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

Okan Mert Katipoğlu

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.

Keywords

Estimation, Hydrology, Machine learning

References

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Details

Primary Language

English

Subjects

Artificial Life and Complex Adaptive Systems

Journal Section

Research Article

Authors

Okan Mert Katipoğlu ^* This is me
Türkiye

Publication Date

July 1, 2021

Submission Date

April 12, 2021

Acceptance Date

June 25, 2021

Published in Issue

Year 2021 Volume: 4 Number: 1

IZ

https://izlik.org/JA25ZF36KD

APA

Katipoğlu, O. M. (2021). Estimation of Incomplete Precipitation Data using the Adaptive Neuro-Fuzzy Inference System (ANFIS) Approach. Data Science and Applications, 4(1), 11-15. https://izlik.org/JA25ZF36KD

AMA

1.Katipoğlu OM. Estimation of Incomplete Precipitation Data using the Adaptive Neuro-Fuzzy Inference System (ANFIS) Approach. DataSCI. 2021;4(1):11-15. https://izlik.org/JA25ZF36KD

Chicago

Katipoğlu, Okan Mert. 2021. “Estimation of Incomplete Precipitation Data Using the Adaptive Neuro-Fuzzy Inference System (ANFIS) Approach”. Data Science and Applications 4 (1): 11-15. https://izlik.org/JA25ZF36KD.

EndNote

Katipoğlu OM (July 1, 2021) Estimation of Incomplete Precipitation Data using the Adaptive Neuro-Fuzzy Inference System (ANFIS) Approach. Data Science and Applications 4 1 11–15.

IEEE

[1]O. M. Katipoğlu, “Estimation of Incomplete Precipitation Data using the Adaptive Neuro-Fuzzy Inference System (ANFIS) Approach”, DataSCI, vol. 4, no. 1, pp. 11–15, July 2021, [Online]. Available: https://izlik.org/JA25ZF36KD

ISNAD

Katipoğlu, Okan Mert. “Estimation of Incomplete Precipitation Data Using the Adaptive Neuro-Fuzzy Inference System (ANFIS) Approach”. Data Science and Applications 4/1 (July 1, 2021): 11-15. https://izlik.org/JA25ZF36KD.

JAMA

1.Katipoğlu OM. Estimation of Incomplete Precipitation Data using the Adaptive Neuro-Fuzzy Inference System (ANFIS) Approach. DataSCI. 2021;4:11–15.

MLA

Katipoğlu, Okan Mert. “Estimation of Incomplete Precipitation Data Using the Adaptive Neuro-Fuzzy Inference System (ANFIS) Approach”. Data Science and Applications, vol. 4, no. 1, July 2021, pp. 11-15, https://izlik.org/JA25ZF36KD.

Vancouver

1.Okan Mert Katipoğlu. Estimation of Incomplete Precipitation Data using the Adaptive Neuro-Fuzzy Inference System (ANFIS) Approach. DataSCI [Internet]. 2021 Jul. 1;4(1):11-5. Available from: https://izlik.org/JA25ZF36KD