Drought forecasting using new machine learning methods

• Autorzy: Belayneh A. , Adamowski J.

Warianty tytułu

PL

Prognozowanie suszy z wykorzystaniem automatycznych samouczących się metod

• Języki publikacji: EN

Abstrakty

EN

In order to have effective agricultural production the impacts of drought must be mitigated. An important aspect of mitigating the impacts of drought is an effective method of forecasting future drought events. In this study, three methods of forecasting short-term drought for short lead times are explored in the Awash River Basin of Ethiopia. The Standardized Precipitation Index (SPI) was the drought index chosen to represent drought in the basin. The following machine learning techniques were explored in this study: artificial neural networks (ANNs), support vector regression (SVR), and coupled wavelet-ANNs, which pre-process input data using wavelet analysis (WA). The forecast results of all three methods were compared using two performance measures (RMSE and R²). The forecast results of this study indicate that the coupled wavelet neural network (WA-ANN) models were the most accurate models for forecasting SPI 3 (3-month SPI) and SPI 6 (6-month SPI) values over lead times of 1 and 3 months in the Awash River Basin in Ethiopia.

PL

Efektywna gospodarka rolna, uzyskanie dużych plonów wymaga prowadzenia działań w celu ograniczenia niekorzystnego wpływu suszy. Ważnym czynnikiem ograniczania skutków suszy jest efektywna i możliwie precyzyjna metoda przewidywania suszy. W artykule przedstawiono trzy metody prognozowania suszy w okresach krótkoterminowych, które zostały zastosowane w zlewni rzeki Awash w Etiopii. Do kwantyfikacji suszy zastosowano wskaźnik standaryzowanego opadu (SPI). Zastosowane zostały następujące samouczące się metody: sztuczne sieci neuronowe (ANNs), regresje wektorowe (SVR) oraz połączenie ANNs z analizą falową (WA), którą zastosowano do wstępnej obróbki danych. Ocenę prognozy dokonano stosując dwa mierniki – RMSE i R². Na podstawie obliczeń stwierdzono, że połączona metoda analizy falowej z siecią neutronową (WA-ANN) jest najdokładniejsza w prognozowaniu wartości SPI 3 (3-miesięczne SPI) i SPI 6 (6-miesięczne SPI) w okresie 1 i 3 miesiące naprzód w zlewni rzeki Awash.

Słowa kluczowe

EN

agriculture; artificial neural networks; drought forecast; SPI; support vector regression; wavelet analysis

PL

analiza falowa; prognoza suszy; regresja wektorowa; rolnictwo; SPI; sztuczna sieć neuronowa

• Wydawca: Wydawnictwo ITP
• Czasopismo: Journal of Water and Land Development
• Rocznik: 2013
• Tom: no. 18 [I-VI]
• Strony: 3--12
• Opis fizyczny: Bibliogr. 32 poz., rys., tab.

Twórcy

autor

Belayneh A.

• Department of Bioresource Engineering, Faculty of Agricultural and Environmental Sciences, McGill University, Quebec, Canada, H9X 3V9

autor

Adamowski J.

• Department of Bioresource Engineering, Faculty of Agricultural and Environmental Sciences, McGill University, Quebec, Canada, H9X 3V9

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