Parameter estimation of an ARMA model for river flow forecasting using goal programming

Mohammadi, Kourosh; Eslami, H R; Kahawita, R.

doi:10.1016/j.jhydrol.2006.05.017

Cited by 107 publications

(41 citation statements)

References 11 publications

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“…Some of the earliest examples of the AR type of stream flow forecast models include Thomas and Fiering (1962) and Yevjevich (1963). These approaches have employed conventional methods of the time series forecasting and modeling (Owen et al, 2001;BuHamra et al, 2003;Zhang, 2003;Mohammadi et al, 2006;Arena et al, 2006;Komornik et al, 2006;Toth et al, 2000). Artificial neural networks (ANN) have been recently accepted as an efficient alternative tool for modeling of complex hydrologic system to the conventional methods and widely used for prediction.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Artificial Intelligence Techniques for river flow forecasting

Fırat

2008

Hydrol. Earth Syst. Sci.

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Abstract. The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN), and Auto-Regressive (AR) models for forecasting of daily river flow is investigated and Seyhan River and Cine River was chosen as case study area. For the Seyhan River, the forecasting models are established using combinations of antecedent daily river flow records. On the other hand, for the Cine River, daily river flow and rainfall records are used in input layer. For both stations, the data sets are divided into three subsets, training, testing and verification data set. The river flow forecasting models having various input structures are trained and tested to investigate the applicability of ANFIS and ANN and AR methods. The results of all models for both training and testing are evaluated and the best fit input structures and methods for both stations are determined according to criteria of performance evaluation. Moreover the best fit forecasting models are also verified by verification set which was not used in training and testing processes and compared according to criteria. The results demonstrate that ANFIS model is superior to the GRNN and FFNN forecasting models, and ANFIS can be successfully applied and provide high accuracy and reliability for daily river flow forecasting.

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Section: Introductionmentioning

confidence: 99%

Comparison of Artificial Intelligence Techniques for river flow forecasting

Fırat

2008

Hydrol. Earth Syst. Sci.

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“…Therefore, the selection of the sensor network flow model must be consistent with its application scenarios. Literature [18][19][20] proposed an ARMA model for flow predictions, which applies to periodic data acquisition. …”

Section: Arma Model For Node Flow Forecastingmentioning

confidence: 99%

An adaptive traffic MAC protocol based on correlation of nodes

Wang

2015

J Wireless Com Network

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EA-MAC protocol is proposed on the basis of SMAC protocol to remedy the shortcomings of SMAC. In the EA-MAC, node correlation analysis algorithm and traffic adaptive duty cycle mechanism are added. Meanwhile, the residual energy is introduced into the existing traffic adaptive back-off mechanism to measure the saving efficiency. In the node correlation algorithm, all network nodes are divided into several areas by computing node correlation according to the collected information. The clustering mechanism is applied for choosing representative node in each area for transmitting data. This method can effectively reduce redundant nodes transmitting duplicate data. In traffic adaptive duty cycle mechanism, the duty cycle is regulated dynamically to decrease idle listening by comparing the threshold set with the flow value obtained from the predict flow model. In back-off mechanisms, by adjusting the value of contention window and back-off time, data collisions can be effectively avoided when network traffic is heavy. In addition, nodes with more remaining energy have priority to access the channel and have shorter back-off time, which can keep the balance of the whole network energy consumption and lengthen network lifetime. Simulation results show that the EA-MAC protocol has better energy saving, throughput, shorter delay performance, and low packet loss rate than that of SMAC protocol in dynamic traffic sensor networks.

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“…The time series of the collected data sample for a period of 48 years was plotted. Equation (1) and Equation (2) were computed to check if gauged points are correlated WNN method and forecasting Models search as Coefficient of correlation and root mean square error are used [18]- [22]. …”

Section: Hydrological Correlation Analysismentioning

confidence: 99%

Capacity Optimization Study of Chua Mini-Hydropower Plant at Chua River, Manica, Mozambique

Uamusse

Tsamba²,

Matsinhe³

et al. 2015

EPE

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Hydropower energy is one of most promising clean energy technologies, however this energy technology has many challenges. Compared with other renewable energies for example biomass, solar and wind energies, it has high capital investment cost. In Mozambique, access to conversional energy in form of electricity has been limited to most of the rural population. The objectives of the investigation research are to analyze Chua micro-hydropower plant exploration in Manica district in Mozambique and to examine the possibility of increasing energy production. The current total installed power generation capacity in Mozambique is about 939 MW. Hydropower contributes 561 MW, making a contribution of 61%. Oil contributes 27%, and natural gas contributes 12% of the total electric grid generation in Mozambique.

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Parameter estimation of an ARMA model for river flow forecasting using goal programming

Cited by 107 publications

References 11 publications

Comparison of Artificial Intelligence Techniques for river flow forecasting

Comparison of Artificial Intelligence Techniques for river flow forecasting

An adaptive traffic MAC protocol based on correlation of nodes

Capacity Optimization Study of Chua Mini-Hydropower Plant at Chua River, Manica, Mozambique

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