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A platform for research: civil engineering, architecture and urbanism
Streamflow Intervals Prediction Using Coupled Autoregressive Conditionally Heteroscedastic With Bootstrap Model
ABSTRACT Streamflow (Qflow) process is one of the complex stochastic processes in the hydrology cycle owing to its associated non‐linearity and non‐stationarity characteristics. It is an essential hydrological process to address the complex time series nonlinear phenomena. In this research, a novel approach was proposed by integrating an autoregressive conditionally heteroscedastic (ARCH) method with bootstrap model to predict future Qflow intervals. For this purpose, two Qflow series located at the Eastern Black Sea basin (Turkey) were subjected to the application of the proposed methodology. Among other regression and machine learning (ML) models, which are suitable for Qflow modeling, the autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), and artificial neural network (ANN) were selected for modeling validation in this study. A group of three numerical metrics and graphical presentations were used for the modeling evaluation and assessment. The proposed ARCH approach performed a superior mathematical model to address the Qflow interval prediction. Remarkable prediction accuracy was shown against the benchmark models. Overall, the approach of coupling the bootstrap procedure with the ARCH model exhibited a robust modeling strategy for predicting Qflow intervals suggested as a new analysis tool.
Streamflow Intervals Prediction Using Coupled Autoregressive Conditionally Heteroscedastic With Bootstrap Model
ABSTRACT Streamflow (Qflow) process is one of the complex stochastic processes in the hydrology cycle owing to its associated non‐linearity and non‐stationarity characteristics. It is an essential hydrological process to address the complex time series nonlinear phenomena. In this research, a novel approach was proposed by integrating an autoregressive conditionally heteroscedastic (ARCH) method with bootstrap model to predict future Qflow intervals. For this purpose, two Qflow series located at the Eastern Black Sea basin (Turkey) were subjected to the application of the proposed methodology. Among other regression and machine learning (ML) models, which are suitable for Qflow modeling, the autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), and artificial neural network (ANN) were selected for modeling validation in this study. A group of three numerical metrics and graphical presentations were used for the modeling evaluation and assessment. The proposed ARCH approach performed a superior mathematical model to address the Qflow interval prediction. Remarkable prediction accuracy was shown against the benchmark models. Overall, the approach of coupling the bootstrap procedure with the ARCH model exhibited a robust modeling strategy for predicting Qflow intervals suggested as a new analysis tool.
Streamflow Intervals Prediction Using Coupled Autoregressive Conditionally Heteroscedastic With Bootstrap Model
Bugrayhan Bickici (author) / Beste Hamiye Beyaztas (author) / Zaher Mundher Yaseen (author) / Ufuk Beyaztas (author) / Ercan Kahya (author)
2025
Article (Journal)
Electronic Resource
Unknown
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Evaluating an Autoregressive Model for Streamflow Forecasting
| British Library Conference Proceedings | 1994