Monthly streamflow forecasting using neuro-wavelet techniques and input analysis
نویسندگان
چکیده
منابع مشابه
Uncertainty Analysis of Monthly Streamflow Forecasting
Streamflow forecasting is an important factor in water resources planning and management. In this study Feed Forward Artificial Neural Network (FFANN) was used for monthly streamflow forecasting. Three scenarios were considered for modeling. Principal Component Analysis (PCA) is used for reducing the model architecture complexity and input data reduction. Twelve statistical criteria were used t...
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Bureau of Economic Geology, Jackson School of Geosciences, University of Texas Austin, Austin, TX 78713, United States Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL 32816, United States Key Laboratory for Agro-Ecological Processes in Subtropical Region, Institute of Subtropical, Agriculture, Chinese Academy of Sciences, Changsha, Ch...
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Accurate and reliable streamflow forecasting plays an important role in various aspects of water resources management such as reservoir scheduling and water supply. This paper shows the development of a novel hybrid model for streamflow forecasting and demonstrates its efficiency. In the proposed hybrid model for streamflow forecasting, the empirical wavelet transform (EWT) is firstly employed ...
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[1] Seasonal forecasting of streamflow provides many benefits to society, by improving our ability to plan and adapt to changing water supplies. A common approach to developing these forecasts is to use statistical methods that link a set of predictors representing climate state as it relates to historical streamflow, and then using this model to project streamflow one or more seasons in advanc...
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In this research, a hybrid wavelet-artificial neural network (WANN) and a geostatistical method were proposed for spatiotemporal prediction of the groundwater level (GWL) for one month ahead. For this purpose, monthly observed time series of GWL were collected from September 2005 to April 2014 in 10 piezometers around Mashhad City in the Northeast of Iran. In temporal forecasting, an artificial...
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ژورنال
عنوان ژورنال: Hydrological Sciences Journal
سال: 2018
ISSN: 0262-6667,2150-3435
DOI: 10.1080/02626667.2018.1552788