Evaluating Performances of LSTM, SVM, GPR, and RF for Drought Prediction in Norway: A Wavelet Decomposition Approach on Regional Forecasting
Yükleniyor...
Dosyalar
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Multidisciplinary Digital Publishing Institute (MDPI)
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
A serious natural disaster that poses a threat to people and their living spaces is drought, which is difficult to notice at first and can quickly spread to wide areas through subtle progression. Numerous methods are being explored to identify, prevent, and mitigate drought, and distinct metrics have been developed. In order to contribute to the research on measures to be taken against drought, the Standard Precipitation Evaporation Index (SPEI), one of the drought indices that has been developed and accepted in recent years and includes a more comprehensive drought definition, was chosen in this study. Machine learning and deep learning algorithms, including support vector machine (SVM), random forest (RF), long short-term memory (LSTM), and Gaussian process regression (GPR), were used to model the droughts in six regions of Norway: Bodø, Karasjok, Oslo, Tromsø, Trondheim, and Vadsø. Four distinct model architectures were employed for this goal, and as a novel approach, the models’ output was enhanced by using discrete wavelet decomposition/transformation (WT). The model outputs were evaluated using the correlation coefficient (r), Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE) as performance evaluation criteria. When the findings were analyzed, the GPR model (W-GPR), which was acquired after WT, typically produced the best results. Furthermore, it was discovered that, out of all the recognized models, M04 had the most effective model structure.
Açıklama
Anahtar Kelimeler
Global Change, Extreme Weather, Machine Learning, Deep Learning, Risk Assessment, SPEI
Kaynak
Water (Switzerland)
WoS Q Değeri
Scopus Q Değeri
Q1
Cilt
16
Sayı
23
