This work is licensed under a Creative Commons Attribution 4.0 International License.Rainfall Prediction Using Adaptive Neuro-Fuzzy Inference System
Uyarlamalı Ağ Tabanlı Bulanık Mantık Çıkarım Sistemi İle Yağış Tahmini
How to cite: Terzi Ã, ÖzcanoÄŸlu O, Baykal T. Rainfall prediction using adaptive neuro-fuzzy inference system. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2018; 1(1): 23-25. DOI: 10.54856/jiswa.201805009
Full Text: PDF, in Turkish.
Total number of downloads: 715
Title: Rainfall Prediction Using Adaptive Neuro-Fuzzy Inference System
Abstract: The rainfall prediction is of great importance in the utilization and planning of water resources. In this study, the validity of Adaptive Neuro-Fuzzy Inference System (ANFIS) in rainfall prediction is investigated. The ANFIS models are developed with different input combinations and it is observed that ANFIS models give successful results in rainfall prediction.
Keywords: Rainfall; water resources; ANFIS model; Isparta
Başlık: Uyarlamalı Ağ Tabanlı Bulanık Mantık Çıkarım Sistemi İle Yağış Tahmini
Özet: Yağış tahmini, su kaynaklarının doğru bir şekilde kullanımı ve planlanmasında oldukça önemli bir yer tutmaktadır. Bu çalışmada, yağış tahmininde uyarlamalı ağ tabanlı bulanık mantık çıkarım sisteminin (ANFIS) geçerliliği araştırılmıştır. Farklı girdi kombinasyonları ile modeller geliştirilmiş ve yağış tahmini için ANFIS modellerinin başarılı sonuçlar verdiği belirlenmiştir.
Anahtar kelimeler: Yağış; su kaynakları; ANFIS modeli; Isparta
- Partal T, Kahya E, Cigizoglu, K. Estimation of precipitation data using artificial neural networks and wavelet transform. Itudergisi Serie D: Engineering 2008; 7(3): 73-85.
- Terzi O, Cevik E. Rainfall estimation using artificial neural network method. International Journal of Technological Science 2012; 4(1): 10-19.
- Saplioglu K, Cimen M. Predicting of daily precipitation using artificial neural network. Journal of Engineering Science and Design 2010; 1(1): 14-21.
- Taylan ED. Precipitation prediction model with genetic evaluationary programming. International Journal of Technological Sciences 2015; 7(1): 8-21.
- Sharifi SS, Delirhasannia R, Nourani V, Sadraddini AA, Ghorbani A. Using artificial neural networks and adaptive neuro-fuzzy inference system for modeling and sensitivity analysis of effective rainfall. Book Chapter in Recent Advances in Continuum Mechanics, Hydrology and Ecology (editor Mladenov), 2013, pp. 133-139.
- Sojitra MA, Purohit RC, Pandya PA. Comparative study of daily rainfall forecasting models using adaptive-neuro fuzzy inference system (ANFIS). Current World Environment 2015; 10(2): 529-536.
- Shamshirband S, Gocic M, Petkovic D, Saboohi H, Herawan T, Kiah MLM, Akib S. Soft-computing methodologies for precipitation estimation: A case study. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2015; 8(3): 1353-1358.
- El-Shafie A, Jaafer O, Akrami SA. Adaptive neuro-fuzzy Ä°nference system based model for rainfall forecasting in Klang River, Malaysia. International Journal of Physical Sciences 2011; 6(12): 2875-2888.
- Akrami SA, Nourani V, Hakim SJS. Development of nonlinear model based on wavelet-ANFIS for rainfall forecasting at Klang Gates Dam. Water Resources Management 2014; 28(10): 2999-3018.
- Jang JSR. ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man, and Cybernetics 1993; 23(3): 665-685.
- Gemici E, Ardiclioglu M, Kocabas F. Modeling of discharge in rivers by artificial neural network. Erciyes Universitesi Fen Bilimleri Enstitusu Dergisi 2013; 29(2): 135-143.
- Firat M. Modeling of watershed using adaptive neuro-fuzzy inference system approach. Pamukkale Universitesi, Fen Bilimleri Enstitusu, PhD thesis, p. 204, 2007.
- Proconsulate of Isparta, Turkey. About Isparta, Retrieved from http://www.isparta.gov.tr/isparta-hakkinda at 2017.