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