Comparing ANN and ARIMA model in predicting the discharge of River Opeki from 2010 to 2020

Fashae, Olutoyin Adeola; Olusola, Adeyemi; Ndubuisi, Ijeoma; Udomboso, Christopher Godwin

doi:10.1002/rra.3391

Cited by 26 publications

(10 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Next, the autocorrelation and partial autocorrelation analysis were performed on the non-stationary data sets to determine the order of the order of auto-regression (p) and moving average (q). The ACF determines the amount of linear dependence between streamflow data and lags of itself, whereas the PACF identifies the required autoregressive terms to reveal the time lag characteristics [53]. Once the order of the model was identified, the Akaike information criterion (AIC) was used to determine the optimum model parameter.…”

Section: Development Of Arima Modelmentioning

confidence: 99%

Exploring Spatiotemporal Relations between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

Sazib

Bolten

Mladenova

2020

Water

View full text Add to dashboard Cite

An understanding of streamflow variability and its response to changes in climate conditions is essential for water resource planning and management practices that will help to mitigate the impacts of extreme events such as floods and droughts on agriculture and other human activities. This study investigated the relationship between precipitation, soil moisture, and streamflow over a wide range of watersheds across the United States using Google Earth Engine (GEE). The correlation analyses disclosed a strong association between precipitation, soil moisture, and streamflow, however, soil moisture was found to have a higher correlation with the streamflow relative to precipitation. Results indicated different strength of the association depends on the watershed classes and lag times assessments. The perennial watersheds showed higher coherence compared to intermittent watersheds. Previous month precipitation and soil moisture have a stronger influence on the current month streamflow, particularly in the snow-dominated watersheds. Monthly streamflow forecasting models were developed using an autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The results showed that the SVM model generally performed better than the ARIMA model. Overall streamflow forecasting model performance varied considerably among watershed classes, and perennial watersheds tend to exhibit better predictably compared to intermittent watersheds due to lower streamflow variability. The SVM models with precipitation and streamflow inputs performed better than those with streamflow input only. Results indicated that the inclusion of antecedent root-zone soil moisture improved the streamflow forecasting in most of the watersheds, and the largest improvements occurred in the intermittent watersheds. In conclusion, this work demonstrated that knowing the relationship between precipitation, soil moisture, and streamflow in different watershed classes will enhance the understanding of the hydrologic process and can be effectively utilized in improving streamflow forecasting for better satellite-based water resource management strategies.

show abstract

Section: Development Of Arima Modelmentioning

confidence: 99%

Exploring Spatiotemporal Relations between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

Sazib

Bolten

Mladenova

2020

Water

View full text Add to dashboard Cite

show abstract

“…ARIMA is a combination of models ( , , ), where ( ) is the order of Autoregressive components, ( ) Integrated is the degree of difference involved (non-seasonal difference), and ( ) is the order of Moving Average components [14,15]. In this study, the interpretation of Autoregressive is the discharge value at the time which is influenced by the previous period until the p-period discharge value.…”

Section: Arima Discharge Forecasting Methodsmentioning

confidence: 99%

Optimization Model of Cirata Reservoir Management Using Discrete Markov and ARIMA Discharge Forecasting Methods

Waluyo

Marselina

2022

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Even though the management operation guidelines have been determined, the optimal management of the Citarum Cascade Reservoir has not yet been achieved. Optimal management of cascade reservoirs in an integrated manner is difficult to be carried out due to complex operating procedures and high uncertainty of hydrological components. So, research on the optimal management of each reservoir considering the discharge forecasting model as input discharge (Qin) was proposed. The objective of this study was to optimize Cirata Reservoir’s operational management. Hence all raw water demands are met without any water wasted through the spillway, as indicated by the correlation between inflow discharge (historical and model) and the correlation between trajectory (guideline and actual). In this case, discharge forecasting methods used were Discrete Markov and ARIMA. The continuous with five years return period (R5 continuous), continuous with ten years return period (R10 continuous), 3-classes Discrete Markov, and 5-classes Discrete Markov methods were applied for guideline trajectories. Based on the results, the correlation coefficients between inflow discharge were 0.63 (3-Classes Discrete Markov); 0.78 (5-Classes Discrete Markov); and 0.68 (ARIMA (1,0,0)(1,0,1) & ARIMA (0,0,2)(1,0,1)). Optimization simulations were carried out for 16 scenarios with combinations of four discharge models and four guideline trajectories during the 2016-2020 period. Based on the research, the scenario by the 5-Classes Discrete Markov discharge model and the R10 continuous guideline trajectory is the most optimal management of the Cirata Reservoir, with a correlation coefficient between the trajectory was 0.91.

show abstract

“…It enables easy real-time adjustment of the model using the availability of more historical data, as well as incorporating the residuals generated by the fit as an element of analysis. Its outstanding advantage is the advanced accuracy of the short time prediction results (Fashae et al 2018). SARIMA models are currently widely used in the fields of energy, climate, medicine and economics, especially for hydrological time series containing seasonal variations, and are admired by scholars at home and abroad.…”

Section: Seasonal Autoregressive Integrated Moving Average (Sarima) M...mentioning

confidence: 99%

A seasonal ARIMA model based on the gravitational search algorithm (GSA) for runoff prediction

Zhang

Zhu

et al. 2022

Water Supply

View full text Add to dashboard Cite

The prediction of river runoff is crucial for flood forecasting, agricultural irrigation and hydroelectric power generation. Coupled runoff prediction model based on the Gravitational Search Algorithm (GSA) and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model is proposed to address the non-linear and seasonal features of runoff data. The GSA has a significant local optimisation capability, while the SARIMA model allows for real-time adjustment of the model using historical data and is suitable for analysing time series with seasonal variations. Consequently, the GSA-SARIMA model was developed and applied to the runoff prediction of the Xianyang section of the Wei River. The results suggest that the GSA-SARIMA model achieves a linear correlation coefficient of 0.9351, a Nash efficiency coefficient of 0.91, a mean relative error of 6.57 and a root mean square error of 0.21. All of the evaluation indicators of this model outperform the other models developed, and its application to actual runoff prediction is feasible, which creates a new path for runoff prediction.

show abstract

Comparing ANN and ARIMA model in predicting the discharge of River Opeki from 2010 to 2020

Cited by 26 publications

References 40 publications

Exploring Spatiotemporal Relations between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

Exploring Spatiotemporal Relations between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

Optimization Model of Cirata Reservoir Management Using Discrete Markov and ARIMA Discharge Forecasting Methods

A seasonal ARIMA model based on the gravitational search algorithm (GSA) for runoff prediction

Contact Info

Product

Resources

About