Research Article
BibTex RIS Cite
Year 2021, Issue: 046, 14 - 33, 30.06.2021

Abstract

References

  • [1] Chen, S.-M. and Hwang, J.-R., (2000), Temperature prediction using fuzzy time series. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 30(2), p. 263-275.
  • [2] Rezaeianzadeh, M., Tabari, H. T., Yazdi, A.A., Isik, S. I. and Kalin, L., (2014), Flood flow forecasting using ANN, ANFIS and regression models. Neural Computing and Applications, 25(1), p. 25-37.
  • [3] Baghban, A., Bahadori, M., Rozyn, J., Lee, M., Abbas, A., Bahadori, A. and Rahimali, A. , (2016), Estimation of air dew point temperature using computational intelligence schemes. Applied thermal engineering, 93, p. 1043-1052.
  • [4] Moghaddamnia, A., Gousheh, M.G., Piri, J., Amin, S., and Han, D., (2009), Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Advances in Water Resources, 32(1), p. 88-97.
  • [5] Shiri, J., Dierickx, W., Pour-Ali Baba, A., Neamati, S., and Ghorbani, M., (2011), Estimating daily pan evaporation from climatic data of the State of Illinois, USA using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Hydrology Research, 42(6), p. 491-502.
  • [6] Eslamian, S. and Amiri, M.J., (2011), Estimation of daily pan evaporation using adaptive neural-based fuzzy inference system. International Journal of Hydrology Science and Technology, 1(3-4), p. 164-175.
  • [7] Goyal, M.K., Bharti, B., Quilty, J., Adamowski, J., and Pandey, A., (2014), Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS. Expert systems with applications, 41(11), p. 5267-5276.
  • [8] Kisi, O.,(2013), Applicability of Mamdani and Sugeno fuzzy genetic approaches for modeling reference evapotranspiration. Journal of hydrology, 504, p. 160-170.
  • [9] Pour-Ali Baba, A., Shiri, J., Kisi, O., Fard, A.F., Kim, S., and Amini, R.,(2013), Estimating daily reference evapotranspiration using available and estimated climatic data by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Hydrology Research, 44(1), p. 131-146.
  • [10] Agboola, A., Gabriel, A., Aliyu, E., and Alese, B.,(2013), Development of a fuzzy logic based rainfall prediction model. International Journal of Engineering and Technology, 3(4), p. 427-435.
  • [11] Sharifi, S.S., Delirhasannia, R., Nourani, V., Sadraddini, A.A. and Ghorbani, A., (2013), Using artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) for modeling and sensitivity analysis of effective rainfall. Recent Advances in Continuum Mechanics, Hydrology and Ecology, Mladenov V (eds), p. 133-139.
  • [12] Hashim, R., Roy, C., Motamedi, S., Shamshirband, S., Petković, D., Gocic, M. and Lee, S.C. ,(2016), Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology. Atmospheric Research, 171, p. 21-30.
  • [13] Remesan, R., Shamim, M.A., Han, D., and Mathew. J.,(2008), ANFIS and NNARX based rainfall-runoff modeling. in 2008 IEEE International Conference on Systems, Man and Cybernetics. IEEE.
  • [14] Sreekanth, P., Sreedevi, P., Ahmed, S. and Geethanjali, N., (2011), Comparison of FFNN and ANFIS models for estimating groundwater level. Environmental Earth Sciences, 62(6), p. 1301-1310.
  • [15] Alipour, Z., Ali, A.M.A., Radmanesh, F. and Joorabyan, M., (2014), Comparison of three methods of ANN, ANFIS and time series models to predict ground water level:(case study: North Mahyar plain). Bulletin of Environment, Pharmacology and Life Sciences, 3, p. 128-134.
  • [16] Pérez, E.C., Algredo-Badillo, I. and Rodríguez, V.H.G., (2012), Performance analysis of ANFIS in short term wind speed prediction. arXiv preprint arXiv:1212.2671.
  • [17] Maroufpoor, S., Sanikhani, H., Kisi, O., Deo, R.C. and Yaseen, Z.M., (2019), Long‐term modelling of wind speeds using six different heuristic artificial intelligence approaches. International Journal of Climatology, 39(8), p. 3543-3557.
  • [18] Altunkaynak, A., Özger, M. and Çakmakci, M., (2005), Water consumption prediction of Istanbul city by using fuzzy logic approach. Water Resources Management, 19(5), p. 641-654.
  • [19] Kashi, H., Emamgholizadeh, S. and Ghorbani, H., (2014), Estimation of soil infiltration and cation exchange capacity based on multiple regression, ANN (RBF, MLP), and ANFIS models. Communications in soil science and plant analysis, 45(9), p. 1195-1213.
  • [20] Kuo, C.-C., Gan, T.Y. and Yu, P.-S., (2010), Seasonal streamflow prediction by a combined climate-hydrologic system for river basins of Taiwan. Journal of hydrology, 387(3-4), p. 292-303.
  • [21] Talei, A., Chua, L.H.C., Quek, C. and Jansson, P.-E.,(2013), Runoff forecasting using a Takagi–Sugeno neuro-fuzzy model with online learning. Journal of Hydrology, 488, p. 17-32.
  • [22] Bazartseren, B., Hildebrandt, G. and Holz, K.-P., (2003), Short-term water level prediction using neural networks and neuro-fuzzy approach. Neurocomputing, 55(3-4), p. 439-450.
  • [23] Kişi, Ö.,(2013), Evolutionary neural networks for monthly pan evaporation modeling. Journal of hydrology, 498, p. 36-45.
  • [24] Nourani, V., Kisi, Ö. and Komasi, M., (2011), Two hybrid artificial intelligence approaches for modeling rainfall–runoff process. Journal of Hydrology, 402(1-2), p. 41-59.
  • [25] Valizadeh, N. and El-Shafie, A., (2013), Forecasting the level of reservoirs using multiple input fuzzification in ANFIS. Water resources management, 27(9), p. 3319-3331.
  • [26] Dorum, A., Yarar, A., Sevimli, M.F. and Onüçyildiz, M., (2010), Modelling the rainfall–runoff data of susurluk basin. Expert Systems with Applications, 37(9), p. 6587-6593.
  • [27] Hayashi, S., Murakami, S., Xu, K.-Q., and Watanabe, M., (2008), Effect of the Three Gorges Dam Project on flood control in the Dongting Lake area, China, in a 1998-type flood. Journal of Hydro-environment Research, 2(3), p. 148-163.
  • [28] El-Shafie, A., Taha, M.R., and Noureldin, A.,(2007), A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam. Water resources management, 21(3), p. 533-556.
  • [29] Wang, W.-C., Chau, K.-W., Cheng, C.-T., and Qiu, L.,(2009), A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of hydrology, 374(3-4), p. 294-306.
  • [30] Fahimi, F., Yaseen, Z.M. and El-shafie, A.,(2017), Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review. Theoretical and applied climatology, 128(3-4), p. 875-903.
  • [31] Viswavandya, M., Sarangi, B., Mohanty, S. and Mohanty, A.,(2020), Short Term Solar Energy Forecasting by Using Fuzzy Logic and ANFIS, in Computational Intelligence in Data Mining, Springer. p. 751-765.
  • [32] Heddam, S.,(2014), Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study. Environmental Monitoring and Assessment, 186(1), p. 597-619.
  • [33] Azadeh, A., Saberi, M. and Ghorbani, S., (2010), An ANFIS algorithm for improved forecasting of oil consumption: a case study of USA, Russia, India and Brazil.
  • [34] Ghassemzadeh, S., Shafflie, M., Sarrafi, A. and Ranjbar, M.,(2013), The importance of normalization in predicting dew point pressure by ANFIS. Petroleum science and technology, 31(10), p. 1040-1047.
  • [35] Jang, J.-S.,(1993), ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), p. 665-685.
  • [36] Varzandeh, M.H.M., Rahbari, O., Vafaeipour, M., Raahemifar, K. and Heidarzade. F., (2014), Performance of wavelet neural network and ANFIS algorithms for short-term prediction of solar radiation and wind velocities. in The 4th World Sustainability Forum.

THE EFFECT OF THE DATA TYPE ON ANFIS RESULTS, CASE STUDY TEMPERATURE AND RELATIVE HUMIDITY

Year 2021, Issue: 046, 14 - 33, 30.06.2021

Abstract

In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to create models to predict mean relative humidity and temperature with the most suitable inputs. To find the most appropriate data type for these meteorological parameters both hourly-daily and raw-normalized data sets were used, and results were compared. The models were trained with 2014-2017 data observed at Kirkuk city station in Iraq, were checked with both 2018 data of Kirkuk and Sanliurfa city station in Turkey to investigate whether a model set with the data of a country could be used for another country data set. The execution of models was evaluated by using root mean square error (RMSE), mean absolute error (MAE), and determination coefficient R2. Among the two parameters, the temperature achieved the best performance using relative humidity and dew point as input variables. According to the results, daily normalized data had lower error values and higher R2 than hourly un-normalized data. Additionally, the results showed that the model performed successfully at the Sanliurfa city station on the temperature parameter because of similar climate conditions to Kirkuk city.

References

  • [1] Chen, S.-M. and Hwang, J.-R., (2000), Temperature prediction using fuzzy time series. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 30(2), p. 263-275.
  • [2] Rezaeianzadeh, M., Tabari, H. T., Yazdi, A.A., Isik, S. I. and Kalin, L., (2014), Flood flow forecasting using ANN, ANFIS and regression models. Neural Computing and Applications, 25(1), p. 25-37.
  • [3] Baghban, A., Bahadori, M., Rozyn, J., Lee, M., Abbas, A., Bahadori, A. and Rahimali, A. , (2016), Estimation of air dew point temperature using computational intelligence schemes. Applied thermal engineering, 93, p. 1043-1052.
  • [4] Moghaddamnia, A., Gousheh, M.G., Piri, J., Amin, S., and Han, D., (2009), Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Advances in Water Resources, 32(1), p. 88-97.
  • [5] Shiri, J., Dierickx, W., Pour-Ali Baba, A., Neamati, S., and Ghorbani, M., (2011), Estimating daily pan evaporation from climatic data of the State of Illinois, USA using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Hydrology Research, 42(6), p. 491-502.
  • [6] Eslamian, S. and Amiri, M.J., (2011), Estimation of daily pan evaporation using adaptive neural-based fuzzy inference system. International Journal of Hydrology Science and Technology, 1(3-4), p. 164-175.
  • [7] Goyal, M.K., Bharti, B., Quilty, J., Adamowski, J., and Pandey, A., (2014), Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS. Expert systems with applications, 41(11), p. 5267-5276.
  • [8] Kisi, O.,(2013), Applicability of Mamdani and Sugeno fuzzy genetic approaches for modeling reference evapotranspiration. Journal of hydrology, 504, p. 160-170.
  • [9] Pour-Ali Baba, A., Shiri, J., Kisi, O., Fard, A.F., Kim, S., and Amini, R.,(2013), Estimating daily reference evapotranspiration using available and estimated climatic data by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Hydrology Research, 44(1), p. 131-146.
  • [10] Agboola, A., Gabriel, A., Aliyu, E., and Alese, B.,(2013), Development of a fuzzy logic based rainfall prediction model. International Journal of Engineering and Technology, 3(4), p. 427-435.
  • [11] Sharifi, S.S., Delirhasannia, R., Nourani, V., Sadraddini, A.A. and Ghorbani, A., (2013), Using artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) for modeling and sensitivity analysis of effective rainfall. Recent Advances in Continuum Mechanics, Hydrology and Ecology, Mladenov V (eds), p. 133-139.
  • [12] Hashim, R., Roy, C., Motamedi, S., Shamshirband, S., Petković, D., Gocic, M. and Lee, S.C. ,(2016), Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology. Atmospheric Research, 171, p. 21-30.
  • [13] Remesan, R., Shamim, M.A., Han, D., and Mathew. J.,(2008), ANFIS and NNARX based rainfall-runoff modeling. in 2008 IEEE International Conference on Systems, Man and Cybernetics. IEEE.
  • [14] Sreekanth, P., Sreedevi, P., Ahmed, S. and Geethanjali, N., (2011), Comparison of FFNN and ANFIS models for estimating groundwater level. Environmental Earth Sciences, 62(6), p. 1301-1310.
  • [15] Alipour, Z., Ali, A.M.A., Radmanesh, F. and Joorabyan, M., (2014), Comparison of three methods of ANN, ANFIS and time series models to predict ground water level:(case study: North Mahyar plain). Bulletin of Environment, Pharmacology and Life Sciences, 3, p. 128-134.
  • [16] Pérez, E.C., Algredo-Badillo, I. and Rodríguez, V.H.G., (2012), Performance analysis of ANFIS in short term wind speed prediction. arXiv preprint arXiv:1212.2671.
  • [17] Maroufpoor, S., Sanikhani, H., Kisi, O., Deo, R.C. and Yaseen, Z.M., (2019), Long‐term modelling of wind speeds using six different heuristic artificial intelligence approaches. International Journal of Climatology, 39(8), p. 3543-3557.
  • [18] Altunkaynak, A., Özger, M. and Çakmakci, M., (2005), Water consumption prediction of Istanbul city by using fuzzy logic approach. Water Resources Management, 19(5), p. 641-654.
  • [19] Kashi, H., Emamgholizadeh, S. and Ghorbani, H., (2014), Estimation of soil infiltration and cation exchange capacity based on multiple regression, ANN (RBF, MLP), and ANFIS models. Communications in soil science and plant analysis, 45(9), p. 1195-1213.
  • [20] Kuo, C.-C., Gan, T.Y. and Yu, P.-S., (2010), Seasonal streamflow prediction by a combined climate-hydrologic system for river basins of Taiwan. Journal of hydrology, 387(3-4), p. 292-303.
  • [21] Talei, A., Chua, L.H.C., Quek, C. and Jansson, P.-E.,(2013), Runoff forecasting using a Takagi–Sugeno neuro-fuzzy model with online learning. Journal of Hydrology, 488, p. 17-32.
  • [22] Bazartseren, B., Hildebrandt, G. and Holz, K.-P., (2003), Short-term water level prediction using neural networks and neuro-fuzzy approach. Neurocomputing, 55(3-4), p. 439-450.
  • [23] Kişi, Ö.,(2013), Evolutionary neural networks for monthly pan evaporation modeling. Journal of hydrology, 498, p. 36-45.
  • [24] Nourani, V., Kisi, Ö. and Komasi, M., (2011), Two hybrid artificial intelligence approaches for modeling rainfall–runoff process. Journal of Hydrology, 402(1-2), p. 41-59.
  • [25] Valizadeh, N. and El-Shafie, A., (2013), Forecasting the level of reservoirs using multiple input fuzzification in ANFIS. Water resources management, 27(9), p. 3319-3331.
  • [26] Dorum, A., Yarar, A., Sevimli, M.F. and Onüçyildiz, M., (2010), Modelling the rainfall–runoff data of susurluk basin. Expert Systems with Applications, 37(9), p. 6587-6593.
  • [27] Hayashi, S., Murakami, S., Xu, K.-Q., and Watanabe, M., (2008), Effect of the Three Gorges Dam Project on flood control in the Dongting Lake area, China, in a 1998-type flood. Journal of Hydro-environment Research, 2(3), p. 148-163.
  • [28] El-Shafie, A., Taha, M.R., and Noureldin, A.,(2007), A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam. Water resources management, 21(3), p. 533-556.
  • [29] Wang, W.-C., Chau, K.-W., Cheng, C.-T., and Qiu, L.,(2009), A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of hydrology, 374(3-4), p. 294-306.
  • [30] Fahimi, F., Yaseen, Z.M. and El-shafie, A.,(2017), Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review. Theoretical and applied climatology, 128(3-4), p. 875-903.
  • [31] Viswavandya, M., Sarangi, B., Mohanty, S. and Mohanty, A.,(2020), Short Term Solar Energy Forecasting by Using Fuzzy Logic and ANFIS, in Computational Intelligence in Data Mining, Springer. p. 751-765.
  • [32] Heddam, S.,(2014), Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study. Environmental Monitoring and Assessment, 186(1), p. 597-619.
  • [33] Azadeh, A., Saberi, M. and Ghorbani, S., (2010), An ANFIS algorithm for improved forecasting of oil consumption: a case study of USA, Russia, India and Brazil.
  • [34] Ghassemzadeh, S., Shafflie, M., Sarrafi, A. and Ranjbar, M.,(2013), The importance of normalization in predicting dew point pressure by ANFIS. Petroleum science and technology, 31(10), p. 1040-1047.
  • [35] Jang, J.-S.,(1993), ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), p. 665-685.
  • [36] Varzandeh, M.H.M., Rahbari, O., Vafaeipour, M., Raahemifar, K. and Heidarzade. F., (2014), Performance of wavelet neural network and ANFIS algorithms for short-term prediction of solar radiation and wind velocities. in The 4th World Sustainability Forum.
There are 36 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Pinar Bakhtiyar Abdulkareem Salıhı This is me 0000-0003-0521-9274

Nadire Üçler 0000-0001-6407-121X

Publication Date June 30, 2021
Submission Date October 5, 2020
Published in Issue Year 2021 Issue: 046

Cite

IEEE P. B. A. Salıhı and N. Üçler, “THE EFFECT OF THE DATA TYPE ON ANFIS RESULTS, CASE STUDY TEMPERATURE AND RELATIVE HUMIDITY”, JSR-A, no. 046, pp. 14–33, June 2021.