Title: Evaluation of hydrological and data-based models in estimation of daily runoff in Galikesh watershed

Authors: Khalil Ghorbani; Mehdi Meftah Halaghi; Elaheh Sohrabian; Saeid Golian; Mehdi Zakerinia

Addresses: Water Engineering Department, College of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan 49189-43464, Iran ' Water Engineering Department, College of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan 49189-43464, Iran ' Water Engineering Department, College of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan 49189-43464, Iran ' Civil Engineering Department, Shahrood University of Technology, Shahrood, Iran ' Water Engineering Department, College of Water and Soil Engineering, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan 49189-43464, Iran

Abstract: Estimating the runoff in a watershed is important not only in the management and proper land-use utilisation, but also plays an important role in minimising the flood damages. In this paper, four methods were used to simulate daily runoff in the Galikesh watershed. In the first method, IHACRES hydrological model was used and in other methods three data-based models, namely, artificial neural network (ANN), K-nearest neighbourhood (KNN) and adaptive neural-fuzzy inference system (ANFIS) were applied. First calibration and training of hydrological and ANN, KNN and ANFIS models, then the performance of all models were compared for both calibration and test periods. Results showed that the data-based models had better performance in simulating the rainfall-runoff process than the hydrological model. Based on observed and simulated runoffs, the correlation coefficient for KNN, ANN and ANFIS were calculated as 0.85, 0.83 and 0.85 and the root mean square error equalled to 1.106, 1.1667 and 1.1168 m³/s, respectively. This is while for IHACRES hydrological model, the correlation coefficient and root mean square error were derived as 0.67 and 1.54 m³/s, respectively.

Keywords: watersheds; runoff simulation; IHACRES; artificial neural networks; ANNs; adaptive neural-fuzzy inference system; ANFIS; K-nearest neighbourhood; KNN; fuzzy logic; hydrological modelling; data-based models; watershed runoff; simulation; rainfall runoff.

DOI: 10.1504/IJHST.2016.073882

International Journal of Hydrology Science and Technology, 2016 Vol.6 No.1, pp.27 - 44

Received: 24 Feb 2015
Accepted: 27 Jun 2015
Published online: 28 Dec 2015 *

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