Cost and Risk Prediction in Road Transportation of Hazmat by ANFIS Trained with PSO, FA, HBBO and ICA

This paper proposes adaptive neuro-fuzzy inference system (ANFIS) to predict the risk with its aggregated cost (CR) of an accident in road transportation hazardous material, aim is provide a more accurate and reliable data for safety transportation. The determination index by conventional methods such as Risk graphs deterministic approaches may lead imprecise values due uncertainties, both parameters models. proposed technique hybrid schema, which combines main advantageous fuzzy logic (address uncertainties) neural network (learn from given data). In other hand our study seeks tune model particle swarm optimization (PSO), firefly algorithm (FA), imperialist competitive (ICA) human based-behavior (HBBO) hence optimize performance ANFIS. simulation result this work comparative analysis shows that ANFIS yield height ANFIS-PSO was outstanding one training phase, while ANFIS-FA gives better results testing process.