A new evolutionary time series model for streamflow forecasting in boreal lake-river systems

Mehr, Ali Danandeh; Ghadimi, Sahand; Marttila, Hannu; Haghighi, Ali Torabi

doi:10.1007/s00704-022-03939-3

Cited by 30 publications

(5 citation statements)

References 48 publications

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“…The observed errors suggest that SARIMA models tend to be more reliable for longer-term forecasts, in line with the findings of Alonso Brito et al [72]. Additionally, the performance of SARIMA models is highly sensitive to the training time series used in model calibration, as highlighted by Danandeh Mehr et al [35].…”

Section: Discussionsupporting

confidence: 80%

“…In contrast, the SARIMA model only achieved a satisfactory rating at the 12 month forecast horizon. For instance, these findings differ from those of Danandeh Mehr et al [35,76] in their studies conducted for rivers in Turkey and Finland, whose NSE results with the SARIMA model report inferior to satisfactory performance in long-term forecasting. Similarly, the research of Chechi and Sanches [37] and Meis et al [77] observed the improved performance of SARIMAX to SARIMA in their studies for Brazilian watersheds, with their analyses highlighting the model's strengths in metrics like RMSE, NSE, and R 2 .…”

Section: Discussioncontrasting

confidence: 77%

“…They obtained R 2 values above 0.83 for all of the monitoring locations in the validation set. Danandeh Mehr et al [35] showed satisfactory results when using the SARIMA model for streamflow forecasting on one-step-ahead daily and weekly scales at the Oulujoki River system in northeastern Finland. However, intramonthly streamflow forecasting exhibited low accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil

Costa,

Menezes Filho,

Canales

et al. 2023

Hydrology

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Stochastic modeling to forecast hydrological variables under changing climatic conditions is essential for water resource management and adaptation planning. This study explores the applicability of stochastic models, specifically SARIMA and SARIMAX, to forecast monthly average river discharge in a sub-basin of the Paranaíba River near Patos de Minas, MG, Brazil. The Paranaíba River is a vital water source for the Alto Paranaíba region, serving industrial supply, drinking water effluent dilution for urban communities, agriculture, fishing, and tourism. The study evaluates the performance of SARIMA and SARIMAX models in long-term discharge modeling and forecasting, demonstrating the SARIMAX model’s superior performance in various metrics, including the Nash–Sutcliffe coefficient (NSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The inclusion of precipitation as a regressor variable considerably improves the forecasting accuracy, and can be attributed to the multivariate structure of the SARIMAX model. While stochastic models like SARIMAX offer valuable decision-making tools for water resource management, the study underscores the significance of employing long-term time series encompassing flood and drought periods and including model uncertainty analysis to enhance the robustness of forecasts. In this study, the SARIMAX model provides a better fit for extreme values, overestimating peaks by around 11.6% and troughs by about 5.0%, compared with the SARIMA model, which tends to underestimate peaks by an average of 6.5% and overestimate troughs by approximately 76.0%. The findings contribute to the literature on water management strategies and mitigating risks associated with extreme hydrological events.

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Section: Discussionsupporting

confidence: 80%

Section: Discussioncontrasting

confidence: 77%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil

Costa,

Menezes Filho,

Canales

et al. 2023

Hydrology

View full text Add to dashboard Cite

show abstract

“…Because of the need to assume linearity or heavy redundancy between lags, many optimal lag selection approaches underperform. To address the collinearity issue, this research also considered the average mutual information (AMI), which may be considered a nonlinear generalisation of the autocorrelation function [62]. The MI method is used in this research to select the best explanatory factors.…”

Section: Data Pre-processing Techniquesmentioning

confidence: 99%

Examination of Single- and Hybrid-Based Metaheuristic Algorithms in ANN Reference Evapotranspiration Estimating

Khairan,

Zubaidi,

Raza

et al. 2023

Sustainability

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Hydrological resource management, including crop watering and irrigation scheduling, relies on reliable estimates of reference evapotranspiration (ETo). However, previous studies of forecasting ETo have not dealt with comparing single and hybrid metaheuristic algorithms in much detail. This study aims to assess the efficiency of a novel methodology to simulate univariate monthly ETo estimates using an artificial neural network (ANN) integrated with the hybrid particle swarm optimisation–grey wolf optimiser algorithm (PSOGWO). Several state-of-the-art algorithms, including constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA), the slime mould algorithm (SMA), the marine predators algorithm (MPA) and the modified PSO algorithm were used to evaluate PSOGWO’s prediction accuracy. Monthly meteorological data were collected in Al-Kut City (1990 to 2020) and used for model training, testing and validation. The results indicate that pre-processing techniques can improve raw data quality and may also suggest the best predictors scenario. That said, all models can be considered efficient with acceptable simulation levels. However, the PSOGWO-ANN model slightly outperformed the other techniques based on several statistical tests (e.g., a coefficient of determination of 0.99). The findings can contribute to better management of water resources in Al-Kut City, an agricultural region that produces wheat in Iraq and is under the stress of climate change.

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“…In order to choose the most effective explanatory variables, the mutual information (MI) technique was applied in this study. Danandeh Mehr et al [52] stated that the average mutual information (AMI) is a non-linear generalization of the autocorrelation function. Mutual information is commonly utilized to locate time-delayed independent variables.…”

Section: Data Pre-processingmentioning

confidence: 99%

Applicability of ANN Model and CPSOCGSA Algorithm for Multi-Time Step Ahead River Streamflow Forecasting

et al. 2022

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Accurate streamflow prediction is significant when developing water resource management and planning, forecasting floods, and mitigating flood damage. This research developed a novel methodology that involves data pre-processing and an artificial neural network (ANN) optimised with the coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA-ANN) to forecast the monthly water streamflow. The monthly streamflow data of the Tigris River at Amarah City, Iraq, from 2010 to 2020, were used to build and evaluate the suggested methodology. The performance of CPSOCGSA was compared with the slim mold algorithm (SMA) and marine predator algorithm (MPA). The principal findings of this research are that data pre-processing effectively improves the data quality and determines the optimum predictor scenario. The hybrid CPSOCGSA-ANN outperformed both the SMA-ANN and MPA-ANN algorithms. The suggested methodology offered accurate results with a coefficient of determination of 0.91, and 100% of the data were scattered between the agreement limits of the Bland–Altman diagram. The research results represent a further step toward developing hybrid models in hydrology applications.

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A new evolutionary time series model for streamflow forecasting in boreal lake-river systems

Cited by 30 publications

References 48 publications

Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil

Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil

Examination of Single- and Hybrid-Based Metaheuristic Algorithms in ANN Reference Evapotranspiration Estimating

Applicability of ANN Model and CPSOCGSA Algorithm for Multi-Time Step Ahead River Streamflow Forecasting

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