03723ntm a2200373 i 4500 0 0 lcc 0 -2 REF 20000 20000 REF 2019-11-05 0 FSKKP .E33 2019 r Thesis 0000127293 2019-11-05 00:00:00 1 2019-11-05 THESIS 0 0 lcc 0 0 REF 10000 10000 2019-11-05 0 CD12104 0000127294 2020-10-26 00:00:00 1 2019-11-05 THESIS 91064 91070 MY-KuUP 20251125105418.0 t||||fr|||| 000 0 ta 191105t20192019my ab||fram|| 000 0 eng d THE0008185(Local) hardback UMP eng UMP rda FSKKP .E33 2019 r Thesis Ahmed, Ehab Ali, author. Water level forecasting using feed forward neural networks optimized by african buffalo algorithm (ABO) / Ehab Ali Ahmed Kuantan, Pahang : UMP, 2019 © 2019 xii, 103 pages : illustrations, maps ; 30 cm. + 1 CD-ROM text rdacontent text rdacontent unmediated rdamedia computer rdamedia volume rdacarrier computer disc rdacarrier text file PDF rda Faculty of Computer Systems & Software Engineering Thesis (Master of Computer Science) -- Universiti Malaysia Pahang – 2019 Includes bibliographical references Water is an essential requirement for human life and activities associated with industries and agriculture. An accurate forecasting model would be helpful in providing a warning of impending flood during the flooding time and assist in regulating reservoir outflows during the low flows. This reason motivated the researchers to exploit the evolution of machine learning to develop water level forecasting systems that were characterized by accuracy, simplicity and low cost. This development goal is to reduce the impact of water variation in river water levels. The machine learning applications, especially Feed forward neural network (FFNN) which inspired from the human biological nervous system have been successful in solving several complex problems. The FFNN training process which is an optimization task to find the optimal controlling parameters (weights and biases) is considered as the main issues in any model performance. Due to that, many algorithms employ different training algorithms to guide the network for providing an accurate result with less training and testing error. These algorithms have succeeded with different accuracy levels, but it is still suffering from some weaknesses. Weakness such as trapped in local minima, slow convergence and finding a good rate between exploitation and exploration of the search space. This research proposed a swarm intelligence training algorithm, Improved African Buffalo Optimization algorithm (IABO) based on the Metaheuristic method called the African Buffalo Optimization algorithm (ABO). ABO has been successful in solving many improvement problems. These successes motivate the development and investigation of its efficiency in training Feed Forward Neural Networks (FFNNs), for solving training process issues. Additionally, the study investigated the effect of neurons number in the hidden layer, the number of population swarm, and the stopping criteria (iterations) on the model’s performance. Water level data set was chosen to test the proposed IABO-trained algorithm. The results were verified by benchmarking with the performance of the Particle Swarm Optimization (PSO) and Backpropagation (BP) algorithms. The results demonstrated the superiority of the IABO-trained algorithm in avoiding local minima, convergence speed, and accuracy compared to the benchmarking (BP and PSO) algorithms in water level forecasting tasks. Faculty of Computer System and Software Engineering Dissertations Universities and colleges Disertations Theses lcc THESIS