Seasonal Forecasting of Rainfall and Runoff Volumes in Riyadh Region, KSA

Fouli, Hesham; Fouli, Rabie; Bashir, Bashar; Loni, Oumar Allafouza

doi:10.1007/s12205-017-0726-3

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…In order to meet the basic requirements for ARIMA model building, the model also needs to be identified and ordered. The common ARIMA identification and ordering method is to determine the optimal ARIMA model by using the partial autocorrelation coefficient, autocorrelation coefficient and the AIC, SC and HQ criteria to determine the fit effect (Fouli et al 2018). The autocorrelation and partial autocorrelation coefficients of the first-order difference series and their correlation plots are obtained using the MATLAB toolbox, as depicted in Figure 6.…”

Section: Arima Identification and Orderingmentioning

confidence: 99%

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

Zhang

Zhu

et al. 2022

Water Supply

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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.

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Section: Arima Identification and Orderingmentioning

confidence: 99%

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

Zhang

Zhu

et al. 2022

Water Supply

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“…Generally, ARIMA model is denoted by ARIMA ( , , ) where , and are non-negative integers. In this notation, the -parameter refers to the autoregressive (AR) part, the -parameter refers to the order of regular differencing part, and the -parameter refers to the moving average (MA) part [18,19]. The back-shift operator ( ), where = − for is a positive integer.…”

Section: Study Region and Datasetmentioning

confidence: 99%

A Hybrid Seasonal Box Jenkins-ANN Approach for Water Level Forecasting in Thailand

Nualtong¹,

Panityakul²,

Khwanmuang³

et al. 2021

eer

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Every year, many basins in Thailand face the perennial droughts and floods that lead to the great impact on agricultural segments. In order to reduce the impact, water management would be applied to the critical basin, for instance, Yom River basin. An importing task of management is quantitative prediction of water that is stated by water level. This study proposes the hybridized forecasting models between the stochastic approaches, seasonal autoregressive integrated moving average (SARIMA) models and machine learning approach, artificial neural network (ANN). The proposed hybrid model is called seasonal autoregressive integrated moving average and artificial neural network or SARIMANN model for average monthly water level (AMWL) time series of Yom River basin. The study period is from April 2007 to March 2020, over thirteen hydrological years. The forecasting performance is the minimum values of root mean squared error (RMSE) and mean absolute percentage error (MAPE) between SARIMA models, ANN models, and SARIMANN models. Results indicated that: The three models reveal the similarity of RMSE and MAPE for both four water level measurement stations for wet and dry seasons. The forecasting performance is the minimum values of RMSE and MAPE of three models. The SARIMA model is the best approach for Y.

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“…The effect of one parameter on other using four types of codes is analysed and discussed. The two-dimensional code families considered in this comparison are GMWRSC, RWOP, CRWOP and MWOOC [21][22][23]. The comparisons are shown in the form of tabulations and figures.…”

Section: Introductionmentioning

confidence: 99%

Scrutinization of FOCDMA Code Families for Application in Optical Communication

Mishra

Ravikumar

2017

IJ-ICT

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<p align="justify">In code division multiple access (CDMA) code construction and analysis, the bit error rate due to multiple access interference is an important performance parameter which is overcome to some extent by generating almost orthogonal codes. Ideal orthogonal code families should have zero autocorrelation and no crosscorrelation. The building of fiber-optic CDMA (FOCDMA) is based on binary, unipolar spreading codes, which in turn requires considerably longer length of spreading codes in order to satisfy these constraints. Spreading an optical bit in wavelength, time and multiple fibers is observed to satisfy the constraints for accommodating sufficiently large number of users with a comparatively smaller spreading code length. Various optical code families of different dimensions have been proposed for FOCDMA. The performance of the code families varies under different conditions. In this research, some aspects of the performance issues have been considered.</p>

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Seasonal Forecasting of Rainfall and Runoff Volumes in Riyadh Region, KSA

Cited by 5 publications

References 9 publications

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

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

A Hybrid Seasonal Box Jenkins-ANN Approach for Water Level Forecasting in Thailand

Scrutinization of FOCDMA Code Families for Application in Optical Communication

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