Simulation of Quantity and Quality of Saq Aquifer Using Artificial Intelligence and Hydraulic Models

Table 1

Review of data-driven models and hydraulic model MODFLOW.

Characteristics	ANN/ANFIS	Hydraulic model	Remarks
Efforts/costs involved in computation expenses (need high-speed computers and time for computations)	x	About 10 times x	Although the hydraulic models like MODFLOW require computers with relatively large processing speed as compared to the data-driven models like ANN and ANFIS, which require only ordinary-type computers [12], this factor is not considered nowadays as high-speed computers are easily available.
Requirements related to the processing of models and data	x	About 3 times x	Data for only water levels, pumping rates, and concentration of contaminants are required. The data for hydraulic parameters and topography (Digital Elevation Model (DEM)) are required in addition to data for water levels, pumping rates, and concentration of contaminants. The hydraulic model (MODFLOW) requires highly accurate aquifer-parameter values. Furthermore, a mesh of an extraordinary resolution and lower time steps but qualifying the limiting ratio of time step to the nodal distance is required, whereas only a good dataset can be sufficient for data-driven models like ANN and ANFIS [13–15].
Model category	Data-driven (black box/semiblack box)	Distributed (based on laws of physics)	Hydraulic models incorporate physical processes and equations based on laws of physics in predicting groundwater levels and groundwater contamination parameters, whereas ANFIS/ANN, being data-driven models, do not use equations based on laws of physics. These models only use the recorded data for their training, testing, and validation [13–16].
Model prediction biases	x	About 10 times x	There is a wide range of variation in biases in the case of hydraulic model results, whereas the biases are mostly limited to a certain range for data-driven model (ANFIS/ANN) predictions. However, the ANFIS/ANN lack generality [13–16].
Future predictions for a long time	Highly challenging	Much easier once the model is calibrated	After calibration and validation of the hydraulic model, it is very easy to use it for future predictions, whereas very high experience and expertise in definite phenomenon is required for long-time future predictions by ANFIS/ANN [13–16].