Rainfall-runoff simulation in Saqez sub-basin using artificial neural network model

Introduction

The rainfall-runoff process, which is affected by various hydrological parameters, is one of the most complex hydrological processes and one of the most basic hydrological topics related to understanding and predicting the processes of runoff production and transfer. It is the outlet point of the watershed. Planning and optimal utilization of runoff is one of the essential issues in watersheds. Therefore, knowing the natural capacity of runoff production and simulating rainfall-runoff is very important. Artificial intelligence and the use of neural network models are one of the methods of rainfall-runoff forecasting. An artificial neural network is a method with the ability to learn, understand, master relationships, and resist errors. Today, artificial intelligence black box methods such as self-constructing and self-learning functions have a wide ability to model and predict complex problems.

Materials and Methods

The purpose of this research is to evaluate the performance of the artificial neural network model for rainfall-runoff simulation in the Saghez sub-basin in Kurdistan province. To carry out this research, 18-year (2001-2018) data received daily from the Meteorological Organization and Saghez Regional Water and Hydrometry Company have been used. In this study, two types of meteorological and hydrometric data were used. The meteorological parameters used include precipitation, evaporation, average temperature, maximum and minimum temperature, and the hydrometric parameter used in this research was only discharge. In the Saghez basin, rainfall-runoff changes have always been considered one of the prominent hydrological indicators. Since the turpentine sub-basin is considered an open basin in terms of its nature, precipitation can be considered a suitable alternative for investigating discharge in the study area of this research. As a result, precipitation is selected as a potential input variable and the adequacy of the remaining variables will be tested separately for the neural network model. In this research, the meteorological parameters used include precipitation, evaporation, average temperature, and maximum and minimum temperature, and the hydrometric parameter used in this research was only Dubai. Finally, to simulate rainfall-runoff using an artificial neural network model, scenarios with different input variables were considered. To evaluate and validate the performance results of the simulated model in different scenarios of this study, using four statistical criteria of correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe index (NSE) was done.

Results and Discussion

Six investigated scenarios were randomly selected by combining different inputs. In the first scenario, the input variable includes precipitation and the output variable is discharge. In the second scenario, the input variables include precipitation and evaporation and the output variable is discharge. In the third scenario, the input variables include precipitation and average temperature and the output variable is discharge. In the fourth scenario, the input variables include precipitation and flow variables with a one-day delay and the output variable of flow. In the fifth scenario, the input variables include precipitation, average temperature, maximum and minimum temperature, and the output variable is discharge. In the sixth scenario, the input variables include precipitation, evaporation, average temperature, maximum and minimum temperature, and the output variable of discharge. In all six scenarios, the output variable is the flow rate. Also, in the modeling, 70% of the data for the training section and 30% of the data for the test section were examined. According to the final results, the performance of the artificial neural network model in scenario number four (input variables including rainfall and discharge with a one-day delay) among the six developed scenarios, with correlation coefficient values of 0.92, mean squared error of 6.65, the average absolute error is 2.04 and the Nash-Sutcliffe index is 0.84 in the education section with the values of 0.91, 5.34, 1.57, and respectively 0.82 selected as the best combination in the test section, and in terms of statistical performance indicators, the results of the Nash-Sutcliffe index values in the training and test section were closer to one, which indicates a good match between the observed values and It is simulated. Also, the correlation coefficient specifies the amount of agreement and distribution of observational data with the predicted results, which can be said that the error measurement indicators and data distribution in the training and test section are a favorable result for prediction. The amount of discharge in this scenario shows that it has a much better performance than the rest of the scenarios. Also, in the fourth scenario, changes in the time series of observed discharge values against the simulated values in the training and test phase were investigated in the artificial neural network model. According to this figure, compared to the observed value, the simulated flow rate had good accuracy and an acceptable error value.

Conclusion

The obtained results showed that for the sub-basin of turpentine, the algorithm of the artificial neural network model for simulating rainfall-runoff on a suitable daily scale has obtained suitable and acceptable results. So it can be said that artificial neural network modeling has high accuracy and low error for the study area. Also, artificial intelligence models can be used as a useful tool and a reliable approach for water resource managers.