Comparison of the monthly streamflow forecasting in Maroon dam using HEC-HMS and SARIMA models

Ahmadpour, Abbas; Mirhashemi, Seyed Hassan; Jou, Parviz Haghighat; frooghi, Farid

doi:10.1007/s40899-022-00686-1

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…Therefore, learning from the past is essential to observe and understand the evolution of the water cycle and river flow dynamic. Setting an early-warning system for taking timely and informed measures to avoid (if possible) or reduce the floods of drought effects on human activities is necessary [3][4][5][6][7][8]. Research of the rivers' flow was performed in different directions, as follows:…”

Section: Introductionmentioning

confidence: 99%

“…It does not effectively provide a river discharge model, which is essential for a reliable forecast. To complement the findings provided by statistical analysis [7,8], the HEC-RAS software was used to model the susceptibility to floods in different basins. HEC-HMS was employed to simulate losses, snowmelt, sub-basin routing, and river flow routing [35].…”

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confidence: 99%

“…Various methods can predict future time series behavior, from classical decomposition models-which emphasize trend, seasonality, and random variation to Box-Jenkins methods [8,10,[38][39][40], exponential smoothing [9], and different types of AI [12,[20][21][22][23][24][25] and hybrid models [28][29][30][31][32][33].…”

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confidence: 99%

“…Reducing the flood effects on the anthropic settlements was a practical problem that needed to be solved, and it was a study direction for different researchers. Some analyzed the dam's environmental effect, emphasizing the river flow alteration [8,12,[52][53][54][55][56]. A deep study of the impact of such structures may contribute to understanding this phenomenon and taking measures to reduce the effects of natural calamities.…”

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confidence: 99%

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Forecasting the River Water Discharge by Artificial Intelligence Methods

Bărbulescu,

Zhen

2024

Water

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The management of water resources must be based on accurate models of the river discharge in the context of the water flow alteration due to anthropic influences and climate change. Therefore, this article addresses the challenge of detecting the best model among three artificial intelligence techniques (AI)—backpropagation neural networks (BPNN), long short-term memory (LSTM), and extreme learning machine (ELM)—for the monthly data series discharge of the Buzău River, in Romania. The models were built for three periods: January 1955–September 2006 (S1 series), January 1955–December 1983 (S2 series), and January 1984–December 2010 (S series). In terms of mean absolute error (MAE), the best performances were those of ELM on both Training and Test sets on S2, with MAETraining = 5.02 and MAETest = 4.01. With respect to MSE, the best was LSTM on the Training set of S2 (MSE = 60.07) and ELM on the Test set of S2 (MSE = 32.21). Accounting for the R2 value, the best model was LSTM on S2 (R2Training = 99.92%, and R2Test = 99.97%). ELM was the fastest, with 0.6996 s, 0.7449 s, and 0.6467 s, on S, S1, and S2, respectively.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Forecasting the River Water Discharge by Artificial Intelligence Methods

Bărbulescu,

Zhen

2024

Water

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“…A wide range of approaches has been employed to improve stream ow synthesis techniques ranging from simple ARMA family (Rao et al, 1982;Stedinger et al, 1985;Mujumdar & Kumar, 1990; Kuo & Sun, 1996;Tesfaye et al, 2006;Fashae et al, 2019;Medda & Bhar, 2019;Gupta et al, 2022) to sophisticated hybrid models (Jia & Culver, 2006;Kwon et al, 2007;Nowak et al, 2011;Niu & Sivakumar, 2013;Hu et al, 2021;Abdelaziz et al, 2023). To synthesize monthly stream ow data that exhibits periodicity, several methods can be employed such as seasonal models (Dimri et al, 2020;Ahmadpour et al, 2022), decomposing stream ow with Fourier transforms (Chong et al, 2019;Abdelaziz et al, 2023) or Wavelet techniques (Nowak et al, 2011;Chong et al, 2019;Rhif et al, 2019), and pattern recognition (Panu et al, 1978;Panu & Unny, 1980a) are some approaches, among others. When it comes to the synthesis of periodic time series, pattern recognition techniques can be well-suited since they are designed to identify and extract patterns in the data.…”

Section: Introductionmentioning

confidence: 99%

Textural Image-Based Feature Prediction Model for Stochastic Streamflow Synthesis

Piran,

Panu

2023

Preprint

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To address the challenge of obtaining reliable streamflow data for water resource management, this paper develops an encoding scheme to transform a streamflow time series into an 8-bit grayscale image where it is feasible to develop a correlation between neighbouring pixels to reveal patterns that are not easily discernable in traditional time series analysis. To extract relevant information from an encoded streamflow image, a novel textural feature extraction approach has been developed for synthesizing streamflow data. The developed textural feature extraction model can capture the simultaneous correlation in two dimensions of an encoded streamflow image, which is then transformed into the frequency domain using a discrete Fourier transform. The use of the power spectrum of the Fourier coefficients facilitates the synthesis of encoded streamflow images. The effectiveness of the model is evaluated using three case studies across Canada by comparing the properties of synthesized streamflow with the historical streamflow using the structural similarity (SSIM) index. Results show that the proposed model effectively synthesizes encoded streamflow with high SSIM values for the Fraser, Black, and South Saskatchewan Rivers. The results also affirm that the model reproduces the temporal dependence and correlation structure of the historical streamflow and the average of 30 synthesized realizations up to more than 100 lags. The null hypothesis tests support the conclusion that there is statistically no significant difference between the synthesized monthly streamflow time series and the historical time series. In addition, the entropy-based test also emphasizes that synthesized and historical streamflow are indistinguishable.

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A hybrid SARIMA-Prophet model for predicting historical streamflow time-series of the Sobat River in South Sudan

Kenyi,

Yamamoto

2024

Discov Appl Sci

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Accurate river streamflow forecasting is pivotal for effective water resource planning, infrastructure design, utilization, optimization, and flood planning and warning. Streamflow prediction remains a difficult task due to several factors such as climate change, topography, and lack of observed data in some cases. This paper investigates and evaluates the individual performances of the seasonal auto-regressive integrated moving average (SARIMA) and Prophet models in forecasting the streamflow of the Sobat River and proposes a hybrid SARIMA-Prophet model to leverage the strengths of both approaches. Using the augmented Dickey-Fuller (ADF) and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests, the flow of the Sobat River was found to be stationary. The performance of the models was then assessed based on their residual errors and predictive accuracy using the mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2). Residual analysis and prediction capabilities revealed that Prophet slightly edged SARIMA in terms of prediction efficacy; however, both models struggled to effectively capture extreme values, resulting in significant overestimations and slight underestimations. The hybrid SARIMA-Prophet model significantly reduced residual variability, achieving a lower MAE of 4.047 m3/s, RMSE of 6.17 m3/s, and a higher R2 of 0.92 than did the SARIMA (MAE: 5.39 m3/s, RMSE: 8.70 m3/s, R2: 0.85) and Prophet (MAE: 5.35 m3/s, RMSE: 8.32 m3/s, and R2: 0.86) models. This indicates that the hybrid model handles both long-term patterns and short-term fluctuations more effectively than the individual models. The findings of the present study highlight the potential of hybrid SARIMA-Prophet models for streamflow forecasting in terms of accuracy and reliability, thus contributing to more effective water resource management and planning, particularly in the Sobat River.

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Comparison of the monthly streamflow forecasting in Maroon dam using HEC-HMS and SARIMA models

Cited by 5 publications

References 17 publications

Forecasting the River Water Discharge by Artificial Intelligence Methods

Forecasting the River Water Discharge by Artificial Intelligence Methods

Textural Image-Based Feature Prediction Model for Stochastic Streamflow Synthesis

A hybrid SARIMA-Prophet model for predicting historical streamflow time-series of the Sobat River in South Sudan

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