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Comparative Analysis of Artificial Intelligence Based Methods for Prediction of Precipitation. Case Study: North Cyprus

  • Selin UzelaltinbulatEmail author
  • Fahreddin Sadikoglu
  • Vahid Nourani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 896)

Abstract

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.

Keywords

Precipitation ANFIS LSSVM ANN Prediction Rainfall station 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Selin Uzelaltinbulat
    • 1
    Email author
  • Fahreddin Sadikoglu
    • 2
  • Vahid Nourani
    • 3
    • 4
  1. 1.Department of Computer Engineering, Faculty of EngineeringNear East UniversityNicosia, Mersin 10Turkey
  2. 2.Department of Electrical and Electronic Engineering, Faculty of EngineeringNear East UniversityNicosia, Mersin 10Turkey
  3. 3.Department of Civil Engineering, Faculty of EngineeringNear East UniversityNicosia, Mersin 10Turkey
  4. 4.Department of Water Resources Engineering, Faculty of Civil EngineeringUniversity of TabrizTabrizIran

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