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

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Okan Mert Katipoglu


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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O. Katipoglu, “Estimation of Incomplete Precipitation Data using the Adaptive Neuro-Fuzzy Inference System (ANFIS) Approach”, DataSCI, vol. 4, no. 1, pp. 11-15, Dec. 2021.
Research Articles


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