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http://dx.doi.org/10.4491/eer.2019.166

Runoff estimation using modified adaptive neuro-fuzzy inference system  

Nath, Amitabha (Department of IT, North-Eastern Hill University)
Mthethwa, Fisokuhle (Department of IT, North-Eastern Hill University)
Saha, Goutam (Department of IT, North-Eastern Hill University)
Publication Information
Environmental Engineering Research / v.25, no.4, 2020 , pp. 545-553 More about this Journal
Abstract
Rainfall-Runoff modeling plays a crucial role in various aspects of water resource management. It helps significantly in resolving the issues related to flood control, protection of agricultural lands, etc. Various Machine learning and statistical-based algorithms have been used for this purpose. These techniques resulted in outcomes with an acceptable rate of success. One of the pertinent machine learning algorithms namely Adaptive Neuro Fuzzy Inference System (ANFIS) has been reported to be a very effective tool for the purpose. However, the computational complexity of ANFIS is a major hindrance in its application. In this paper, we resolved this problem of ANFIS by incorporating one of the evolutionary algorithms known as Particle Swarm Optimization (PSO) which was used in estimating the parameters pertaining to ANFIS. The results of the modified ANFIS were found to be satisfactory. The performance of this modified ANFIS is then compared with conventional ANFIS and another popular statistical modeling technique namely ARIMA model with respect to the forecasting of runoff. In the present investigation, it was found that proposed PSO-ANFIS performed better than ARIMA and conventional ANFIS with respect to the prediction accuracy of runoff.
Keywords
ANFIS; ARIMA; Fuzzy Inference System; PSO; PSO-ANFIS; Rainfall-runoff;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 Wang WC, Chau KW, Xu DM, Chen XY. Improving forecasting accuracy of annual runoff time series using ARIMA based on eemd decomposition. Water Resour. Manag. 2015:29:2655-2675.   DOI
2 Mislan M, Haviluddim H, Hardwinarto S, Soeparto S, Aipassa M. Rainfall monthly prediction based on artificial neural network:A case study in tenggarong station, east kalimantan-indonesia. Procedia Comput. Sci. 2015;59:142-151.   DOI
3 Reshma T, Reddy KV, Pratap D, Agilan V. Parameters optimization using Fuzzy rule based multi-objective genetic algorithm for an event based rainfall-runoff model. Water Resour. Manag. 2018;32:1501-1516.   DOI
4 Tayfur G, Brocca L. Fuzzy logic for rainfall-runoff modelling considering soil moisture. Water Resour. Manag. 2015;29:3519-3533.   DOI
5 Akgun, OB, Kentel E. Estimation of streamflow using Takagi-Sugeno fuzzy rule-based model. EPiC Series in Eng. 2018;3:18-25.   DOI
6 Asadnia M, Chua L, Qin XS, Talei A. Improved particle swarm optimization-based artificial neural network for rainfall-runoff modeling. J. Hydrol. Eng. 2013;19:1320-1329.   DOI
7 Cheng CT, Niu WJ, Feng ZK, Shen JJ, Chau KW. Daily reservoir runoff forecasting method using artificial neural network based on quantum-behaved particle swarm optimization. Water 2015;7:4232-4246.   DOI
8 Mazandaranizadeh H, Motahari M. Development of a PSO-ANN model for rainfall-runoff response in basins, Case Study: Karaj Basin. Civil Eng. J. 2017;3:35-44.   DOI
9 Shabani M, Mazahery A, Rahimipour M, Tofigh A, Razavi M. The most accurate ANN learning algorithm for fem prediction of mechanical performance of alloy A356. Kov. Mater. 2012;50:25-31.
10 Rahman M., Islam AS., Nadvi SYM, Rahman RM. (2013). Comparative study of ANFIS and ARIMA model for weather forecasting in Dhaka. In: International Conference on Informatics, Electronics and Vision (ICIEV), p.1-6.
11 Tekta SM. Weather forecasting using ANFIS and ARIMA models. Environ. Res. Eng. Manag. 2010;1;5-10.
12 Azad A, Farzin S, Kashi H, Sanikhani H, Karami H, Kisi O. Prediction of river flow using hybrid neuro-fuzzy models. Arab. J. Geosci. 2018:11;718.   DOI
13 Jang JS. ANFIS: Adaptive-network-based fuzzy inference system. In: IEEE transactions on systems, man, and cybernetics, eds. Piscataway:IEEE; 1993. p. 665-685.
14 Jones AHS., Pranolo A, Dianto A, Winiarti S. Prediction of population growth using Sugeno and Adaptive Neuro-Fuzzy Inference System (ANFIS). In: IOP Conference Series eds. Mater. Sci. Eng. 2018;403:012073.
15 Azad A, Karami H, Farzin S, Saeedian A, Kashi H, Sayyahi F. Prediction of water quality parameters using ANFIS optimized by intelligence algorithms (Case study: Gorganrood River). KSCE J. Civil Eng. 2018;22:2206-2213.   DOI
16 Rezaeianzadeh M, Tabari H, Yazdi AA, Isik S, Kalin L. Flood flow forecasting using ANN, ANFIS and regression models. Neural Comput. Appl. 2014;25:25-37.   DOI
17 Zhou Y, Guo S, Chang FJ. Explore an evolutionary recurrent ANFIS for modelling multi-step-ahead flood forecasts. J. Hydrol. 2019;570:343-355.   DOI
18 Bengio Y, Simard P, Frasconi P, et al. Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Network. 1994;5:157-166.   DOI
19 Ardakani MAH, Behnia N. Estimation of suspended sediment load in different time steps using hybrid wavelet-ANFIS. Int. J. Hydrol. Sci. Technol. 2018;8:372-392.   DOI
20 Yadav RK, Balakrishnan M. Comparative evaluation of ARIMA and ANFIS for modeling of wireless network traffic time series. EURASIP J. Wirel. Comm. Network. 2014;15;1-8.
21 Dariane AB, Azimi S. Forecasting streamflow by combination of a genetic input selection algorithm and wavelet transforms using ANFIS models. Hydrol. Sci. J. 2016;61:585-600.   DOI
22 Basser H, Karami H, Shamshirband S, et al. Hybrid ANFIS-PSO approach for predicting optimum parameters of a protective spur dike. Appl. Soft. Comput. 2015;30:642-649.   DOI
23 Qasem SN, Ebtehaj I, Riahi Madavar H. Optimizing ANFIS for sediment transport in open channels using different evolutionary algorithms. J. Appl. Res. Water Waste. 2017;4:290-298.
24 Pousinho HMI, Mendes VMF, Catalao JPS. A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal. Energ. Convers. Manage. 2011;52:397-402.   DOI
25 Jalalkamali A. Using of hybrid fuzzy models to predict spatio-temporal groundwater quality parameters. Earth Sci. Inform. 2015;8:885-894.   DOI
26 Tien Bui D, Khosravi K, Li S, et al. New hybrids of anfis with several optimization algorithms for flood susceptibility modeling. Water 2018;10:1210.   DOI
27 Kisi O, Alizamir M, Zounemat-Kermani M. Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydro-climatic data. Nat. Hazard. 2017;87:367-381.   DOI
28 Kisi O, Keshavarzi A, Shiri J, Zounemat-Kermani M, Omran ESE. Groundwater quality modeling using neuro-particle swarm optimization and neuro-differential evolution techniques. Hydrol. Res. 2017;48:1508-1519.   DOI
29 Yosefvand F, Shabanlou S, Kardar S. Adaptive neuro-fuzzy Inference system optimization using PSO for predicting sediment transport in sewers. Int. J. Optim. Civil. Eng. 2019;9:331-342.
30 Azad A, Farzin S, Kashi H, Sanikhani H, Karami H, Kisi O. Prediction of river flow using hybrid neuro-fuzzy models. Arab. J. Geosci. 2018;11:718.   DOI
31 Ehteram M, Afan HA, Dianatikhah M, et al. Assessing the predictability of an improved ANFIS model for monthly streamflow using lagged climate indices as predictors. Water 2019;11:1130.   DOI
32 Talpur N, Salleh MNM, Hussain K. An investigation of MF on performance of ANFIS for solving classification problems. In: IOP Conference Series: Materials Science and Engineering, eds. Bristol: IOP Publishing ; 2017. p. 012103.
33 Ghomsheh VS, Shoorehdeli MA, Teshnehlab M. Training ANFIS structure with modified PSO algorithm. In:Mediterranean Conference on Control & Automation; 27-29 June 2007; p. 1-6.
34 Eberhart R, Kennedy J. A new optimizer using particle swarm theory. In: MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science; 4-6 October 1995; Nagoya. p. 39-43.
35 Elbedwehy MN, Zawbaa HM, Ghali N, Hassanien AE. Detection of heart disease using binary particle swarm optimization. In:Federated Conference on Computer Science and Information Systems (FedCSIS); 9-12 September 2012; Wroclaw. p. 177-182.
36 Sudheer KP, Gosain AK, Ramasastri S. A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol. Process. 2002;16:1325-1330.   DOI
37 Tran TT, Giang NN, Duong HN, Nguyen HT, Van Hoai T, Van Nghi V. A comprehensive study on predicting river runoff. In: 9th International Conference on Knowledge and Systems Engineering (KSE); 19-21 October 2017; Hue, Vietnam. p. 251-256.
38 Rezakazemi M, Dashti A, Asghari M, Shirazian S. $H_2$-selective mixed matrix membranes modeling using ANFIS, PSO-ANFIS, GA-ANFIS. Int. J. Hydrog. Energ. 2017;42:15211-15225.   DOI
39 Diaz-Robles LA, Ortega JC, Fu JS, et al. A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile. Atmos. Environ. 2008;42:8331-8340.   DOI
40 Kim BM, Teffera S, Zeldin MD. Characterization of PM25 and PM10 in the South Coast air basin of Southern California: Part 1-spatial variations. J. Air Waste Manag. Assoc. 2000;50;2034-2044.   DOI