Publication:
A new soft computing model for daily streamflow forecasting

Simple item page
dc.citedby18
dc.contributor.authorSammen S.S.en_US
dc.contributor.authorEhteram M.en_US
dc.contributor.authorAbba S.I.en_US
dc.contributor.authorAbdulkadir R.A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57192093108en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid57208942739en_US
dc.contributor.authorid57200567560en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:05:27Z
dc.date.available2023-05-29T09:05:27Z
dc.date.issued2021
dc.descriptionClimate change; Data streams; Floods; Forecasting; Genetic algorithms; Hydroelectric power plants; Mean square error; Multilayer neural networks; Particle swarm optimization (PSO); Soft computing; Soil conservation; Water conservation; Water management; Water supply; Flow quantification; Multi layer perceptron; Optimization algorithms; Predicting models; Root mean square errors; Soft computing models; Streamflow forecasting; Watershed management; Stream flow; algorithm; forecasting method; hydroelectric power plant; numerical model; optimization; principal component analysis; streamflow; watershed; Helianthusen_US
dc.description.abstractAccurate stream flow quantification and prediction are essential for the local and global planning and management of basins to cope with climate change. The ability to forecast streamflow is crucial, as it can help mitigate flood risks. Long-term stream flow data records are needed for hydropower plant construction, flood prediction, watershed management, and long-term water supply use. An accurate assessment of streamflow is considered as�very challenging and critical tasks. A new predicting model is developed in this research, combining the technique of sunflower optimization (SFA) as an evolutionary algorithm with the multi-layer perceptron (MLP) algorithm to predict streamflow in Malaysia's Jam Seyed Omar (JSO) and Muda Di Jeniang (MDJ) stations. Principal component analysis (PCA) was performed on Q (t) (t: the number of the current day) before model creation to pick essential inputs for a maximum of 6 lags. With the classical MLP and two other hybrid MLP models (MLP-particle swarm optimization (MLP-PSO) and MLP-genetic algorithm (MLP-GA)), the results of the MLP-sunflower algorithm (SFA) were benchmarked. As compared to other models, the MLP-SFA could be able to reduce the Root Mean Square Error (RMSE) by a value of between 12 and 21% at the JSO station and between 8 and 24% at the MDJ station. In conclusion, this research found that combining MLP with optimization algorithms improved the precision of the stand-alone MLP model, with SFA integration being the most efficient. � 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s00477-021-02012-1
dc.identifier.epage2491
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85105878406
dc.identifier.spage2479
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85105878406&doi=10.1007%2fs00477-021-02012-1&partnerID=40&md5=de2bfa0995b71148196efb238552136f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25891
dc.identifier.volume35
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleStochastic Environmental Research and Risk Assessment
dc.titleA new soft computing model for daily streamflow forecastingen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS