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

Journal of Water and Land Development

Shashi MathurIndian Institute of Technology, Department of Civil Engineering, Hauz Khas, New Delhi 110 016, IndiaOther articles by this author:De Gruyter OnlineGoogle Scholar¹  Basant YadavCorresponding authorIndian Institute of Technology, Department of Civil Engineering, Hauz Khas, New Delhi 110 016, IndiaEmailOther articles by this author:De Gruyter OnlineGoogle Scholar¹  Jan AdamowskiMcGill University, Faculty of Agricultural and Environmental Sciences, Department of Bioresource Engineering, Quebec, Canada, H9X 3V9, CanadaEmailOther articles by this author:De Gruyter OnlineGoogle Scholar²  Sudheer ChMinistry of Environment, Forest and Climate Change, IndiaEmailOther articles by this author:De Gruyter OnlineGoogle Scholar³ 

[1] Indian Institute of Technology, Department of Civil Engineering, Hauz Khas, New Delhi 110 016, India;McGill University, Faculty of Agricultural and Environmental Sciences, Department of Bioresource Engineering, Quebec, Canada, H9X 3V9, Canada;Ministry of Environment, Forest and Climate Change, India

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

DOI  :  10.1515/jwld-2017-0012

学科分类：农业科学（综合）

来源: Instytut Technologiczno-Przyrodniczego / Institute of Technology and Life Sciences

【 摘 要 】

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.

【 授权许可】

Unknown