Hindcasting and Forecasting Total Suspended Sediment Concentrations Using a NARX Neural Network
| Autor: | Vladimir J. Alarcon |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Watershed
010504 meteorology & atmospheric sciences Valparaiso 0208 environmental biotechnology Geography Planning and Development TJ807-830 forecasting 02 engineering and technology Land cover STREAMS Management Monitoring Policy and Law TD194-195 01 natural sciences Renewable energy sources Hindcast GE1-350 Precipitation Chile 0105 earth and related environmental sciences watershed Hydrology Nonlinear autoregressive exogenous model Baseflow Environmental effects of industries and plants Renewable Energy Sustainability and the Environment Sediment total suspended sediment 020801 environmental engineering Environmental sciences NARX Environmental science |
| Zdroj: | Sustainability Volume 13 Issue 1 Sustainability, Vol 13, Iss 363, p 363 (2021) |
| ISSN: | 2071-1050 |
| DOI: | 10.3390/su13010363 |
| Popis: | Estimating and forecasting suspended sediments concentrations in streams constitutes a valuable asset for sustainable land management. This research presents the development of a non-linear autoregressive exogenous neural network (NARX) for forecasting sediment concentrations at the exit of Francia Creek watershed (Valparaiso, Chile). Details are presented on input data selection, data splitting, selection of model architecture, determination of model structure, NARX training (optimization of model parameters), and model validation (hindcasting and forecasting). The study explored if the developed artificial neural network model is valid for forecasting daily suspended sediment concentrations for a complete year, capturing seasonal trends, and maximum and baseflow concentrations. Francia Creek watershed covers approximately 3.24 km2. Land cover within the catchment consists mainly of native and exotic vegetation, eroded soil, and urban areas. Input data consisting of precipitation and stream flow time-series were fed to a NARX network for forecasting daily suspended sediments (SST) concentrations for years 2013&ndash 2014, and hindcasting for years 2008&ndash 2010. Training of the network was performed with daily SST, precipitation, and flow data from years 2012 and 2013. The resulting NARX net consisted of an open-loop, 12-node hidden layer, 100 iterations, using Bayesian regularization backpropagation. Hindcasting of daily and monthly SST concentrations for years 2008 through 2010 was successful. Daily SST concentrations for years 2013 and 2014 were forecasted successfully for baseflow conditions (R2 = 0.73, NS = 0.71, and Kling-Gupta efficiency index (K-G) = 0.84). Forecasting daily SST concentrations for year 2014 was within acceptable statistical fit and error margins (R2 = 0.53, NS = 0.47, K-G = 0.60, d = 0.82). Forecasting of monthly maximum SST concentrations for the two-year period (2013 and 2014) was also successful (R2 = 0.69, NS = 0.60, K-G = 0.54, d = 0.84). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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