Modelling Ungauged Basins Using Remote Sensing (RS) Data and Artificial Neural Networks (ANNs) (Case Study: Ardabil Plain Basin)

Although rainfall-runoff modelling is not considered a big challenge recently, it is still one of the challenging issues for researchers in the basin or sub-basins without statistical data. One of the new methods in this field is the use of remote sensing techniques and the use of machine learning (artificial intelligence). In this research, to calculate the runoff in the basins without data, two basins were used including the basins for the Samian and the Amoghein hydrometric stations in Ardabil plain. The first station was chosen as the outlet of the Ardabil Plain basin for model training and calibration and the second station was used as the basin without data for verification and testing. Modelling was done using 9 input parameters including air pressure, vegetation cover index (low and high cover), soil temperature, ground surface temperature, soil water volume, runoff, evaporation potential and precipitation. Also, a parameter related to the observational data of the stations was used as an output. Modelling was done using four models of NARX, ANN-ACO, ANN-GA, ANN-PSO and the accuracy of the models were evaluated using MSE, R2, RMSE, NSE and MAE. The results showed that the NARX model is clearly superior to other models with an accuracy of 0.001, 0.86, 0.039, 0.855 and 0.015 respective to the above-mentioned measures. Remote sensing methods combined with artificial intelligence can respond to hydrologists’ challenges in data-scarce and ungauged basins around the world due to their ability to provide high-precision results.