|تعداد مشاهده مقاله||7,656,515|
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Simulation of rainfall-runoff process using geomorphology-based adaptive neuro-fuzzy inference system (ANFIS)
|Caspian Journal of Environmental Sciences|
|مقاله 2، دوره 18، شماره 2، تیر 2020، صفحه 109-122 اصل مقاله (1.51 M)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.22124/cjes.2020.4067|
|Shabanali Gholami1؛ Mehdi Vafakhah* 2؛ Kamal Ghaderi1؛ Mohammad Reza Javadi1|
|1Department of Natural Resources, Noor Branch, Islamic Azad University, Noor, Iran|
|2Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran|
|This research was conducted to present an integrated rainfall-runoff model based on the physical characteristics of the watershed, and to predict discharge not only in the outlet, but also at any desired point within the basin. To achieve this goal, a matrix of hydro-climatic variables (i.e. daily rainfall and daily discharge) and geomorphologic characteristics such as upstream drainage area (A), mean slope of watershed (S) and curve number (CN) was designed and simulated using artificial intelligence techniques. Integrated Geomorphology-based Artificial Neural Network (IGANN) model with Root Mean Squared Error (RMSE) of 0.02786 m3 s-1 and Nash-Sutcliffe Efficiency (NSE) of 0.9403 and Integrated Geomorphology-based Adaptive Neuro-Fuzzy Inference System (IGANFIS) model with RMSE of 0.02795 m3 s-1 and NSE of 0.94467 were able to predict the discharge values of all hydrometric stations of the Chalus River watershed with a very low error and high accuracy. The results of cross validation stage confirmed the efficiency of models. Hydro-climatic variables and geomorphologic parameters selected in the study were: discharge of one day ago, discharge of two days ago, rainfall of current day and rainfall of one day ago and S, CN and A, respectively. In addition, the IGANN model shows superiority compared with the IGANFIS model.|
|Physical characteristics of watershed؛ Rainfall-runoff modeling؛ Black box modeling؛ Artificial intelligence؛ Geomorphologic unit hydrograph|
Aalami, MT, Hosseinzadeh, H 2010, Modeling rainfall – runoff process in Lighvan Chai basin using conditional threshold temperature neuron. Water and Soil Science, 20: 97-110.
Asadi, S, Shahrabi, J, Abbaszadeh, P Tabanmehr, S 2013, A new hybrid artificial neural networks for rainfall–runoff process modeling. Neurocomputing, 121: 470-480.
Dastorani, MT, Sharifi Darani, H, Ali, T, Moghadam Nia, A 2012, Evaluation of the application of artificial neural networks and adaptive neuro-fuzzy inference systems for rainfall-runoff modelling in Zayandeh_rood Dam Basin. Iranian Journal of Water and Wastewater, 22: 114-125.
Eshagh Teimori, MA, Habibnejad, M, Kaviyan, A, Shahedi, K 2012, Estimation of rainfall-runoff process in basin with low data using wms model (Case study: Chalus Watershed). Irannian Journal of Irrigation & Water Engineering, 3:12-25.
Firat, M, Güngör, M 2007, River flow estimation using adaptive neuro fuzzy inference system. Mathematics and Computers in Simulation, 75: 87-96.
Ghose, D, Panda, S, Swain, P 2013, Prediction and optimization of runoff via ANFIS and GA. Alexandria Engineering Journal, 52: 209-220.
Green, I, Stephenson, D 1986, Criteria for comparison of single event models. Hydrological Sciences Journal, 31: 395-411.
Jacquin, AP, Shamseldin, AY 2006, Development of rainfall–runoff models using Takagi–Sugeno fuzzy inference systems. Journal of Hydrology, 329: 154-173.
Jacquin, AP, Shamseldin, AY 2009, Review of the application of fuzzy inference systems in river flow forecasting. Journal of Hydroinformatics, 11: 202-210.
Jang, J-S 1993, ANFIS: adaptive-network-based fuzzy inference system, IEEE transactions on systems. Man, and Cybernetics, 23: 665-685.
Jayawardena, A, Perera, E, Zhu, B, Amarasekara, J, Vereivalu, V 2014, A comparative study of fuzzy logic systems approach for river discharge prediction. Journal of Hydrology, 514: 85-101.
Kisi, O, Shiri, J, Tombul, M 2013, Modeling rainfall-runoff process using soft computing techniques. Computers & Geosciences, 51: 108-117.
Kurtulus, B, Razack, M 2010, Modeling daily discharge responses of a large karstic aquifer using soft computing methods: Artificial neural network and neuro-fuzzy. Journal of Hydrology, 381: 101-111.
Lohani, A, Kumar, R, Singh, R 2012, Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. Journal of Hydrology, 442: 23-35.
Moghimi, A, Mousavi Harami, R, Motamed, A, Ahmadi, H 2009, Investigating the effect of morphometric variables of watershed on maximum flood discharge in the Chalous river basin using statistical methods and mathematical models. Journal of Land and Resources, Islamic Azad University, Lahijan Branch, 2: 65-80.
Nash, JE, Sutcliffe, JV 1970, River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 10: 282-290.
Nayak, P, Sudheer, K, Jain, S 2007, Rainfall‐runoff modeling through hybrid intelligent system. Water Resources Research, 43, W07415, doi:10.1029/2006WR004930.
Nayak, PC, Sudheer, K, Rangan, D, Ramasastri, K 2004, A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology, 291: 52-66.
Nourani, V, Kalantari, O 2010, Integrated artificial neural network for spatiotemporal modeling of rainfall–runoff–sediment processes. Environmental Engineering Science, 27: 411-422.
Nourani, V, Kisi, Ö, Komasi, M 2011, Two hybrid artificial intelligence approaches for modeling rainfall–runoff process. Journal of Hydrology, 402: 41-59.
Nourani, V, Komasi, M 2013, A geomorphology-based ANFIS model for multi-station modeling of rainfall–runoff process. Journal of Hydrology, 490: 41-55.
Rashidi, S, Vafakhah, M, Lafdani, EK, Javadi, MR 2016, Evaluating the support vector machine for suspended sediment load forecasting based on gamma test. Arabian Journal of Geosciences, 9: 583.
Talei, A, Chua, LH 2012, Influence of lag time on event-based rainfall–runoff modeling using the data driven approach. Journal of Hydrology, 438: 223-233.
Talei, A, Chua, LHC, Wong, TS 2010, Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall–runoff modeling. Journal of Hydrology, 391: 248-262.
Vafakhah, M 2012, Application of artificial neural networks and adaptive neuro-fuzzy inference system models to short-term streamflow forecasting. Canadian Journal of Civil Engineering, 39: 402-414.
Wang, W-C, Chau, K-W, Cheng, C-T, Qiu, L 2009, A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology, 374: 294-306.
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