Analysis of water budget prediction accuracy using ARIMA models

Biryło, Monika; Rzepecka, Zofia; Kuczynska-Siehien, Joanna; Nastula, Jolanta

doi:10.2166/ws.2017.156

Cited by 10 publications

(7 citation statements)

References 8 publications

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“…It comes in a variety of forms like AR, MA, or combination of AR and MA, referred to as autoregressive moving average (ARMA) or seasonal autoregressive integrated moving average (SARIMA) [22,23]. It has been found in literature that only a few attempts have been undertaken to predict the WL using the SARIMA model, such as predictions of lake water levels [24] and groundwater levels [25]. Whereas, the SARIMA model has the advantage of requiring few model parameters to describe time series, that show non-stationarity both within and through seasons [26].…”

Section: Introductionmentioning

confidence: 99%

Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study

et al. 2022

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Reservoir water level (RWL) prediction has become a challenging task due to spatio-temporal changes in climatic conditions and complicated physical process. The Red Hills Reservoir (RHR) is an important source of drinking and irrigation water supply in Thiruvallur district, Tamil Nadu, India, also expected to be converted into the other productive services in the future. However, climate change in the region is expected to have consequences over the RHR’s future prospects. As a result, accurate and reliable prediction of the RWL is crucial to develop an appropriate water release mechanism of RHR to satisfy the population’s water demand. In the current study, time series modelling technique was adopted for the RWL prediction in RHR using Box–Jenkins autoregressive seasonal autoregressive integrated moving average (SARIMA) and artificial neural network (ANN) hybrid models. In this research, the SARIMA model was obtained as SARIMA (0, 0, 1) (0, 3, 2)12 but the residual of the SARIMA model could not meet the autocorrelation requirement of the modelling approach. In order to overcome this weakness of the SARIMA model, a new SARIMA–ANN hybrid time series model was developed and demonstrated in this study. The average monthly RWL data from January 2004 to November 2020 was used for developing and testing the models. Several model assessment criteria were used to evaluate the performance of each model. The findings showed that the SARIMA–ANN hybrid model outperformed the remaining models considering all performance criteria for reservoir RWL prediction. Thus, this study conclusively proves that the SARIMA–ANN hybrid model could be a viable option for the accurate prediction of reservoir water level.

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Section: Introductionmentioning

confidence: 99%

Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study

et al. 2022

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“…Predicting dynamic water level variations using ARIMA and sequential Gaussian simulation (SGS) was the focus of (Takafuji et al, 2019). Groundwater fluctuation patterns in the Warangal district were assessed using the ARIMA and parametric Mann-Kendal (MK) methods (Satish Kumar & Venkata Rathnam, 2019) Multiple research (Birylo et al, 2018) (Gibrilla et al, 2018) used the ARIMA model to forecast yearly, monthly, and seasonal GWL. Forecasting GWL in a complex and uncertain environment can be made easier with the utilization of ANN algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Groundwater Levels at Colorado State of USA Using ARIMA and ANN Models

Ghafoor,

Umer,

Muhammad

et al. 2023

Preprint

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Variations in groundwater levels that are noticed in different parts of the world are typically the primary focus of research carried out in hydrology. The fluctuating levels of groundwater can be ascribed to several different factors, some of which include an increase in the demand for water, inappropriate irrigation practices, improper management of soil, and unregulated extraction from aquifers. It is necessary to effectively manage groundwater resources to have a trustworthy method of measuring and forecasting groundwater levels. The dynamics of groundwater are inherently uneven and difficult to understand. As a result, applying methods driven by data could potentially yield significant gains in hydrology. In this study, two data-driven models were utilized to estimate groundwater levels at a total of 4 monitoring wells located in Colorado state, USA. These models included ARIMA and ANN. The comparative analysis of the algorithms made use of a data set that had one month's worth of information from each of the years 1980 to 2019. The models were put through their paces, and the results of those tests were statistically and visually assessed. To evaluate the relative accuracy and precision of the models, the mean absolute error (MAE), mean square error (MSE), and root-mean-square error (RMSE) were used. When compared to ARIMA, the analysis demonstrates that ANN can produce the most accurate forecasts of groundwater levels in Colorado state USA.

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“…The AR part describes the relationship between present and past observations, the MA part represents the autocorrelation structure of error, and the I part represents the differencing level of the series [65]. ARIMA is one of the most powerful and successful linear statistical models for time series forecasting [66]. Research made by Valipour and Banihabib [67] showed that in comparison with ARMA (autoregressive moving average), the ARIMA model is better than ARMA because it can make time-series stationery in the training and forecasting phase [68].…”

Section: Forecasting Using Automated Arima Toolmentioning

confidence: 99%

Kabul River Flow Prediction Using Automated ARIMA Forecasting: A Machine Learning Approach

et al. 2021

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The water level in a river defines the nature of flow and is fundamental to flood analysis. Extreme fluctuation in water levels in rivers, such as floods and droughts, are catastrophic in every manner; therefore, forecasting at an early stage would prevent possible disasters and relief efforts could be set up on time. This study aims to digitally model the water level in the Kabul River to prevent and alleviate the effects of any change in water level in this river downstream. This study used a machine learning tool known as the automatic autoregressive integrated moving average for statistical methodological analysis for forecasting the river flow. Based on the hydrological data collected from the water level of Kabul River in Swat, the water levels from 2011–2030 were forecasted, which were based on the lowest value of Akaike Information Criterion as 9.216. It was concluded that the water flow started to increase from the year 2011 till it reached its peak value in the year 2019–2020, and then the water level will maintain its maximum level to 250 cumecs and minimum level to 10 cumecs till 2030. The need for this research is justified as it could prove helpful in establishing guidelines for hydrological designers, the planning and management of water, hydropower engineering projects, as an indicator for weather prediction, and for the people who are greatly dependent on the Kabul River for their survival.

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Analysis of water budget prediction accuracy using ARIMA models

Cited by 10 publications

References 8 publications

Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study

Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study

Predicting Groundwater Levels at Colorado State of USA Using ARIMA and ANN Models

Kabul River Flow Prediction Using Automated ARIMA Forecasting: A Machine Learning Approach

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