RBFNN Versus Empirical Models for Lag Time Prediction in Tropical Humid Rivers

Seyam, Mohammed; Othman, Faridah; El-Shafie, Ahmed

doi:10.1007/s11269-016-1518-0

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…R is a statistical technique that indicates the strength and direction of a linear relationship between two variables [36,37]. In this study, R was used to validate the agreement between the observed and predicted hourly SFs.…”

Section: Performance Evaluation Criteriamentioning

confidence: 99%

Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines

2017

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Abstract. Stream flow (SF) prediction is considered as a very complex due to the hydrological systems of surface water are complex and dynamic. The reliable prediction of stream flow (SF) can be performed by either conceptual or data-driven based models. In the modelling of hydrological processes, the support vector machine (SVM) is a novel, data-driven approach. Hence, six SVM-based models were generated in this study to predict real time hourly SF in the Selangor River Basin from the water level and rainfall of upstream stations. These models composed of six different combinations of input variables and were trained and tested under hourly records of SF, rainfall, and water level over one year (2011). Among the SVM-based models, SVM-M6, which has nine input variables, was the most effective. Under the training and testing data sets, its correlation coefficient and mean absolute error values were 0.992, 0.953, 0.061 and 0.253 respectively.

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Section: Performance Evaluation Criteriamentioning

confidence: 99%

Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines

2017

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“…Therefore, the artificial intelligence approach is considered a suitable option to model such a relationship [3,13]. The strength of AI techniques originated from their ability to simulate the human brain's behavior to solve complex problems [14]. As one of the AI techniques, artificial neural network (ANN) structures are composed of input, output, and at least one hidden layer, each of which has one or more simple interconnected adaptive elements called 'nodes' that have a high ability to perform a huge number of parallel computations.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Influence of Excess Pumping on Groundwater Salinity in the Gaza Coastal Aquifer (Palestine) Using Three Predicted Future Scenarios

Seyam

Alagha

Abunama

et al. 2020

Water

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The Gaza coastal aquifer (GCA) is the only source of water for about two million citizens living in Gaza Strip, Palestine. The groundwater quality in GCA has deteriorated rapidly due to many factors. The most crucial factor is the excess pumping due to the high population density. The objective of this article was to evaluate the influence of excess pumping on GCA’s salinity using 10-year predicted future scenarios based on artificial neural networks (ANNs). The ANN-based model was generated to predict the GCA’s salinity for three future scenarios that were designed based on different pumping rates. The results showed that when the pumping rate remains at the present conditions, salinity will increase rapidly in most GCA areas, and the availability of fresh water will decrease in disquieting rates by 2030. Only about 8% of the overall GCA’s area is expected to stay within 500 mg/L of the chloride concentration. Results also indicate that salinity would be improved slightly if the pumping rate is kept at 50% of the current pumping rates while the improvement rate is much faster if the pumping is stopped completely, which is an unfeasible scenario. The results are considered as an urgent call for developing an integrated water management strategy aiming at improving GCA quality by providing other drinking water resources to secure the increasing water demand.

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Modeling the Effect of the Pumping Variations on the Groundwater Quality in the Semiarid Aquifers

Seyam

2022

Sustainable Energy-Water-Environment Nexus in Deserts

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RBFNN Versus Empirical Models for Lag Time Prediction in Tropical Humid Rivers

Cited by 6 publications

References 33 publications

Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines

Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines

Investigation of the Influence of Excess Pumping on Groundwater Salinity in the Gaza Coastal Aquifer (Palestine) Using Three Predicted Future Scenarios

Modeling the Effect of the Pumping Variations on the Groundwater Quality in the Semiarid Aquifers

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