World Journal of Engineering Research and Technology (WJERT) has indexed with various reputed international bodies like : Google Scholar , Index Copernicus , Indian Science Publications , SOCOLAR, China , International Institute of Organized Research (I2OR) , Cosmos Impact Factor , Research Bible, Fuchu, Tokyo. JAPAN , Scientific Indexing Services (SIS) , Jour Informatics (Under Process) , UDLedge Science Citation Index , International Impact Factor Services , International Scientific Indexing, UAE , International Society for Research Activity (ISRA) Journal Impact Factor (JIF) , International Innovative Journal Impact Factor (IIJIF) , Science Library Index, Dubai, United Arab Emirates , Scientific Journal Impact Factor (SJIF) , Science Library Index, Dubai, United Arab Emirates , Eurasian Scientific Journal Index (ESJI) , Global Impact Factor (0.342) , IFSIJ Measure of Journal Quality , Web of Science Group (Under Process) , Directory of Research Journals Indexing , Scholar Article Journal Index (SAJI) , International Scientific Indexing ( ISI ) , 

World Journal of Engineering
Research and Technology

( An ISO 9001:2015 Certified International Journal )

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

Impact Factor : 5.924

ICV : 79.45

News & Updation

  • Article Invited for Publication

    Article are invited for publication in WJERT Coming Issue

  • ICV

    WJERT Rank with Index Copernicus Value 79.45 due to high reputation at International Level

  • WJERT New Impact Factor

    Its our Pleasure to Inform you that WJERT Impact Factor has been increased from  5.549 to 5.924 due to high quality Publication at International Level


    JANUARY 2022 Issue has been successfully launched on 1 January 2022.

  • New Issue Published

    Its Our pleasure to inform you that, WJERT 1 January 2022 Issue has been Published, Kindly check it on




A.O. Ibeje*


In the study, simulation model is developed for the prediction of daily inflow into Dadin-Kowa Reservoir (River Gongola) in Northern Nigeria. In the study, the 1991-2001 records of observed and forecasted daily rainfall amounts are used as predictors and the reservoir daily inflow as predicted targets for Multilayer Perceptron Artificial Neural Networks (MLP-ANNs). With a learning rate of 0.01 and momentum coefficient of 0.85, the MLP-ANN model is developed using 1 input node, 7 hidden nodes, 1000 training epoches and 24 adjustable parameters. Error measures such as the Mean Absolute Error (MAE), the Mean Squared Relative Error (MSRE) and the Coefficient of Determination (R2) are employed to evaluate the performance of the developed model for data calibration (1991-1998), verification (1991-2001) and validation (2010-2011). The result revealed: MAE={0.7156, 0.6717, 1.046} x 10-5; MSRE = {1.4984, 1.5087, 1.1478 }x 10-7; and R = {0.9957,0.9958,0.9688}. Furthermore, dynamic model is developed based on observed and simulated daily reservoir inflow to obtain optimal allocation policy to irrigation, industrial and domestic user sectors for each month of the year. The research reveals that only the months with prolonged dry spells have optimal returns to the user sectors while the months with records of rainfall could not produce optimized returns in the model. Therefore, the application of the results will lead to saving N175, 298,126 annually in the dam provision of water to the region.

[Full Text Article]