Possibility of GPS precipitable water vapour for reservoir inflow forecasting

• Autorzy: Uang-aree P. , Kingpaiboon S.

Identyfikatory

DOI

10.2478/jwld-2018-0016

Warianty tytułu

PL

Możliwość prognozowania dopływu do zbiornika na podstawie danych GPS o zawartości pary wodnej

• Języki publikacji: EN

Abstrakty

EN

We investigated the possibility of using GPS precipitable water vapour (GPS-PWV) for forecasting reservoir inflow. The correlations between monthly GPS-PWV and the inflow of two reservoirs were examined and the relationship tested, using a group method of data handling (GMDH) type neural network algorithm. The daily and monthly reservoir inflows were directly proportional to daily and monthly GPS-PWV trends. Peak reservoir inflow, however, shifted from the peak averages for GPS-PWV. A strong relationship between GPS-PWV and inflow was confirmed by high R² values, high coefficients of correlation, and acceptable mean absolute errors (MAE) of both the daily and monthly models. The Ubon Ratana reservoir model had a monthly MAE of 54.19∙10⁶ m³ and a daily MAE of 5.40∙10⁶ m³. By comparison, the Lumpow reservoir model had a monthly MAE of 25.65∙10⁶ m³ and a daily MAE of 2.62∙10⁶ m³. The models using GPS-PWV as input data responded to extreme inflow better than traditional variables such that reservoir inflow could be predicted using GPS-PWV without using actual inflow and rainfall data. GPS-PWV, thus, represents a helpful tool for regional and national water management. Further research including more reservoirs is needed to confirm this preliminary finding.

PL

W pracy przedstawiono wyniki badań możliwości użycia danych GPS o zawartości pary wodnej (GPS- -PWV) do prognozowania dopływu do zbiornika. Analizowano korelacje między miesięczną wartością GPS- -PWV a dopływem do dwóch zbiorników; zależność testowano, stosując algorytm sieci neuronowej, zwany metodą grupowania argumentów (GMDH). Dobowe i miesięczne dopływy do zbiorników były proporcjonalne do dobowych i miesięcznych trendów GPS-PWV. Maksymalny dopływ odbiegał jednak od maksymalnych średnich GPS-PWV. Silna zależność między GPS-PWV a dopływem została potwierdzona dużymi wartościami R², wysokim współczynnikiem korelacji i akceptowalnym średnim błędem bezwzględnym (MAE) zarówno w modelu dobowym, jak i miesięcznym. W modelu dla zbiornika Ubon Ratana miesięczny błąd bezwzględny wynosił 54,19∙10⁶ m³ a dobowy – 5,40∙10⁶ m³. Dla porównania w modelu dla zbiornika Lumpow wartość miesięczna MAE wynosiła 25,65∙10⁶ m³, a dobowa 2,62∙10⁶ m³. Modele z wykorzystaniem GPS-PWV jako danych wejściowych reagowały lepiej niż tradycyjne zmienne na dopływ ekstremalny i dlatego dopływ do zbiornika można przewidzieć bez znajomości rzeczywistego dopływu i danych opadowych. GPS-PWV jest więc pomocnym narzędziem w regionalnej i narodowej gospodarce wodnej. Potrzebne są dalsze badania obejmujące większą liczbę zbiorników, aby potwierdzić prezentowane wyniki wstępne.

Słowa kluczowe

EN

GMDH; hydrology; PWV; water management; watershed

PL

GMDH; gospodarka wodna; hydrologia; PWV; zlewnia

• Wydawca: Wydawnictwo ITP
• Czasopismo: Journal of Water and Land Development
• Rocznik: 2018
• Tom: no. 36
• Strony: 161--171
• Opis fizyczny: Bibliogr. 47 poz., rys., tab.

Twórcy

autor

Uang-aree P.

• Sakon Nakhon Rajabhat University, Faculty of Science and Technology, Sakon Nakhon, Thailand

autor

Kingpaiboon S.

• Khon Kaen University, Department of Agricultural Engineering, Agricultural Machinery and Postharvest Technology Center, 123 Mitraphab Road, Nai-Muang, Muang District, 40002 Khon Kaen, Thailand

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).