumulation in poorly ventilated areas and the utility of machine learning in forecasting air pollution in diverse airsheds such as parking garages. [ABSTRACT FROM AUTHOR]

This research developed a more efficient integrated model (IM) based on combining the Nash-Sutcliffe efficiency coefficient (NSEC) and individual data mining (DM) algorithms for the spatial mapping of dust provenance in the Hamoun-e-Hirmand Basin, southeastern Iran. This region experiences severe wind erosion and includes the Sistan plain which is one of the most PM2.5-polluted regions in the world. Due to a prolonged drought over the last two decades, the frequency of dust storms in the study area is increasing remarkably. Herein, 14 factors controlling dust emissions (FCDEs) including soil characteristics, climatic variables, digital elevation map, normalized difference vegetation index, land use and geology were mapped. Correlation and collinearity among the FCDEs were examined by the Pearson test, tolerance coefficient (TC) and variance inflation factor (VIF), with the results suggesting a lack of collinearity between FCDEs. A tree-based genetic algorithm was applied to prioritize and quantify the importance weights of the FCDEs. Thirteen individual data mining models were applied for mapping dust provenance. The model performance was assessed using root mean square error, mean absolute error and NSEC. Based on clustering analysis, the 13 DM models were grouped into five clusters and then the cluster with the highest NSEC values used in an integrated modelling process. Based on the results, the IM (NSEC = 93%) outperformed the individual DM models (the NSEC values range between 51 and 92%). Using the IM, 11, 5, 7 and 77% of the total study area were classified into low, moderate, high and very high susceptibility classes for dust provenance, respectively. Overall, the results illustrate the benefits of an IM for mapping spatial variation in the susceptibility of catchment areas to act as dust sources. [ABSTRACT FROM AUTHOR]