1. Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction
- Author
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Hai-Bang Ly, Binh Thai Pham, Tien-Thinh Le, Lanh Si Ho, Dieu Tien Bui, Nguyen Trung Thang, Hoang-Long Nguyen, Thanh-Hai Le, and Le Hoang Son
- Subjects
Coefficient of determination ,010504 meteorology & atmospheric sciences ,Mean squared error ,0211 other engineering and technologies ,02 engineering and technology ,lcsh:Technology ,01 natural sciences ,lcsh:Chemistry ,021105 building & construction ,Genetic algorithm ,International Roughness Index ,General Materials Science ,Firefly algorithm ,ANFIS ,lcsh:QH301-705.5 ,Instrumentation ,0105 earth and related environmental sciences ,Mathematics ,Fluid Flow and Transfer Processes ,Adaptive neuro fuzzy inference system ,Artificial neural network ,particle swarm optimization ,lcsh:T ,Process Chemistry and Technology ,General Engineering ,Particle swarm optimization ,lcsh:QC1-999 ,Computer Science Applications ,machine learning ,lcsh:Biology (General) ,lcsh:QD1-999 ,lcsh:TA1-2040 ,lcsh:Engineering (General). Civil engineering (General) ,ANN ,Algorithm ,lcsh:Physics - Abstract
The International Roughness Index (IRI) is the one of the most important roughness indexes to quantify road surface roughness. In this paper, we propose a new hybrid approach between adaptive network based fuzzy inference system (ANFIS) and various meta-heuristic optimizations such as the genetic algorithm (GA), particle swarm optimization (PSO), and the firefly algorithm (FA) to develop several hybrid models namely GA based ANGIS (GANFIS), PSO based ANFIS (PSOANFIS), FA based ANFIS (FAANFIS), respectively, for the prediction of the IRI. A benchmark model named artificial neural networks (ANN) was also used to compare with those hybrid models. To do this, a total of 2811 samples in the case study of the north of Vietnam (Northwest region, Northeast region, and the Red River Delta Area) within the scope of management of the DRM-I Department were used to validate the models in terms of various criteria like coefficient of determination (R) and the root mean square error (RMSE). Experimental results affirmed the potentiality and effectiveness of the proposed prediction models whereas the PSOANFIS (RMSE = 0.145 and R = 0.888) is better than the other models named GANFIS (RMSE = 0.155 and R = 0.872), FAANFIS (RMSE = 0.170 and R = 0.849), and ANN (RMSE = 0.186 and R = 0.804). The results of this study are helpful for accurate prediction of the IRI for evaluation of quality of road surface roughness.
- Published
- 2019
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