Abstract
Every year, many basins in Thailand face the perennial droughts and floods that lead to the great impact on agricultural segments. In order to reduce the impact, water management would be applied to the critical basin, for instance, Yom River basin. An importing task of management is quantitative prediction of water that is stated by water level. This study proposes the hybridized forecasting models between the stochastic approaches, seasonal autoregressive integrated moving average (SARIMA) models and machine learning approach, artificial neural network (ANN). The proposed hybrid model is called seasonal autoregressive integrated moving average and artificial neural network or SARIMANN model for average monthly water level (AMWL) time series of Yom River basin. The study period is from April 2007 to March 2020, over thirteen hydrological years. The forecasting performance is the minimum values of root mean squared error (RMSE) and mean absolute percentage error (MAPE) between SARIMA models, ANN models, and SARIMANN models. Results indicated that: The three models reveal the similarity of RMSE and MAPE for both four water level measurement stations for wet and dry seasons. The forecasting performance is the minimum values of RMSE and MAPE of three models. The SARIMA model is the best approach for Y.31 Station [Wet Season], Y.20 Station [Wet Season], Y.37 Station [Wet Season], Y.31 Station [Dry Season], Y.20 Station [Dry Season], and Y.1C Station [Dry Season, while the best method for Y.37 Station [Dry Season] is ANN model, furthermore the SARIMANN model is the best approach for Y.1C Station [Wet Season]. All methods have delivered the similar results in dry season, while both SARIMA and SARIMANN are better than ANN in wet season by RMSE for all stations. Even though the downstream is affected by many disturbances, it is still more accurate than the upstream. This is the visible evidence to indicate that the stochastic based models, SARIMA and SARIMANN proposed in this study are appropriate for the high fluctuation series. Furthermore, the dry season forecasting is more accurate than the wet season.
Highlights
Among 25 main basins of Thailand, The Yom River basin located in Northern Thailand, with tropical wet and dry or even savanna climate throughout the year
This study proposes the hybridized forecasting models between the stochastic approaches, seasonal autoregressive integrated moving average (SARIMA) models and machine learning approach, artificial neural network (ANN)
Comparisons of the three models reveal the similarity of root mean square error (RMSE) and mean absolute percentage error (MAPE) for both wet season forecasting and dry season forecasting
Summary
Among 25 main basins of Thailand, The Yom River basin located in Northern Thailand, with tropical wet and dry or even savanna climate throughout the year. Changes of streamflow may be caused by climate changing and the human activities disturbance These lead to complication of hydrological modeling [4, 5, 6, 7]. Using auto regressive integrated moving average (ARIMA) and multiplicative Holt–Winters method, which the mean absolute percentage error (MAPE), mean squared error (MSE) and mean absolute error (MAE) were used to measure the performance. Forecasts from both methods were found to be acceptable but ARIMA gave a better result for that case. The accuracy of the three models was subjected to be based on the mean square error (MSE) and mean absolute error (MAE) and mean absolute percentage error (MAPE). The result showed that the hybrid ARIMA-ANN model was able to provide more accurate forecasts short-term (7-day) than the ARIMA and ANN models, but for a long-term forecast (30-day) the ANN model provides the most accurate forecast
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