Simulation and Prediction of Rainfall and Crop Yield in West Java Using ANFIS

Ruminta Roem, Tati Nurmala

Abstract


Simulation of numerical data for prediction purposes is very important for the planning and anticipation of the future, for example, prediction data of rainfall and agricultural production. There are various models to simulate and forecast the numerical data, one of which is a artificial intelligence model using ANFIS. In this connection it has studied a simulation and prediction of rainfall and agricultural production in West Java using ANFIS. The study uses data of rainfall and crop production. The method of this study is descriptive explanatory which is a type of quantitative analysis. Numerical data were analyzed using ANFIS of the Software Matlab 8.0. The study results showed that ANFIS can simulate rainfall and crop yield with highly accurate and has the potential to be used as one of the alternative model to predict rainfall and crop yield in West Java


Keywords


artificial intelligence, ANFIS, crop yield, numeric data, rainfall pattern

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Ahour, M., A. Nouri, M.S. Sadeghian. 2013. The Study of Artificial Neural Network (ANN) Efficiency with Neuro-Fuzzy Inference System (ANFIS) in Dissolved oxygen Simulation of River Water. Bulletin of Environment, Pharmacology and Life Sciences Bull. Env. Pharmacol. Life Sci., Vol .2 (9): 30-38.

Al-Zubaidi, S., J.A.Ghani,and C.H CheHaron. 2013. Prediction of Surface Roughness When End Milling Ti6Al4V Alloy Using Adaptive Neurofuzzy Inference System. ModellingandSimulationinEngineering, Vol. 2013.

Bisht, D.C.S., M.M. Raju, M.C. Joshi. 2009. Simulation of Water Table Elevation Fluctuation using Fuzzy-Logic and ANFIS. Computer Modelling and New Technologies, Vol.13 (2): 16–23.

Boulet, G., Chehbouni, A., Braud, I., Vauclin, M., Haverkamp, R., and Zammit, C. 2000. A Simple Water and Energy Balance Model Designed for Regionalization and Remote Sensing Data Utilization. J. Agricultural and Forest Meteorology, Vol. 105(1-3): 117-132.

Cigizoglu, H.K. 2003. Estimation, Forecasting, and Extrapolation of River Flows by Artificial Neural Networks. Hydrology Science Journal, Vol. 48(3): 349-361.

Domingo, L., Villagarcia, M., Boer, M., Arboledas, L.A., and Puigdefabregas, J. 2001. Evaluating the Long-term Water Balance of Arid Zone Stream Bed Vegetation Using Evapotranspiration Modeling and Hillslope Runoff Measurements. J. Hydrology. 243(1-2), 17-30.

Franc, J.L. and Panigrahi, S. 1997. Artificial Neural Network Models of Wheat Leaf Wetness. J. Agricultural and Forest Meteorology, Vol. 88(1-4): 57-65.

Jang, J.S.R. 1993. ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Trans. on Systems, Man and Cybernetics, Vol. 23(3): 665-685.

Kadhim, H.H. 2011. Self learning of ANFIS inverse control using iterative learning technique. Int. J. Comp. App., Vol. 21(8): 24-29.

Kumar A.R., K.P. Sudheer, S.K. Jain. and P.K. 2005. Rainfall-runoff modelling using artificial neural networks: comparison of network types. Hydrol. Process., Vol.19: 1277-1291.

Lau, K.M., C.H. Ho, and L.S. Kang. 1997. Anomalous Atmospheric Hidrologic Processes Associated with ENSO, Mechamisms of Hidrologic Cycle-Radiation Interaction. J. Climate, Vol. 11 : 800-815.

MacKay, M.D., Seglenieks, F., Verseghy, D., Soulis, E.D., Snelgrove, K.R., Walker, A., and Szeto, K. (2003). Modeling Mackenzie Basin Surface Water Balance During CAGES with the Canadian Regional Climate Model. J. Hydrometeorology, Vol. 4(4): 748-767.

Majdar, H.A. and M. Vafakhah. 2015. Monthly River Flow Prediction using Adaptive Neuro-Fuzzy Inference System (A Case Study: Gharasu Watershed, Ardabil Province-Iran). ECOPERSIA, Vol. 3(4): 1175-1188

Mashudi, M.R. 2001. Forecasting Water Demand Using Neural Networks in the Operation of Reservoirs in the Citarum Cascade, West Java, Indonesia. Dissertation, The Faculty of The Engineering Management and Systems Engineering Department, The George Washington University.

Mutlu, E., Chaubey, I., Hexmoor, H. and Bajwa, S.G. 2008. Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed. Hydrol. Process. Vol. 22(26): 5097-5106.

Nayak, P.C., K.P. Sudheer, D.M. Rangan, and K.S. Ramasastri. 2004. A neuro Fuzzy Computing Techni-que for Modeling Hydrological Time Series. J. Hydrology, Vol. 291: 52-66.

Oldeman, J. R. 1975. “An agro-climatic map of Java”. C. R. J. Agr. Bogor. Contr. Centr. Res. Inst. Agric. Bogor, No.16/1975.

Ozelkan, E.C. and Duckstein, L. 2001. Fuzzy Conceptual Rainfall-Runoff Models. J. Hydrology, Vol. 253(1-4): 41-68.

Ramesh, K., A. P. Kesarkar, J. Bhate, M.V. Ratnam, and A. Jayaraman. 2015. Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations. Atmos. Meas. Tech., Vol. 8, 369–384.

Riyanto, B., F. Febrianto, and C. Machbub. 2000. Adaptive network based Fuzzy Inference System for foprecasting daily gasoline demand. Proceedings of the Sixth AEESEAP Triennial conference, Kuta, Bali, Indonedia, August 23–25, 2000.

Roads, J.O. and A. Betts. 1999. NCEP-NCAR and ECMWF Reanalysis Surface Water and Energy Budgets for the Mississippi River Basin. J. Hydrometeorology, Vol. 1(1): 88-94.

Ruminta, 2001. Pendugaan Curah Hujan Di Wilayah Sumatra Dengan Menggunakan ANFIS. Tesis Program Magister, Institut Teknologi Bandung.

Ruminta, Bayong, T.H.K., T.H. Liong, dan I. Soekarno. 2007. Kecenderungan Hidrometeorologi di Daerah Aliran Sungai Citarum. Padjadjaran Journal of Life and Physical Sciences, Vol. 9(1): 23-37.

Salehfar, H, N. Bengiamin, and J. Huang. 2000. A Systematic approach to linguistic fuzzy modeling based on input-output data. Proceedings of the 2000 Winter Simulation Conference. J. A. Joines, R. R. Barton, K. Kang, and P. A. Fishwick, Eds., University of North Dakota, U.S.A.

Shapiro, A F. 2002. From Neural Networks, Fuzzy Logic, and Genetic Algorithms to ANFIS and Beyond. A Proposal for the American Risk and Insurance Association 2002 Annual Meeting, University Park, USA.

Shu, C. and T.B. Ouarda. 2008. Regional flood frequency nalysis at ungauged sites using the adaptive neuro-fuzzy inference system. J. Hydrol., Vol. 349(1): 31-43.

Sveinsson, O.G.B., Salas, J.D., Boes, D.C., and Pielke, R.A. (2002). Modeling Dynamics of Long-term Variability of Hydroclimatic Processes. J. Hydrometeorology, Vol. 4(3): 489-505.

Tokar, A.S. and M. Markus. 2000. Precipitation-Runoff Modeling Using Artificial Neural Networks and Conceptual Models. J. Hydrologic Engineering, Vol. 5(2): 156-161.

Toninelli, V., D.G. Salvucci, and M. Mancini. 2003. Parameter Estimation Technique for a Water Balance Model and Application to Measured Data. Hydrology Days, 192-206.

Vafakhah, M. 2012. Application of artificial neural networks and adaptive neuro-fuzzy inference system models to short-term streamflow forecasting. Can. J. Civil. Eng., Vol. 39(4): 402-414.

Wang, W., K.W. Chau, C.T. Chang, and L.A. Qui. 2009. comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J. Hydrol., Vol. 374 (3-4): 294-306.

Wang, J.S. and C.X. Ning. 2015. ANFIS Based Time Series Prediction Method of Bank Cash Flow Optimized by Adaptive Population Activity PSO Algorithm. Information, Vol. 6, 300-313.

Wooldridge, S.C. and J.D. Kalma. 2001. Regional-Scale Hydrological Modelling Using Multiple-Parameter Landscape Zones and a Quasi-distributed Water Balance Model. Hydrology and Earth System Sciences, Vol. 5(1): 59–74.

Yang, F., A. Kumar, K., Schlesinger, M.E., and Wang, W. (2003). Intensity of Hydrological Cycle in Warmer Climate. J. Climate, Vol. 16(14): 2419-2423.

Zhu, Y. (2000) : ANFIS : Adaptive Neuro Fuzzy Inference System, EE Dept., Univ. of Missouri, Rolla.




DOI: https://doi.org/10.24198/jmi.v13.n2.11844.83-94

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