Aquifer recharge rates and patterns are often uncertain, especially in arid areas due to sporadic and erratic rainfall. Therefore, determining the optimal groundwater abstraction using classical approaches such as Monte Carlo Simulation (MCS) requires a large number of groundwater simulations and exorbitant computational efforts. The problem becomes even more complex and time consuming for regional coastal aquifers whose domains must be discretized using high-resolution meshes. In fact, even fast evolutionary multi-objective optimization techniques generally require a large number of simulations to determine the Pareto-front among the objectives. This study explores the performance of a Decision Tree (DT) approach for the generation of the Pareto optimal solutions of groundwater extraction. This paper applies the DTs for the optimal management of the Al-Khoud coastal aquifer in Oman. The learning process of the developed DT-based model uses the output of a numerical simulation model to assess the aquifer response based on different abstraction policies. The trained DT network then utilizes the NSGA-II to determine the Pareto-optimal solutions. The simulation show that the general flux pattern in the study area is toward the sea and the hydraulic head following a similar pattern in both best and worst recharging scenarios downstream of the studied recharging dam. Statistical tests showed a good correlation between the DT-based and simulation-based results and demonstrate the capability of the DT approach to obtain high-quality solutions by incorporating a large number of recharge scenarios. Moreover, the required runtime of the DT-based approach is extremely low (5 min) compared to that of the simulation-based method (several days). This means that including additional Monte-Carlo simulations can be readily done in few minutes using the obtained DTs, instead of the long computational time needed by the simulation-based approach.

Determining the ultimate bearing capacity (UBC) is vital for design of shallow foundations. Recently, soft computing methods (i.e. artificial neural networks and support vector machines) have been used for this purpose. In this paper, Random Forest (RF) is utilized as a tree-based ensemble classifier for predicting the UBC of shallow foundations on cohesionless soils. The inputs of model are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ) and internal friction angle (ϕ). A set of 112 load tests data were used to calibrate and test the developed RF-based model. The used data set consists of 47 full-scale observations and 65 small-scale laboratory footing load tests. To demonstrate the efficiency of proposed RF-based model, the results are compared with some popular classical formulas that are most commonly used for determining the UBC. The results show the efficiency and capabilities of the proposed RF-based model as a practical tool in evaluating the UBC of shallow foundations in a fast and accurate way.

Toward green educational building development, windows are important design elements as the source of natural lighting and heating in classrooms. The amount of natural lighting and net heating received by a classroom in a year depends on the school location, weather conditions, as well as the window orientation and size. Schools in Iran consume a considerable amount of energy which is mostly supplied using nonrenewable fossil fuel resources. This energy consumption can be reduced through a well-designed daylighting approach. In this paper, in order to investigate the effects of window characteristics on construction and operational costs of schools, by varying the Window-to-Wall Ratio (WWR) and window orientation, 288 daylighting scenarios are generated for a typical standard classroom in a warm-dry climatic zone in central Iran. The DOE-2 software is utilized to estimate annual gas and electric consumption, for the generated scenarios over a period of 50 years. Considering the operation and construction cost, the best window facing and optimal range of WWR in each orientation is determined for the studied standard classroom. The results of simulated daylighting scenarios are then used to train regression based Support Vector Machines (SVMs) in order to show the feasibility of applying the Support Vector Regression (SVR) as an artificial intelligent system. The obtained results show that SVR as an architectural assistant performs well and the SVR-based predictor can rapidly, easily and accurately predict the operational and construction cost of a classroom just by determining the window size and installation face.

Wave Height (WH) is one of the most important factors in design and operation of maritime projects. Different methods such as semi-empirical, numerical and soft computing-based approaches have been developed for WH forecasting. The soft computing-based methods have the ability to approximate nonlinear wind–wave and wave–wave interactions without a prior knowledge about them. In the present study, several soft computing-based models, namely Support Vector Machines (SVMs), Bayesian Networks (BNs), Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used for mapping wind data to wave height. The data set used for training and testing the simulation models comprises the WH and wind data gathered by National Data Buoy Center (NDBC) in Lake Superior, USA. Several statistical indices are used to evaluate the efficacy of the aforementioned methods. The results show that the ANN, ANFIS and SVM can provide acceptable predictions for wave heights, while the BNs results are unreliable.

Developing optimal operating policies for conjunctive use of surface and groundwater resources when different decision makers and stakeholders with conflicting objectives are involved is usually a challenging task. This problem would be more complex when objectives related to surface and groundwater quality are taken into account. In this paper, a new methodology is developed for real time conjunctive use of surface and groundwater resources. In the proposed methodology, a well-known multi-objective genetic algorithm, namely Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed to develop a Pareto front among the objectives. The Young conflict resolution theory is also used for resolving the conflict of interests among decision makers. To develop the real time conjunctive use operating rules, the Probabilistic Support Vector Machines (PSVMs), which are capable of providing probability distribution functions of decision variables, are utilized. The proposed methodology is applied to Tehran Aquifer inTehran metropolitan area,Iran. Stakeholders in the study area have some conflicting interests including supplying water with acceptable quality, reducing pumping costs, improving groundwater quality and controlling the groundwater table fluctuations. In the proposed methodology, MODFLOW and MT3D groundwater quantity and quality simulation models are linked with NSGA-II optimization model to develop Pareto fronts among the objectives. The best solutions on the Pareto fronts are then selected using the Young conflict resolution theory. The selected solution (optimal monthly operating policies) is used to train and verify a PSVM. The results show the significance of applying an integrated conflict resolution approach and the capability of support vector machines for the real time conjunctive use of surface and groundwater resources in the study area. It is also shown that the validation accuracy of the proposed operating rules is higher that 80% and based on these rules, the cumulative groundwater table variation is limited to 80 centimetres during a 15-year planning horizon.

Developing optimal operating policies for conjunctive use of surface and groundwater resources when different stakeholders with conflicting objectives are involved, is usually a challenging task. This problem would be more complex when objectives related to surface and groundwater quality are taken into account. In this study, a new methodology is proposed to resolve the conflict of interests among water users, water supply and environmental protection agencies which are involved in a problem of conjunctive use of surface and groundwater resources.In order to develop a Pareto front among objectives, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used. To incorporate the objectives related to groundwater table fluctuations and groundwater quality, the MODFLOW and MT3D groundwater quantity and quality simulation models and the optimization model are embedded. The best non-dominated solution on the Pareto front is selected using the Rubinstein Sequential Bargaining Theory (RSBT). The Fuzzy Set Theory is also utilized to better define the utility functions of decision makers. To evaluate the efficiency of the proposed methodology, it is applied to the conjunctive use of surface and groundwater resources in the Tehran metropolitan area, Iran. (C) 2009 Elsevier B.V. All rights reserved.