Thailand port throughput prediction via particle swarm optimization based neural network

Abstract

Shipping volume in Thailand have signifi cantly increased in last four years. It is important to pay attention to the trend of Thailand port throughput and use as the guideline to prepare for the needs of supporting facilities, infrastructures, fi nancial and human resources. An effective forecasting technique called particle swarm based neural network (PSO-NN) is developed to estimate Thailand port throughput in this work. The prediction results from PSONN and classical backpropagation training algorithm, backpropagation neural networks (BPNN) were compared. The results shown that PSONN provides more accurate results than BPNN when apply to predict port throughput of Thailand. The mean squared error obtained from PSONN are about 10 times lower than that of BPNN. This confi rms that neural network based on PSO training algorithm has better performance and better ability to escape local optimum than that of BPNN.

Hybrid approaches for container traffic forecasting in the context of anomalous events: The case of the Yangtze River Delta region in the COVID-19 pandemic

“…Hua and Faghri (1994) first applied ANN to traffic prediction and, since then, more and more ANN-based forecasting models have emerged to improve traffic forecasting performance. Typical examples include Back Propagation Neural Networks (BPNN) ( Kunnapapdeelert and Thepmongkorn, 2020 ), Feed Forward Neural Networks (FFNN) ( Do et al, 2019 ), Radial Basis Function ( Zhu et al, 2014 ), and Recurrent Neural Networks (RNN) ( Li et al, 2018 ). Meanwhile, ANN has been used to compare traditional prediction models, to demonstrate the promising performance of ANN for specific applications ( Sayed and Razavi, 2000 ).…”