Comparative assessment of time series and artificial intelligence models to estimate monthly streamflow: A local and external data analysis approach

Mehdizadeh, Saeid; Fathian, Farshad; Safari, Mir Jafar Sadegh; Adamowski, Jan

doi:10.1016/j.jhydrol.2019.124225

Cited by 46 publications

(11 citation statements)

References 34 publications

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“…Several other studies also encouraged the researchers to use MARS and M5T models for the prediction of runoff e.g. [31][32][33][34][35][36][37]. Apart from runoff prediction, MARS and M5T models were also used for the prediction of evapotranspiration (ET) and Pan Evaporation (Ep).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Relative Humidity in a High Elevated Basin of Western Karakoram by Using Different Machine Learning Models

Adnan¹,

Adnan²,

Liu³

et al. 2021

Weather Forecasting

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Accurate and reliable prediction of relative humidity is of great importance in all fields concerning global climate change. The current study has employed Multivariate Adaptive Regression Spline (MARS) and M5 Tree (M5T) models to predict the relative humidity in the Hunza River basin, Pakistan. Both the models provided the best prediction for the input scenario S6 (RHt-1, RHt-2, RHt-3, Tt-1, Tt-2, Tt-3). The statistical analysis displayed that the MARS model provided a better prediction of relative humidity as compared to M5T at all meteorological stations, especially, at Ziarat followed by Khunjerab and Naltar. The values of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) were (5.98%, 5.43%, and 0.808) for Khunjerab; (6.58%, 5.08%, and 0.806) for Naltar; and (5.86%, 4.97%, 0.815) for Ziarat during the testing of MARS model whereas, the values were (6.14%, 5.56%, and 0.772) for Khunjerab; (6.19%, 5.58% and 0.762) for Naltar and (6.08%, 5.46%, 0.783) for Ziarat during the testing of M5T model. Both the models performed slightly better in training as compared to the testing stage. The current study encourages future research to be conducted at high altitude basins for the prediction of other meteorological variables using machine learning tools.

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Section: Introductionmentioning

confidence: 99%

Prediction of Relative Humidity in a High Elevated Basin of Western Karakoram by Using Different Machine Learning Models

Adnan¹,

Adnan²,

Liu³

et al. 2021

Weather Forecasting

View full text Add to dashboard Cite

show abstract

“…gene expression programming, Bayesian networks, multivariate adaptive regression splines) and stochastic timeseries models (e.g. autoregressive, autoregressive moving average) and their combinations, concluding that stochastic models perform better than single ML algorithms but all models are inferior to hybrid stochastic-ML models [5][6][7]. For example, Zhu et al [3] used a mixture-kernel (GPR Gaussian Process Regression) approach to forecast seasonal streamflows obtaining good forecast quality.…”

Section: Introductionmentioning

confidence: 99%

Comparison between SARIMA and Holt–Winters models for forecasting monthly streamflow in the western region of Cuba

et al. 2021

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The present study aims to compare SARIMA and Holt–Winters model forecasts of mean monthly flow at the V Aniversario basin, western Cuba. Model selection and model assessment are carried out with a rolling cross-validation scheme using mean monthly flow observations from the period 1971–1990. Model performance is analyzed in one- and two-year forecast lead times, and comparisons are made based on mean squared error, root mean squared error, mean absolute error and the Nash–Sutcliffe efficiency; all these statistics are computed from observed and simulated time series at the outlet of the basin. The major findings show that Holt–Winters models had better performance in reproducing the mean series seasonality when the training observations were insufficient, while for longer training subsets, both models were equally competitive in forecasting one year ahead. SARIMA models were found to be more reliable for longer lead-time forecasts, and their limitations after being trained on short observation periods are due to overfitting problems. Article Highlights Comparison based on rolling cross-validation revealed the models forecasts sensibility to available observations amount. HW and SARIMA models perform better when limited observations or long-view forecasting, respectively, otherwise they do similar. HW models were superior modeling less variable monthly flows while SARIMA models better forecast the highly variable periods.

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“…Over the time, many hybrid models have been also implemented such as fractionally autoregressive integrated moving average (FARIMA) and self-exciting threshold autoregressive (SETAR) with GEP, MARS, and MLR [18]. Similarly, another authors used autoregressive conditional heteroscedasticity (ARCH) to hybridized GEP and MARS models [19]. In particular, the conventional MLbased models need numerous trial-and-error processes to determine the optimum architecture design.…”

Section: Introductionmentioning

confidence: 99%

On the Investigation of Monthly River Flow Generation Complexity Using the Applicability of Machine Learning Models

et al. 2021

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Streamflow is associated with several sources on nonstationaries and hence developing machine learning (ML) models is always the motive to provide a reliable methodology to understand the actual mechanism of streamflow. The current research was devoted to generating monthly streamflows from annual streamflow. In this study, three different ML models were applied for this purpose, including Multiple Additive Regression Trees (MART), Group Methods of Data Handling (GMDH), and Gene Expression Programming (GEP). The models were developed based on annual streamflow and monthly time index of three rivers (i.e., Upper Zab, Lower Zab, and Diyala) located in the north region of Iraq. The modeling results indicated an optimistic simulation for generating the monthly streamflow time series from annual streamflow time series. The potential of the MART model was superior to the GMDH and GEP models for Upper Zab River (R2 0.84, 0.64, and 0.47), Lower Zab River (R2 0.75, 0.46, and 0.40), and Diyala River (R2 0.78, 0.42, and 0.5). The results of RMSE were 113, 169, and 208 for Upper Zab River, 95, 149, and 0.5 for Lower Zab River, and 73, 118, and 109 for Diyala River. The results have proved the possibility of changing the timescale in generating streamflow data.

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Comparative assessment of time series and artificial intelligence models to estimate monthly streamflow: A local and external data analysis approach

Cited by 46 publications

References 34 publications

Prediction of Relative Humidity in a High Elevated Basin of Western Karakoram by Using Different Machine Learning Models

Prediction of Relative Humidity in a High Elevated Basin of Western Karakoram by Using Different Machine Learning Models

Comparison between SARIMA and Holt–Winters models for forecasting monthly streamflow in the western region of Cuba

On the Investigation of Monthly River Flow Generation Complexity Using the Applicability of Machine Learning Models

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