A framework toward developing a groundwater conceptual model

Developing an accurate conceptual model is the most important step in the process of a groundwater numerical modeling. Disorganized and limited available data and information, especially in the developing countries, make the preparation of the conceptual model difficult and sometimes cumbersome. In this research, an integrative and comprehensive method is proposed to develop groundwater conceptual model for an unconfined aquifer. The proposed method consists of six steps. A preliminary step (step 0) is aimed at collecting all the available data and information. The output of the first step as “controlling observations” is conceptual model version 00. This step should be rigorously checked due to its critical role in the controlling of final conceptual model. Step 2 determines the aquifer geometry. The output of this step is conceptual model version 01. Step 3 is responsible to determine hydrodynamic properties and its output develops conceptual model version 02. Step 4 evaluates the surface and subsurface interactions and lateral in/out groundwater flows. The output of this step is conceptual model version 03. Step 5 is to integrate the results from other steps and to deliver the final conceptual model version. The accuracy level of the conceptual model and the annual groundwater balance is also determined at this step. The presented groundwater conceptual model procedure was implemented for the Neishaboor plain, Iran. Results showed its usefulness and practicality in developing the conceptual model for the study area.     

Application of “panel-data” modeling to predict groundwater levels in the Neishaboor Plain, Iran

The aim of this research was to predict groundwater levels in the Neishaboor plain, Iran, using a ??panel-data?? model. Panel-data analysis endows regression analysis with both spatial and temporal dimensions. The spatial dimension pertains to a set of cross-sectional units of observation. The temporal dimension pertains to periodic observations of a set of variables characterizing these cross-sectional units over a particular time span. Firstly, the available observation wells in the Neishaboor plain were clustered according to their fluctuation behavior using the ??Ward?? method, which resulted in six areal zones. Then, for each cluster, an observation well was selected as its representative, and for each zone, values of monthly precipitation and temperature, as independent variables, were estimated by the inverse-distance method. Finally, the performance of different panel-data regression models such as fixed-effects and random-effects models were investigated. The results showed that the two-way fixed-effects model was superior. The performance indicators for this model (R ²?=?0.97, RMSE?=?0.05?m and ME?=?0.81?m) reveal the effectiveness of the method. In addition, the results were compared with the results of an artificial-neural-network (ANN) model, which demonstrated the superiority of the panel-data model over the ANN model.     

Impurity effect on clear water evaporation: toward modelling wastewater evaporation using ANN,ANFIS-SC and GEP techniques

The growing shortage of freshwater resources and increasing environmental awareness give rise to the use of treated wastewater as an alternative resource for water supply. Accurate estimation of wastewater evaporation (WWE), as the main cause of water losses, is necessary for proper water resources management. Unfortunately, few studies have focused on modelling WWE despite its vital importance. This study investigates the ability of gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANN) techniques to estimate WWE as a function of variables including wastewater properties, clear water evaporation and climatic factors. The study uses measured data from an experiment conducted in Neishaboor municipal wastewater treatment plant, Iran. Results indicate that the ANN model is superior among the three methods, and also demonstrates higher accuracy when compared with those of a dimensional analysis model using the F-test statistic.     

Groundwater recharge estimation in arid hardrock‐alluvium aquifers using combined water‐table fluctuation and groundwater balance approaches

This paper proposes an approach to estimate groundwater recharge using an optimization‐based water‐table fluctuation method combined with a groundwater balance model in an arid hardrock‐alluvium region, located at the Oman–United Arab Emirates border. We introduce an “effective hardrock thickness” term to identify the percentage of the considered hardrock thickness in which effective groundwater flow takes place. The proposed method is based upon a Thiessen polygon zoning approach. The method includes subpolygons to represent specific geologic units and to enhance the confidence of the estimated groundwater recharge. Two linear and 1 nonlinear submodels were developed to evaluate the model components for the calibration (October 1996 to September 2008) and validation (October 2008 to September 2013) periods. Long‐term annual groundwater recharge from rainfall and return flow over the model domain are estimated as 24.62 and 5.71 Mm³, respectively, while the effective groundwater flow circulation is found to occur in the upper 7% of the known hardrock thickness (42 m), confirming conclusions of previous field studies. Considering a total difference in groundwater levels between eastern and western points of the study area of the order of 220 m and a 12‐year monthly calibration period, a weighted root mean squared error in predicted groundwater elevation of 2.75 m is considered quite reasonable for the study area characterized by remarkable geological and hydrogeological diversity. The proposed approach provides an efficient and robust method to estimate groundwater recharge in regions with a complex geological setting in which interaction between fractured and porous media cannot be easily assessed.