Streamflows forecast with Neural Networks

Stream flow forecasting can be an appropriate indicator in estimating future conditions for water resources management. The present study aimed to compare the efficiency of Support Vector Machine (SVM), Adaptive Neural Fuzzy Inference Systems (ANFIS) and conceptual hydrological model of MIKE11/NAM in simulating the daily stream flow. The studied area is Eskandari basin located in Iran. For this purpose, a ten-year period (1999-2009) of daily data including rainfall, runoff, temperature and evaporation were used. Furthermore, the performances of the models in flow simulation were investigated using statistical indicators of correlation coefficient (R 2 ), Root Mean Square Error (RMSE) and the Nash-Sutcliffe (NS) coefficient. The results showed that every three models possess an appropriate performance and efficiency in the studied area. During testing (verification) period, SVM with the highest correlation coefficient (R 2 =0.99) and lowest RMSE equal to (RMSE=2.13 s m / 3 ), had a better performance than ANFIS model (R 2 =0.82, RMSE=3.21 s m / 3 ) and NAM model (R 2 =0.75, RMSE=3.48 s m / 3 ). In addition, Nash-Sutcliffe coefficient for SVM, ANFIS and NAM models were 0.99, 0.79 and 0.70, respectively.

This study provides a unique opportunity to analyze the issue of flow forecast based on the soil and water assessment tool (SWAT) and artificial neural network (ANN) models. In last two decades, the ANNs have been extensively applied to various water resources system problems. In this study, the ANNs were applied to the daily flow of the Pracana basin in Portugal. The comparison of ANN models and a pro- cess-based model SWAT was established based on their prediction accuracy. The ANN model was found to be more successful than the SWAT in relation to better forecast of peak flow. Nevertheless the SWAT model results revealed a better value of mean squared error. The results of this study, in general, showed that ANNs can be powerful tools in daily flow forecasts.

Keywords: Flow, Forecast, Artificial neural networks, SWAT, Portugal

Since fresh water is limited while agricultural and human water demands are continuously increasing, optimal prediction and management of streamflows as a source of fresh water is crucially important. This study investigates and demonstrates how data preprocessing and data mining techniques would improve the accuracy of streamflow predictive models. Based on easily accessible Snow Telemetry data (SNOTEL), four streamflow prediction models – autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs), a hybrid-model of ANN and ARIMA (ANN-ARIMA), and an adaptive neuro fuzzy inference system (ANFIS) – were developed and utilized in a streamflow prediction process on Elephant Butte Reservoir. Utilizing the statistical correlation analysis and the extracting importance degrees of predic-tors led to efficiently select the most effective predictors for daily and monthly streamflow to Elephant Butte Reservoir. For the daily prediction time step, by preprocessing the historical data and extracting and utilizing the extracted climate variability indices through data mining techniques, the ANFIS model achieved a superior streamflow prediction performance for Elephant Butte Reservoir compared to the other three evaluated prediction models. Additionally, for predicting monthly streamflow to the Elephant Butte Reservoir, ANFIS showed significantly higher accuracy than the ANNs. As an optimal application of the developed predictive expert systems, successful integrating the prediction models in integrated reservoir operations balanced the need for a reliable supply of irrigation water against losses through evaporation. The optimal operation plan significantly minimizes the total evaporation loss from both reservoirs by providing the optimal storage levels in both reservoirs. This study provides the conceptual procedures of non-seasonal (ARIMA) model, and since the model is univariate, it demonstrates a strongly-reliable inflow prediction when existing information is limited to streamflow data as a predictor.

This study provides a unique opportunity to analyze the issue of flow forecast based on the soil and water assessment tool (SWAT) and artificial neural network (ANN) models. In last two decades, the ANNs have been extensively applied to various water resources system problems. In this study, the ANNs were applied to the daily flow of the Pracana basin in Portugal. The comparison of ANN models and a process-based model SWAT was established based on their prediction accuracy. The ANN model was found to be more successful than the SWAT in relation to better forecast of peak flow. Nevertheless the SWAT model results revealed a better value of mean squared error. The results of this study, in general, showed that ANNs can be powerful tools in daily flow forecasts.

This study provides a unique opportunity to analyze the issue of flow forecast based on the soil and water assessment tool (SWAT) and artificial neural network (ANN) models. In last two decades, the ANNs have been extensively applied to various water resources system problems. In this study, the ANNs were applied to the daily flow of the Pracana basin in Portugal. The comparison of ANN models and a process-based model SWAT was established based on their prediction accuracy. The ANN model was found to be more successful than the SWAT in relation to better forecast of peak flow. Nevertheless the SWAT model results revealed a better value of mean squared error. The results of this study, in general, showed that ANNs can be powerful tools in daily flow forecasts.

This study provides a unique opportunity to analyze the issue of flow forecast based on the soil and water assessment tool (SWAT) and artificial neural network (ANN) models. In last two decades, the ANNs have been extensively applied to various water resources system problems. In this study, the ANNs were applied to the daily flow of the Pracana basin in Portugal. The comparison of ANN models and a process-based model SWAT was established based on their prediction accuracy. The ANN model was found to be more successful than the SWAT in relation to better forecast of peak flow. Nevertheless the SWAT model results revealed a better value of mean squared error. The results of this study, in general, showed that ANNs can be powerful tools in daily flow forecasts.

The dependence of agricultural production on water resources is a known fact. Therefore, understanding hydrological processes and events in agricultural production form the basis of effective and reliable management of water resources. Many traditional methods used for the analysis of time-dependent variables in hydrology and meteorology assume linear relationships between these variables. However, the temporal changes of these parameters are quite complex and therefore cannot be easily modeled by conventional estimation methods. Artificial neural networks (ANNs), on the other hand, allow the analysis of nonlinear relationships or processes whose statistical or mathematical calculations cannot be determined in such systems. ANNs have been accepted as a successful model in multidimensional research involving dynamic processes in the field of hydrology.

This study, it is aimed at the modeling of stream flows in Bektas Creek. With the aim of modeling, daily meteorological parameters (precipitation, temperature, sunbathing time, relative humidity) measured in Kangal Region and one day delayed flows were used. Streamflow forecasts are simulated with the Generalized Regression Neural Network (GRNN). To reveal the difference of the GRNN model from other ANNs, the same data were also used in the feed-forward neural network (FFNN) model. Model performances were evaluated taking into account the Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Determination coefficient (R).

Keywords: Meteorological Parameters, Streamflow Prediction, Generalized Regression Neural Network, Feed-Forward Neural Network.