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Year 2019, Volume: 32 Issue: 1, 145 - 162, 01.03.2019

Abstract

References

  • Anlı et al., “Regional Frequency Analysis of the Annual Maximum Precipitation Observed In Trabzon Province”, Journal of Agricultural Sciences, 15(3), 240-248, (2009).
  • Tonkaz, T., “An Assessment of Monthly Total Precipitation Characteristics in GAP Area and Generation of Synthetic Series of Monthly Precipitation Data”, Journal of Agricultural Sciences, 13(1):29-37, (2007).
  • Turhan, E., Çağatay, H. Ö., & Çetin, A., “Modelling of Rainfall-Runoff Relation with Artificial Neural Network Methods for Lower Seyhan Plain Sub-Basin and Assessment in Point of Rainy-Droughty Terms”, Çukurova University Journal of the Faculty of Engineering and Architecture, 31(2), pp. 227-241, December (2016).
  • Saplıoğlu, K. and Çimen, M., "Predicting Of Daily Precipitation Using Artificial Neural Network", Journal of Engineering Science and Design, Vol:1 No:1 pp.14-21, (2010).
  • Sharma, A. & Nijhawan, G. “Rainfall Prediction Using Neural Network”, IJCST 3.3, 65-69, (2015).
  • Taylan, E. D., "Precipitation Prediction Model with Genetic Evaluationary Programming", SDU International Journal of Technological Science 7.1 (2015).
  • Shoba, G. & Shobha, G., “Rainfall prediction using Data Mining techniques: A Survey”. Int. J. of Eng. and Comput. Sci3, no. 5: 6206-6211, (2014).
  • Zaw, W. T., & Naing, T. T. “Empirical statistical modeling of rainfall prediction over Myanmar”, World Academy of Science, Engineering and Technology 2, no. 10: 500-504, (2008).
  • Sukanya, R. and Prabha, K., "Comparative Analysis for Prediction of Rainfall using Data Mining Techniques with Artificial Neural Network." Volume-5, Issue-6, Page 288-292, (2017).
  • Deo, R. C., & Şahin, M. “Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia”, Atmospheric Research, 153, 512-525, (2015).
  • Wu, J., Long, J., & Liu, M., “Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm”, Neurocomputing, 148, 136-142, (2015).
  • Plouffe, C. C., Robertson, C., & Chandrapala, L., “Comparing interpolation techniques for monthly rainfall mapping using multiple evaluation criteria and auxiliary data sources: A case study of Sri Lanka”, Environmental Modelling & Software, 67, 57-71, (2015).
  • Partal, T., Cigizoglu, H. K., & Kahya, E., “Daily precipitation predictions using three different wavelet neural network algorithms by meteorological data”, Stochastic environmental research and risk assessment, 29(5), 1317-1329, (2015).
  • Beheshti, Z., Firouzi, M., Shamsuddin, S. M., Zibarzani, M., & Yusop, Z., “A new rainfall forecasting model using the CAPSO algorithm and an artificial neural network”, Neural Computing and Applications, 27(8), 2551-2565, (2016).
  • Tezel, G., & Buyukyildiz, M., “Monthly evaporation forecasting using artificial neural networks and support vector machines. Theoretical and applied climatology, 124(1-2), 69-80, (2016).
  • Akrami, S. A., El-Shafie, A., & Jaafar, O., “Improving rainfall forecasting efficiency using modified adaptive Neuro-Fuzzy Inference System (MANFIS)”, Water resources management, 27(9), 3507-3523, (2013).
  • Islam, S., & Talukdar, B., “Performance Improvement of a Rainfall Prediction Model using Particle Swarm Optimization”, International Journal of Computational Engineering Research, 6(7), 39-42, (2016).
  • Qiu, M., Zhao, P., Zhang, K., Huang, J., Shi, X., Wang, X., & Chu, W. “A Short-Term Rainfall Prediction Model using Multi-Task Convolutional Neural Networks”, In Data Mining (ICDM), 2017 IEEE International Conference on (pp. 395-404). IEEE. (2017, November).
  • Taormina, R., & Chau, K. W. “Data-driven input variable selection for rainfall–runoff modeling using binary-coded particle swarm optimization and extreme learning machines”, Journal of Hydrology, 529, 1617-1632. (2015).
  • Sethi, N., & Garg, K., “Exploiting data mining technique for rainfall prediction”, International Journal of Computer Science and Information Technologies, 5(3), 3982-3984. (2014).
  • Kashani, M. H., Ghorbani, M. A., Dinpashoh, Y., & Shahmorad, S., “Integration of Volterra model with artificial neural networks for rainfall-runoff simulation in forested catchment of northern Iran”, Journal of Hydrology, 540, 340-354. (2016).
  • Shafaei, M., & Kisi, O., “Predicting river daily flow using wavelet-artificial neural networks based on regression analyses in comparison with artificial neural networks and support vector machine models”, Neural Computing and Applications, 28(1), 15-28. (2017).
  • Cuevas, E., Díaz, P., Avalos, O., Zaldívar, D., & Pérez-Cisneros, M. “Nonlinear system identification based on ANFIS-Hammerstein model using Gravitational search algorithm”, Applied Intelligence, 48(1), 182-203. (2018).
  • Jang, J. S., “ANFIS: Adaptive-Network-Based Fuzzy Inference System”, IEEE Transactions on Systems, Man and Cybernetics, 23(3), 665-685, (1993).
  • Prasad, K., Gorai, A. K., Goyal, P., “Development of ANFIS models for air quality forecasting and input optimization for reducing the computational cost and time”, Atmos Environ, 128, 246–262, (2016).
  • Demuth, H., Beale, M. “ANFIS and the ANFIS Editor GUI, Fuzzy logic toolbox for use with MATLAB”, User’s Guide Version 4, 2:104-130, MA, (2000).
  • Zare, M. & Koch, M., “Groundwater level fluctuations simulation and prediction by ANFIS-and hybrid Wavelet-ANFIS/Fuzzy C-Means (FCM) clustering models: Application to the Miandarband plain”, Journal of Hydro-environment Research, 18, 63-76. (2018).
  • Karaboga, D. & Kaya, E. “Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey”, Artificial Intelligence Review, 1-31. (2018).
  • Takagi, T., Sugeno, M., “Fuzzy identification of systems and its applications to modelling and control”, IEEE Transactions on Systems, Man and Cybernetics, 15: 116–132, (1985).
  • Kramer, O., “Genetic Algorithm Essentials”, Springer, (2017).
  • Dener, M., Calp, M. H., “Solving the Exam Scheduling Problems in Central Exams with Genetic Algorithms”, Mugla Journal of Science and Technology, 4(1), 102-115, (2018).
  • Haznedar, B., Arslan, M. T., & Kalınlı, A., “Training ANFIS structure using genetic algorithm for liver cancer classification based on microarray gene expression data", Sakarya University Journal of Science, 21(1), 54-62, (2017).
  • Calp, M. H., & Akcayol, M. A., “Optimization of Project Scheduling Activities in Dynamic CPM and PERT Networks Using Genetic Algorithms”, Süleyman Demirel University Journal of Natural and Applied Sciences (SDU J Nat Appl Sci), 22(2), 615-627, (2018).
  • Calp, M. H., “Medical Diagnosis with a Novel SVM-CoDOA Based Hybrid Approach”, BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 9(4), 6-16, (2018).
  • Haznedar, B. and Kalinli, A., "Training ANFIS structure using simulated annealing algorithm for dynamic systems identification", Neurocomputing 302: 66-74, (2018).
  • Kacher, O., Boltnev, A., Kalitkin, N., “Speeding up the convergence of simple grained methods”, in: Proceedings of the 10th International Conference, pp. 349-354, (2005).
  • Simon, D. J., “Training fuzzy systems with the extended Kalman filter”, Fuzzy sets and systems 132(2), 189, (2002).
  • Meteoblue Weather, https://www.meteoblue.com/en/weather/archive/export/basel_switzerland_2661604, Access Date: 07.04.2018.
  • Bayram, S., Kaplan, K., Kuncan, M., Ertunç, H. M., “Ball Bearings space of time Statistical Feature Extraction and Neural Networks with Error Estimation Method Size”, Automatic Control National Meeting, TOK2013, Malatya, 26-28 September, (2013).
  • Calp, M. H., “An estimation of personnel food demand quantity for businesses by using artificial neural networks”, DOI: 10.2339/politeknik.444380, (2019). (Accepted - In Press).
  • Özkan, M. T., Eldem, C., & Köksal, E., “Notch Sensitivity Factor Determination with Artificial Neural Network for Shafts under the Bending Stress”, Pamukkale University Journal of Engineering Sciences, 19(1), 24-32, (2013).
  • Gandomi, A. H. & Roke, D. A., “Assessment of artificial neural network and genetic programming as predictive tools”, Advances in Engineering Software, 88, 63-72, (2015).
  • Elmas, Ç., “Artificial Intelligence Applications”, Seçkin Publishing, First Edition, November, (2007).
  • Calp, M. H., Şahin, İ., “The Determination by Using Fuzzy Expert System of the Usability Level of Website User Interface Design”, International Journal of Human Sciences, Volume: 10 Special Issue, 141-150, (2013).
  • Isiaka, R.M., Omidiora, E.O, Olabiyisi, S.O, and Okediran, O.O., “Mamdani fuzzy model for learning activities evaluation”, International Journal of Applied Information Systems (IJAIS). Foundation of Computer Science FCS. New York, USA. 7(3): p. 1-8, (2014).
  • Pereira, L. F., Patil, S. A., & Mahadeshwar, C. D., “Artifact removal from EEG using ANFIS-GA”, In Green Engineering and Technologies (IC-GET), 2016 Online International Conference on (pp. 1-6). IEEE, (2016, November).
  • Wahyuni, I. and Mahmudy, W. F., “Rainfall Prediction in Tengger-Indonesia Using Hybrid Tsukamoto FIS and Genetic Algorithm”, Submited to J. ICT Res. Appl., pp. 1–8, (2016).
  • Moradi, S.T. & Nikolaev, N.I., “Optimization of Cement Spacer Rheology Model Using Genetic Algorithm”, IJE Trans. A Basics, 29(1), pp. 127-131, (2016).
  • Lu, J. T., Chang, Y. C., & Ho, C. Y., “The optimization of chiller loading by adaptive neuro-fuzzy inference system and genetic algorithms,” Mathematical Problems in Engineering, (2015).
  • Sarkheyli, A., Zain, A. M., & Sharif, S., “A multi-performance prediction model based on ANFIS and new modified-GA for machining processes”, Journal of Intelligent Manufacturing, 26(4), 703-716, (2015).
  • Wahyuni, I., Mahmudy, W. F., & Iriany, A., “Rainfall Prediction using Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) and Genetic Algorithm”, Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-8), 51-56, (2017).
  • Lee, Y.G., "Genetic Optimization Applied to Larry Connors Trading System in Two Markets", Review of Computational Science and Engineering 3, no. 3:29, (2017).
  • Qasem, S. N., Ebtehaj, I., & Riahi Madavar, H., “Optimizing ANFIS for sediment transport in open channels using different evolutionary algorithms”, Journal of Applied Research in Water and Wastewater, 4(1), 290-298, (2017).

A Hybrid ANFIS-GA Approach for Estimation of Regional Rainfall Amount

Year 2019, Volume: 32 Issue: 1, 145 - 162, 01.03.2019

Abstract

Effective use and management of ever-diminishing water resources are
critically important to the future of humanity. At this point, rainfall is one
of the most important factors that supply water resources, but the fact that
the rainfall higher is more than normal causes many disasters such as flood,
erosion. Therefore, rainfall amount must be analyzed mathematically,
statistically or heuristically in order to take precautions, in the region. In
this study, an Adaptive Neuro Fuzzy Inference System - Genetic Algorithm
(ANFIS-GA) based hybrid model was proposed for estimation of regional rainfall
amount. Purpose of the study is to minimize the loss of life and goods for
people of the region by estimating the amount of annual rainfall and ensuring
effective management of water resources and allowing some evaluations and
preparations according to possible climate changes. The estimation model was
developed by coding in the MATLAB package program. In the development of the
model, 3650 meteorological data from 2008-2018 years belonging to Basel, a
Swiss city, were utilized. The real data were tested on both the Artificial
Neural Network (ANN) and the hybrid ANFIS-GA model. The obtained results
demonstrated that the training R-value of the suggested ANFIS-GA model was
0.9920, the testing R-value was 0.9840 and the error ratio was 0.0011. This
clearly shows that predictive performance of the model is high and error level
is low, and therefore that hybrid approaches such as ANFIS-GA can be easily
used in predicting meteorological events.

References

  • Anlı et al., “Regional Frequency Analysis of the Annual Maximum Precipitation Observed In Trabzon Province”, Journal of Agricultural Sciences, 15(3), 240-248, (2009).
  • Tonkaz, T., “An Assessment of Monthly Total Precipitation Characteristics in GAP Area and Generation of Synthetic Series of Monthly Precipitation Data”, Journal of Agricultural Sciences, 13(1):29-37, (2007).
  • Turhan, E., Çağatay, H. Ö., & Çetin, A., “Modelling of Rainfall-Runoff Relation with Artificial Neural Network Methods for Lower Seyhan Plain Sub-Basin and Assessment in Point of Rainy-Droughty Terms”, Çukurova University Journal of the Faculty of Engineering and Architecture, 31(2), pp. 227-241, December (2016).
  • Saplıoğlu, K. and Çimen, M., "Predicting Of Daily Precipitation Using Artificial Neural Network", Journal of Engineering Science and Design, Vol:1 No:1 pp.14-21, (2010).
  • Sharma, A. & Nijhawan, G. “Rainfall Prediction Using Neural Network”, IJCST 3.3, 65-69, (2015).
  • Taylan, E. D., "Precipitation Prediction Model with Genetic Evaluationary Programming", SDU International Journal of Technological Science 7.1 (2015).
  • Shoba, G. & Shobha, G., “Rainfall prediction using Data Mining techniques: A Survey”. Int. J. of Eng. and Comput. Sci3, no. 5: 6206-6211, (2014).
  • Zaw, W. T., & Naing, T. T. “Empirical statistical modeling of rainfall prediction over Myanmar”, World Academy of Science, Engineering and Technology 2, no. 10: 500-504, (2008).
  • Sukanya, R. and Prabha, K., "Comparative Analysis for Prediction of Rainfall using Data Mining Techniques with Artificial Neural Network." Volume-5, Issue-6, Page 288-292, (2017).
  • Deo, R. C., & Şahin, M. “Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia”, Atmospheric Research, 153, 512-525, (2015).
  • Wu, J., Long, J., & Liu, M., “Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm”, Neurocomputing, 148, 136-142, (2015).
  • Plouffe, C. C., Robertson, C., & Chandrapala, L., “Comparing interpolation techniques for monthly rainfall mapping using multiple evaluation criteria and auxiliary data sources: A case study of Sri Lanka”, Environmental Modelling & Software, 67, 57-71, (2015).
  • Partal, T., Cigizoglu, H. K., & Kahya, E., “Daily precipitation predictions using three different wavelet neural network algorithms by meteorological data”, Stochastic environmental research and risk assessment, 29(5), 1317-1329, (2015).
  • Beheshti, Z., Firouzi, M., Shamsuddin, S. M., Zibarzani, M., & Yusop, Z., “A new rainfall forecasting model using the CAPSO algorithm and an artificial neural network”, Neural Computing and Applications, 27(8), 2551-2565, (2016).
  • Tezel, G., & Buyukyildiz, M., “Monthly evaporation forecasting using artificial neural networks and support vector machines. Theoretical and applied climatology, 124(1-2), 69-80, (2016).
  • Akrami, S. A., El-Shafie, A., & Jaafar, O., “Improving rainfall forecasting efficiency using modified adaptive Neuro-Fuzzy Inference System (MANFIS)”, Water resources management, 27(9), 3507-3523, (2013).
  • Islam, S., & Talukdar, B., “Performance Improvement of a Rainfall Prediction Model using Particle Swarm Optimization”, International Journal of Computational Engineering Research, 6(7), 39-42, (2016).
  • Qiu, M., Zhao, P., Zhang, K., Huang, J., Shi, X., Wang, X., & Chu, W. “A Short-Term Rainfall Prediction Model using Multi-Task Convolutional Neural Networks”, In Data Mining (ICDM), 2017 IEEE International Conference on (pp. 395-404). IEEE. (2017, November).
  • Taormina, R., & Chau, K. W. “Data-driven input variable selection for rainfall–runoff modeling using binary-coded particle swarm optimization and extreme learning machines”, Journal of Hydrology, 529, 1617-1632. (2015).
  • Sethi, N., & Garg, K., “Exploiting data mining technique for rainfall prediction”, International Journal of Computer Science and Information Technologies, 5(3), 3982-3984. (2014).
  • Kashani, M. H., Ghorbani, M. A., Dinpashoh, Y., & Shahmorad, S., “Integration of Volterra model with artificial neural networks for rainfall-runoff simulation in forested catchment of northern Iran”, Journal of Hydrology, 540, 340-354. (2016).
  • Shafaei, M., & Kisi, O., “Predicting river daily flow using wavelet-artificial neural networks based on regression analyses in comparison with artificial neural networks and support vector machine models”, Neural Computing and Applications, 28(1), 15-28. (2017).
  • Cuevas, E., Díaz, P., Avalos, O., Zaldívar, D., & Pérez-Cisneros, M. “Nonlinear system identification based on ANFIS-Hammerstein model using Gravitational search algorithm”, Applied Intelligence, 48(1), 182-203. (2018).
  • Jang, J. S., “ANFIS: Adaptive-Network-Based Fuzzy Inference System”, IEEE Transactions on Systems, Man and Cybernetics, 23(3), 665-685, (1993).
  • Prasad, K., Gorai, A. K., Goyal, P., “Development of ANFIS models for air quality forecasting and input optimization for reducing the computational cost and time”, Atmos Environ, 128, 246–262, (2016).
  • Demuth, H., Beale, M. “ANFIS and the ANFIS Editor GUI, Fuzzy logic toolbox for use with MATLAB”, User’s Guide Version 4, 2:104-130, MA, (2000).
  • Zare, M. & Koch, M., “Groundwater level fluctuations simulation and prediction by ANFIS-and hybrid Wavelet-ANFIS/Fuzzy C-Means (FCM) clustering models: Application to the Miandarband plain”, Journal of Hydro-environment Research, 18, 63-76. (2018).
  • Karaboga, D. & Kaya, E. “Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey”, Artificial Intelligence Review, 1-31. (2018).
  • Takagi, T., Sugeno, M., “Fuzzy identification of systems and its applications to modelling and control”, IEEE Transactions on Systems, Man and Cybernetics, 15: 116–132, (1985).
  • Kramer, O., “Genetic Algorithm Essentials”, Springer, (2017).
  • Dener, M., Calp, M. H., “Solving the Exam Scheduling Problems in Central Exams with Genetic Algorithms”, Mugla Journal of Science and Technology, 4(1), 102-115, (2018).
  • Haznedar, B., Arslan, M. T., & Kalınlı, A., “Training ANFIS structure using genetic algorithm for liver cancer classification based on microarray gene expression data", Sakarya University Journal of Science, 21(1), 54-62, (2017).
  • Calp, M. H., & Akcayol, M. A., “Optimization of Project Scheduling Activities in Dynamic CPM and PERT Networks Using Genetic Algorithms”, Süleyman Demirel University Journal of Natural and Applied Sciences (SDU J Nat Appl Sci), 22(2), 615-627, (2018).
  • Calp, M. H., “Medical Diagnosis with a Novel SVM-CoDOA Based Hybrid Approach”, BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 9(4), 6-16, (2018).
  • Haznedar, B. and Kalinli, A., "Training ANFIS structure using simulated annealing algorithm for dynamic systems identification", Neurocomputing 302: 66-74, (2018).
  • Kacher, O., Boltnev, A., Kalitkin, N., “Speeding up the convergence of simple grained methods”, in: Proceedings of the 10th International Conference, pp. 349-354, (2005).
  • Simon, D. J., “Training fuzzy systems with the extended Kalman filter”, Fuzzy sets and systems 132(2), 189, (2002).
  • Meteoblue Weather, https://www.meteoblue.com/en/weather/archive/export/basel_switzerland_2661604, Access Date: 07.04.2018.
  • Bayram, S., Kaplan, K., Kuncan, M., Ertunç, H. M., “Ball Bearings space of time Statistical Feature Extraction and Neural Networks with Error Estimation Method Size”, Automatic Control National Meeting, TOK2013, Malatya, 26-28 September, (2013).
  • Calp, M. H., “An estimation of personnel food demand quantity for businesses by using artificial neural networks”, DOI: 10.2339/politeknik.444380, (2019). (Accepted - In Press).
  • Özkan, M. T., Eldem, C., & Köksal, E., “Notch Sensitivity Factor Determination with Artificial Neural Network for Shafts under the Bending Stress”, Pamukkale University Journal of Engineering Sciences, 19(1), 24-32, (2013).
  • Gandomi, A. H. & Roke, D. A., “Assessment of artificial neural network and genetic programming as predictive tools”, Advances in Engineering Software, 88, 63-72, (2015).
  • Elmas, Ç., “Artificial Intelligence Applications”, Seçkin Publishing, First Edition, November, (2007).
  • Calp, M. H., Şahin, İ., “The Determination by Using Fuzzy Expert System of the Usability Level of Website User Interface Design”, International Journal of Human Sciences, Volume: 10 Special Issue, 141-150, (2013).
  • Isiaka, R.M., Omidiora, E.O, Olabiyisi, S.O, and Okediran, O.O., “Mamdani fuzzy model for learning activities evaluation”, International Journal of Applied Information Systems (IJAIS). Foundation of Computer Science FCS. New York, USA. 7(3): p. 1-8, (2014).
  • Pereira, L. F., Patil, S. A., & Mahadeshwar, C. D., “Artifact removal from EEG using ANFIS-GA”, In Green Engineering and Technologies (IC-GET), 2016 Online International Conference on (pp. 1-6). IEEE, (2016, November).
  • Wahyuni, I. and Mahmudy, W. F., “Rainfall Prediction in Tengger-Indonesia Using Hybrid Tsukamoto FIS and Genetic Algorithm”, Submited to J. ICT Res. Appl., pp. 1–8, (2016).
  • Moradi, S.T. & Nikolaev, N.I., “Optimization of Cement Spacer Rheology Model Using Genetic Algorithm”, IJE Trans. A Basics, 29(1), pp. 127-131, (2016).
  • Lu, J. T., Chang, Y. C., & Ho, C. Y., “The optimization of chiller loading by adaptive neuro-fuzzy inference system and genetic algorithms,” Mathematical Problems in Engineering, (2015).
  • Sarkheyli, A., Zain, A. M., & Sharif, S., “A multi-performance prediction model based on ANFIS and new modified-GA for machining processes”, Journal of Intelligent Manufacturing, 26(4), 703-716, (2015).
  • Wahyuni, I., Mahmudy, W. F., & Iriany, A., “Rainfall Prediction using Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) and Genetic Algorithm”, Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-8), 51-56, (2017).
  • Lee, Y.G., "Genetic Optimization Applied to Larry Connors Trading System in Two Markets", Review of Computational Science and Engineering 3, no. 3:29, (2017).
  • Qasem, S. N., Ebtehaj, I., & Riahi Madavar, H., “Optimizing ANFIS for sediment transport in open channels using different evolutionary algorithms”, Journal of Applied Research in Water and Wastewater, 4(1), 290-298, (2017).
There are 53 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Computer Engineering
Authors

M. Hanefi Calp 0000-0001-7991-438X

Publication Date March 1, 2019
Published in Issue Year 2019 Volume: 32 Issue: 1

Cite

APA Calp, M. H. (2019). A Hybrid ANFIS-GA Approach for Estimation of Regional Rainfall Amount. Gazi University Journal of Science, 32(1), 145-162.
AMA Calp MH. A Hybrid ANFIS-GA Approach for Estimation of Regional Rainfall Amount. Gazi University Journal of Science. March 2019;32(1):145-162.
Chicago Calp, M. Hanefi. “A Hybrid ANFIS-GA Approach for Estimation of Regional Rainfall Amount”. Gazi University Journal of Science 32, no. 1 (March 2019): 145-62.
EndNote Calp MH (March 1, 2019) A Hybrid ANFIS-GA Approach for Estimation of Regional Rainfall Amount. Gazi University Journal of Science 32 1 145–162.
IEEE M. H. Calp, “A Hybrid ANFIS-GA Approach for Estimation of Regional Rainfall Amount”, Gazi University Journal of Science, vol. 32, no. 1, pp. 145–162, 2019.
ISNAD Calp, M. Hanefi. “A Hybrid ANFIS-GA Approach for Estimation of Regional Rainfall Amount”. Gazi University Journal of Science 32/1 (March 2019), 145-162.
JAMA Calp MH. A Hybrid ANFIS-GA Approach for Estimation of Regional Rainfall Amount. Gazi University Journal of Science. 2019;32:145–162.
MLA Calp, M. Hanefi. “A Hybrid ANFIS-GA Approach for Estimation of Regional Rainfall Amount”. Gazi University Journal of Science, vol. 32, no. 1, 2019, pp. 145-62.
Vancouver Calp MH. A Hybrid ANFIS-GA Approach for Estimation of Regional Rainfall Amount. Gazi University Journal of Science. 2019;32(1):145-62.