Forecasting Of Stream Flow Usıng Singular Spectrum Analysis And Long-Short Term Memory Networks

Abstract

Stream flow estimation has an important role in the planning and management of water resources. Accurate estimation of stream flow data, that is characterised ny non-linear and non-stationary, is a challenging problem. In recent years, data-based techniques have been used extensively in forecasting of stream flow. In this study, stream flow estimation was made with the Long-Short Term Memory (LSTM) Networks from Deep Neural Networks, which were used as popular. Subband data was obtained by using Single Spectrum Analysis (SSA), which plays an important role in the analysis of time series in order to increase the forecast performance. As a result of estimation of SSA subband data of stream flow forecasting data with LSTM network, one ahead forecasting study was carried out. Using this proposed SSA-LSTM model, high performance forecasted data was obtained with 0.0021 Mean Square Error (MSE) value, 0.0361 Mean Absolute Error (MAE) value and 0.9710 Correlation (R) coefficient value.

Keywords

• Stream flow forecasting,Deep Neural Networks,Long-Short Term Neural Networks,Singular Spectrum Analysis

Tekil Spektrum Analizi ve Uzun-Kısa Süreli Bellek Ağları ile Nehir Akım Tahmini

Abstract

Su yapılarının planlanması ve yönetiminde nehir akım tahminleri önemli bir yere sahiptir. Lineer olmayan ve durağan olmayan karaktere sahip nehir akım verilerinin doğru tahmini zorlu bir problemdir. Son yıllarda veri tabanlı teknikler, nehir akım problemlerinde yoğun olarak kullanılmaktadır. Önerilen çalışmada popüler olarak kullanılmaya başlanan Derin Sinir Ağlarından Uzun – Kısa Süreli Bellek (Long-Short Term Memory, LSTM) Ağları ile nehir akım tahmini gerçekleştirilmiştir. Tahmin performansını artırmak üzere zaman serilerinin analizinde önemli bir yer tutan Tekil Spektrum Analizi (TSA) kullanılarak alt bant verileri elde edilmiştir. Nehir akım tahmin verisine ait TSA alt bant verilerinin LSTM ağları ile tahmini sonucu bir ileri adım tahmin çalışması gerçekleştirilmiştir. Önerilen TSA-LSTM modeli kullanılarak 0.0021 Ortalama Karesel Hata (MSE) değeri, 0.0361 Ortalama Mutlak Hata (MAE) değeri ve 0.9710 Korelasyon (R) değeri ile yüksek performanslı tahmin verisi elde edilmiştir.

Keywords

• Nehir akım tahmini,Derin Sinir Ağları,Kısa-Uzun Bellek Ağları,Tekil Spektrum Analizi

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