Using support vector regression to predict direct runoff, base flow and total flow in a mountainous watershed with limited data in Uttaranchal, India

• Autorzy: Adamowski J.

Warianty tytułu

PL

Wykorzystanie wektorów wspierających w zależnościach regresyjnych do prognozowania odpływu bezpośredniego i całkowitego w zlewniach górskich przy ograniczonej liczbie danych w zlewni Uttaranchal, Indie

• Języki publikacji: EN

Abstrakty

EN

In the ecologically sensitive Himalayan region, land transformations and utilization of natural resources have modified water flow patterns. To ascertain future sustainable water supply it is necessary to predict water flow from the watersheds as affected by rainfall and morphological parameters. Although such predictions may be made using available process-based models, in mountainous and hilly areas it is extremely difficult to determine the numerous parameters needed to run such models, thus limiting their applicability. Artificial intelligence (AI) based models are a possible alternative in such circumstances. In this study an AI technique, support vector machines (SVM), was used for modeling the rainfall-runoff relationship from three hilly watersheds in the state of Uttaranchal, India. Different SVM models were developed to predict direct runoff, base flow, and total flow based on the daily rainfall, runoff, and morphological parameters collected from each watershed. The results confirm the potential of SVM models in the prediction of runoff, base flow, and total flow in hilly areas.

PL

Na obszarach wrażliwych, jakim są Himalaje, zmiany w wykorzystaniu powierzchni obszarów górskich oraz zasobów przyrodniczych modyfikują warunki kształtowania się odpływu. Dla zrównoważonego gospodarowania zasobami wodnymi w tym regionie koniecznym jest prognozowanie odpływu ze zlewni na podstawie opadu i warunków morfologicznych obszaru. Prognozowanie odpływu przy wykorzystaniu modeli deterministycznych jest dosyć trudne i ograniczone ze względu na trudności w identyfi kacji wielu parametrów. W pracy zastosowano modele wykorzystujące techniki sztucznej inteligencji (AI) za pomocą wektorów wspierających (SVM) jako alternatywę do modelowania zależności opadodpływ dla trzech zlewni górskich w stanie Uttaranchal, Indie. Wyniki zawarte w pracy potwierdzają możliwość wykorzystanie metody SVM do prognozowania charakterystycznych wielkości odpływu w warunkach górskich.

Słowa kluczowe

EN

India; direct runoff; total outflow; support vector regression; mountain catchment; watershed

• Wydawca: -
• Czasopismo: Annals of Warsaw University of Life Sciences - SGGW. Land Reclamation
• Rocznik: 2013
• Tom: 45
• Numer: 1
• Opis fizyczny: p.71-83,fig.,ref.

Twórcy

autor

Adamowski J.

• Department of Bioresource Engineering, Faculty of Agricultural and Environmental Sciences, McGill University, 21111 Lakeshore Road, Ste-Anne de Bellevue, Que., H9X 3V9 Canada

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