Assessing the suitability of extreme learning machines (ELM) for groundwater level prediction

• Autorzy: Yadav B. , Ch S. , Mathur S. , Adamowski J.

Identyfikatory

DOI

10.1515/jwld-2017-0012

Warianty tytułu

PL

Ocena zdolności ekstremalnych maszyn uczących (ELM) do przewidywania poziomu wód gruntowych

• Języki publikacji: EN

Abstrakty

EN

Fluctuation of groundwater levels around the world is an important theme in hydrological research. Rising water demand, faulty irrigation practices, mismanagement of soil and uncontrolled exploitation of aquifers are some of the reasons why groundwater levels are fluctuating. In order to effectively manage groundwater resources, it is important to have accurate readings and forecasts of groundwater levels. Due to the uncertain and complex nature of groundwater systems, the development of soft computing techniques (data-driven models) in the field of hydrology has significant potential. This study employs two soft computing techniques, namely, extreme learning machine (ELM) and support vector machine (SVM) to forecast groundwater levels at two observation wells located in Canada. A monthly data set of eight years from 2006 to 2014 consisting of both hydrological and meteorological parameters (rainfall, temperature, evapotranspiration and groundwater level) was used for the comparative study of the models. These variables were used in various combinations for univariate and multivariate analysis of the models. The study demonstrates that the proposed ELM model has better forecasting ability compared to the SVM model for monthly groundwater level forecasting.

PL

Na całym świecie fluktuacje poziomów wód gruntowych stanowią ważny temat badań hydrologicznych. Rosnące potrzeby wodne, błędne praktyki irygacyjne, niewłaściwa gospodarka glebowa i niekontrolowana eksploatacja poziomów wodonośnych są powodami, dla których poziom wód gruntowych podlega fluktuacjom. Dla skutecznego zarządzania zasobami wód gruntowych istotne jest dysponowanie dokładnymi zapiskami i zdolność prognozowania poziomu tych wód. Rozwój technik komputerowych (modele wykorzystujące dane) w dziedzinie hydrologii ma istotny potencjał z powodu niepewnego i złożonego charakteru systemów wód gruntowych. W prezentowanych badaniach wykorzystano dwie techniki komputerowe: maszynę uczenia ekstremalnego (ELM) i maszynę wektorów nośnych (SVM – ang. support vector machine) do przewidywania poziomów wód gruntowych w dwóch studzienkach obserwacyjnych w Kanadzie. Do porównawczych badań modeli wykorzystano zestaw danych miesięcznych z ośmiu lat (2006–2014), składający się z danych hydrologicznych i meteorologicznych (opady, temperatura, ewapotranspiracja, poziom wody). Wymienione zmienne zastosowano w rozmaitych kombinacjach do jedno- i wieloparametrycznej analizy modeli. Wyniki dowodzą, że model ELM ma lepsze zdolności przewidywania miesięcznych poziomów wód gruntowych w porównaniu z modelem SVM.

Słowa kluczowe

EN

extreme learning machine (ELM); forecasting; groundwater level; support vector machine (SVM); water resource management

PL

maszyna uczenia ekstremalnego (ELM); maszyna wektorów nośnych SVM; poziom wód gruntowych; prognozowanie; zarządzanie zasobami wodnymi

• Wydawca: Wydawnictwo ITP
• Czasopismo: Journal of Water and Land Development
• Rocznik: 2017
• Tom: no. 32
• Strony: 103--112
• Opis fizyczny: Bibliogr. 53 poz., rys., tab.

Twórcy

autor

Yadav B.

• Indian Institute of Technology, Department of Civil Engineering, Hauz Khas, New Delhi 110 016, India

autor

Ch S.

• Ministry of Environment, Forest and Climate Change

autor

Mathur S.

• Indian Institute of Technology, Department of Civil Engineering, Hauz Khas, New Delhi 110 016, India

autor

Adamowski J.

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

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Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).