Makine Öğrenimi Kullanarak Aylık Akarsu Akışı Tahmini

Year 2020, Volume: 13 Issue: 3, 1242 - 1251, 31.12.2020

Fatih Tosunoğlu Sinan Hanay Emre Çintaş Barış Özyer

https://doi.org/10.18185/erzifbed.780477

Cited By: 3

Abstract

Nehir akımı tahmini herhangi bir havzadaki su kaynaklarının planlanması, dizaynı ve yönetiminde oldukça önemli rol oynamaktadır. Doğru nehir akımı tahmini su kaynakları sistemlerinin teknik ve ekonomik açıdan daha yararlı tasarlanmasını sağlamaktadır. Bu çalışmada, farklı makine öğretisi algoritmaları Çoruh havzasındaki aylık nehir akımlarının modellenmesinde kullanılmıştır. Bu amaç için, Destek Vektör Makineleri (SVM), Adaptif Yükseltme (AdaBoost), K En Yakın Komşular (KNN) ve Rassal Ormanlar ve makine öğretisi algoritmaları kullanılmış ve karşılaştırılmıştır. Kullanılan modellere ait test skoru sonuçlarına göre Random Forest based modeli diğer modellere göre daha iyi sonuç vermiştir.

Keywords

Adaptif yükseltme, destek vektör makineleri, KNN, makine öğrenmesi

Monthly Streamflow Forecasting Using Machine Learning

Abstract

Streamflow forecasting holds a vital role in planning, design, and management of basin water resources. Accurate streamflow forecast provides a more efficient design of water resources systems technically and economically. In this study, various machine learning algorithms were used to model monthly streamflow data in the Coruh river basin, Turkey. For modeling, Support Vector Machines (SVM), Adaptive Boosting (AdaBoost), K-Nearest Neighbours (KNN) and Random Forest algorithms were considered and compared. Based on the test scores of the considered models with the hyperparameters, Random Forest based model outperforms all other models.

Keywords

Adaptive boosting, KNN, machine learning, support vector machines

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