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Estimation of Incomplete Precipitation Data using the Adaptive Neuro-Fuzzy Inference System (ANFIS) Approach

Yıl 2021, Cilt: 4 Sayı: 1, 11 - 15, 30.06.2021

Öz

The completeness and continuity of precipitation data, which is one of the basic components of the hydrological cycle, is of vital importance for the planning of water resources. In this study, the gaps in the missing precipitation data in the Erzincan precipitation observation station were filled by using the adaptive neuro-fuzzy inference system (ANFIS). While Erzincan precipitation station 17094 was used as output, Bayburt 17089, Tercan 17718 and Zara 17716 precipitation stations were selected as model inputs. In the ANFIS model, monthly total precipitation data (52 years) between 1966 and 2017 were used. In the model established, 80% of the data (1968) were used for training and 20% (492) for testing. In the ANFIS model, variables were tried by dividing them into sub-sets between 3 and 8. The most suitable ANFIS model was revealed by comparing various statistical indicators. As a result of the study, 3 sub-sets, hybrid learning algorithm, trimf membership function, and model with 600 epochs were selected as the most suitable model.

Kaynakça

  • Tabii, referansları düzeltebilirim. İşte düzeltilmiş hali:
  • [1] L. Campozano, E. Sánchez Á. Avilés, and E. Samaniego, “Evaluation of infilling methods for time series of daily temperature data: case study of Limpopo Province, South Africa”. Climate, 7(7), pp. 86, 2019.
  • [2] M. Kaya, The completion with ANFIS of the missing currents data stream. Süleyman Demirel University Graduate School of Natural and Applied Sciences Department of Civil Engineering, MSc Thesis, Isparta, Turkey, 2018.
  • [3] M. Fırat, Modeling of watershed using adaptive neuro-fuzzy inference system approach. Pamukkale University Institute of Science and Technology, Doctoral dissertation, Denizli, Turkey, 2007.
  • [4] J. Piri, and M. R. R. Kahkha, “Prediction of water level fluctuations of Chahnimeh reservoirs in Zabol using ANN, ANFIS and cuckoo optimization algorithm.” Safety, and Environment, 4(2), pp. 706-715, 2017.
  • [5] H. Arslan, F. Üneş, M. Demirci, B. Taşar, and A. Yilmaz, “Estimation of Keban Dam Lake Level Change Using ANFIS and Support Vector Machines.” Osmaniye Korkut Ata University Journal of Natural and Applied Sciences, 3(2), pp. 71-77, 2020.
  • [6] V. H. Quej, J. Almorox, J. A. Arnaldo, and L. Saito, “ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment.” Journal of Atmospheric and Solar-Terrestrial Physics, 155, pp. 62-70, 2017.
  • [7] D. Tien Bui, K. Khosravi, S. Li, H. Shahabi, M. Panahi, V. P. Singh, et al. New hybrids of ANFIS with several optimization algorithms for flood susceptibility modeling. Water, 10(9), pp. 1210, 2018.
  • [8] M. Irwanto, H. Alam, M. Masri B. Ismail, W. Z. Leow, and Y. M. Irwan, “Solar energy density estimation using ANFIS based on daily maximum and minimum temperature.” International Journal of Power Electronics and Drive Systems, 10(4), pp. 2206, 2019.
  • Tabii, referansları düzeltebilirim. İşte düzeltilmiş hali:
  • [9] R. M. Adnan, A. Malik A. Kumar, K. S. Parmar, and O. Kisi, “Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs.” Arabian Journal of Geosciences, 12(19), pp. 1-14, 2019.
  • [10] W. Suparta, and A. A. Samah, “Rainfall prediction by using ANFIS times series technique in South Tangerang, Indonesia.” Geodesy and Geodynamics, 11(6), pp. 411-417, 2020.
  • [11] H. Çıtakoğlu, and Ö. Coşkun, “Precipitation Prediction of Central Anatolia Regional Stations Using Artificial Intelligence Techniques with Wavelet Transform Model.” Harran University Journal of Engineering, 6(1), pp. 39-54, 2021.
  • [12] A. Faruq A. Marto, N. K. Izzaty, A. T. Kuye, S. F. M. Hussein, and S. S. Abdullah, “Flood disaster and early warning: application of ANFIS for river water level forecasting.” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pp. 1-10, 2021.
  • [13] R. M. Adnan, A. Petroselli, S. Heddam, C. A. G. Santos, and O. Kisi, “Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model.” Stochastic Environmental Research and Risk Assessment, 35(3), pp. 597-616, 2021.
  • [14] R. V. Gerger, Gümüş, and D. Selmin, “The Evaluation of Different Artificial Intelligence Methods in Determination of Tigris Basin’s Rainfall Runoff Relationship.” BSEU Journal of Science, 8(1), pp. 300-311, 2021.
  • [15] V. Nguyen, Q. Li, and L. Nguyen, “Drought forecasting using ANFIS-a case study in drought prone area of Vietnam.” Paddy and water environment, 15(3), pp. 605-616, 2017.
  • [16] E. D. Taylan, Ö. Terzi, and T. Baykal, “Hybrid wavelet–artificial intelligence models in meteorological drought estimation.” Journal of Earth System Science, 130(1), pp. 1-13, 2021.
  • [17] Ö. Terzi, and A. Terzi, “Drought Estimation by using Adaptive Network Based Fuzzy Inference System in Çukurova Basin.” Duzce University Journal of Science & Technology, 8(1), pp. 578-588, 2020.
  • [18] C. Belvederesi, J. A. Dominic Q. K. Hassan, A. Gupta, and G. Achari, “Predicting river flow using an AI-based sequential adaptive neuro-fuzzy inference system.” Water, 12(6), pp. 1622, 2020.
  • [19] O. M. Katipoglu, “Completion of missing temperature data using Adaptive Network Based Fuzzy Inference System (ANFIS).” 4th International Conference on Data Science and Applications (ICONDATA’21), June 4-6, Turkey, 2021, pp. 6.
  • [20] D. Yıldırım, B. Cemek, and E. Küçüktopcu, “Estimation of Daily Evaporation Using Fuzzy Artificial Neural Network (ANFIS) and Multilayer Artificial Neural Network System (ANN).” Toprak Su Dergisi, pp. 24-31, 2019.
  • [21] V. Gümüş, O. Şimşek, N. G. Soydan, M. S. Aköz, and K. Yenigün, “Adana istasyonunda buharlaşmanın farklı yapay zeka yöntemleri ile tahmini.” Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 7(2), pp. 309-318, 2016.
  • [22] A. Ramazan, and K. Saplıoğlu, “Detection of sediment transport in streams by using artificial neural networks and ANFIS methods.” Niğde Omer Halisdemir University Journal of Engineering Sciences, 9(1), pp. 437-450, 2020.
  • [23] J. S. Jang, “ANFIS: adaptive-network-based fuzzy inference system.” IEEE transactions on systems, man, and cybernetics, 23(3), pp. 665-685.
  • [24] A. Özel, and M. Büyükyıldız, “Usability of artificial intelligence methods for estimation of monthly evaporation.” Niğde Ömer Halisdemir University Journal of Engineering Sciences, 8(1), pp. 244-254, 2019.
Yıl 2021, Cilt: 4 Sayı: 1, 11 - 15, 30.06.2021

Öz

Kaynakça

  • Tabii, referansları düzeltebilirim. İşte düzeltilmiş hali:
  • [1] L. Campozano, E. Sánchez Á. Avilés, and E. Samaniego, “Evaluation of infilling methods for time series of daily temperature data: case study of Limpopo Province, South Africa”. Climate, 7(7), pp. 86, 2019.
  • [2] M. Kaya, The completion with ANFIS of the missing currents data stream. Süleyman Demirel University Graduate School of Natural and Applied Sciences Department of Civil Engineering, MSc Thesis, Isparta, Turkey, 2018.
  • [3] M. Fırat, Modeling of watershed using adaptive neuro-fuzzy inference system approach. Pamukkale University Institute of Science and Technology, Doctoral dissertation, Denizli, Turkey, 2007.
  • [4] J. Piri, and M. R. R. Kahkha, “Prediction of water level fluctuations of Chahnimeh reservoirs in Zabol using ANN, ANFIS and cuckoo optimization algorithm.” Safety, and Environment, 4(2), pp. 706-715, 2017.
  • [5] H. Arslan, F. Üneş, M. Demirci, B. Taşar, and A. Yilmaz, “Estimation of Keban Dam Lake Level Change Using ANFIS and Support Vector Machines.” Osmaniye Korkut Ata University Journal of Natural and Applied Sciences, 3(2), pp. 71-77, 2020.
  • [6] V. H. Quej, J. Almorox, J. A. Arnaldo, and L. Saito, “ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment.” Journal of Atmospheric and Solar-Terrestrial Physics, 155, pp. 62-70, 2017.
  • [7] D. Tien Bui, K. Khosravi, S. Li, H. Shahabi, M. Panahi, V. P. Singh, et al. New hybrids of ANFIS with several optimization algorithms for flood susceptibility modeling. Water, 10(9), pp. 1210, 2018.
  • [8] M. Irwanto, H. Alam, M. Masri B. Ismail, W. Z. Leow, and Y. M. Irwan, “Solar energy density estimation using ANFIS based on daily maximum and minimum temperature.” International Journal of Power Electronics and Drive Systems, 10(4), pp. 2206, 2019.
  • Tabii, referansları düzeltebilirim. İşte düzeltilmiş hali:
  • [9] R. M. Adnan, A. Malik A. Kumar, K. S. Parmar, and O. Kisi, “Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs.” Arabian Journal of Geosciences, 12(19), pp. 1-14, 2019.
  • [10] W. Suparta, and A. A. Samah, “Rainfall prediction by using ANFIS times series technique in South Tangerang, Indonesia.” Geodesy and Geodynamics, 11(6), pp. 411-417, 2020.
  • [11] H. Çıtakoğlu, and Ö. Coşkun, “Precipitation Prediction of Central Anatolia Regional Stations Using Artificial Intelligence Techniques with Wavelet Transform Model.” Harran University Journal of Engineering, 6(1), pp. 39-54, 2021.
  • [12] A. Faruq A. Marto, N. K. Izzaty, A. T. Kuye, S. F. M. Hussein, and S. S. Abdullah, “Flood disaster and early warning: application of ANFIS for river water level forecasting.” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, pp. 1-10, 2021.
  • [13] R. M. Adnan, A. Petroselli, S. Heddam, C. A. G. Santos, and O. Kisi, “Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model.” Stochastic Environmental Research and Risk Assessment, 35(3), pp. 597-616, 2021.
  • [14] R. V. Gerger, Gümüş, and D. Selmin, “The Evaluation of Different Artificial Intelligence Methods in Determination of Tigris Basin’s Rainfall Runoff Relationship.” BSEU Journal of Science, 8(1), pp. 300-311, 2021.
  • [15] V. Nguyen, Q. Li, and L. Nguyen, “Drought forecasting using ANFIS-a case study in drought prone area of Vietnam.” Paddy and water environment, 15(3), pp. 605-616, 2017.
  • [16] E. D. Taylan, Ö. Terzi, and T. Baykal, “Hybrid wavelet–artificial intelligence models in meteorological drought estimation.” Journal of Earth System Science, 130(1), pp. 1-13, 2021.
  • [17] Ö. Terzi, and A. Terzi, “Drought Estimation by using Adaptive Network Based Fuzzy Inference System in Çukurova Basin.” Duzce University Journal of Science & Technology, 8(1), pp. 578-588, 2020.
  • [18] C. Belvederesi, J. A. Dominic Q. K. Hassan, A. Gupta, and G. Achari, “Predicting river flow using an AI-based sequential adaptive neuro-fuzzy inference system.” Water, 12(6), pp. 1622, 2020.
  • [19] O. M. Katipoglu, “Completion of missing temperature data using Adaptive Network Based Fuzzy Inference System (ANFIS).” 4th International Conference on Data Science and Applications (ICONDATA’21), June 4-6, Turkey, 2021, pp. 6.
  • [20] D. Yıldırım, B. Cemek, and E. Küçüktopcu, “Estimation of Daily Evaporation Using Fuzzy Artificial Neural Network (ANFIS) and Multilayer Artificial Neural Network System (ANN).” Toprak Su Dergisi, pp. 24-31, 2019.
  • [21] V. Gümüş, O. Şimşek, N. G. Soydan, M. S. Aköz, and K. Yenigün, “Adana istasyonunda buharlaşmanın farklı yapay zeka yöntemleri ile tahmini.” Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 7(2), pp. 309-318, 2016.
  • [22] A. Ramazan, and K. Saplıoğlu, “Detection of sediment transport in streams by using artificial neural networks and ANFIS methods.” Niğde Omer Halisdemir University Journal of Engineering Sciences, 9(1), pp. 437-450, 2020.
  • [23] J. S. Jang, “ANFIS: adaptive-network-based fuzzy inference system.” IEEE transactions on systems, man, and cybernetics, 23(3), pp. 665-685.
  • [24] A. Özel, and M. Büyükyıldız, “Usability of artificial intelligence methods for estimation of monthly evaporation.” Niğde Ömer Halisdemir University Journal of Engineering Sciences, 8(1), pp. 244-254, 2019.
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Yaşam ve Karmaşık Uyarlanabilir Sistemler
Bölüm Research Article
Yazarlar

Okan Mert Katipoğlu Bu kişi benim

Yayımlanma Tarihi 30 Haziran 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 4 Sayı: 1

Kaynak Göster

IEEE O. M. Katipoğlu, “Estimation of Incomplete Precipitation Data using the Adaptive Neuro-Fuzzy Inference System (ANFIS) Approach”, International Journal of Data Science and Applications, c. 4, sy. 1, ss. 11–15, 2021.

AI Research and Application Center, Sakarya University of Applied Sciences, Sakarya, Türkiye.