Comparative Analysis of Artificial Intelligence Based Methods for Prediction of Precipitation. Case Study: North Cyprus
| Autor: | Vahid Nourani, Fahreddin Sadikoglu, Selin Uzelaltinbulat |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Adaptive neuro fuzzy inference system
010504 meteorology & atmospheric sciences Flood myth Artificial neural network business.industry Computer science 0208 environmental biotechnology 02 engineering and technology 01 natural sciences 020801 environmental engineering Support vector machine Water resources Artificial intelligence Precipitation business 0105 earth and related environmental sciences |
| Zdroj: | 13th International Conference on Theory and Application of Fuzzy Systems and Soft Computing — ICAFS-2018 ISBN: 9783030041632 |
| DOI: | 10.1007/978-3-030-04164-9_11 |
| Popis: | Prediction of precipitation is important for design, management of water resources systems, planning, flood predicting and hydrological events. This study aimed to compare the performance of three different “Artificial Intelligence (AI)” techniques which are “Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Inference System (ANFIS) and Least Square Support Vector Machine (LSSVM)” to estimate monthly rainfall in Kyrenia Station of Turkish Republic of Northern Cyprus (TRNC). The monthly data covering ten years’ precipitation were used for the predictions. The comparative results showed that the LSSVM model can cause a bit more reliable performance in regard to ANN and ANFIS. |
| Databáze: | OpenAIRE |
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