Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction
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 al...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/9/21/4715 |
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| author | Hoang-Long Nguyen Binh Thai Pham Le Hoang Son Nguyen Trung Thang Hai-Bang Ly Tien-Thinh Le Lanh Si Ho Thanh-Hai Le Dieu Tien Bui |
| author_facet | Hoang-Long Nguyen Binh Thai Pham Le Hoang Son Nguyen Trung Thang Hai-Bang Ly Tien-Thinh Le Lanh Si Ho Thanh-Hai Le Dieu Tien Bui |
| author_sort | Hoang-Long Nguyen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T02:43:13Z |
| format | Article |
| id | doaj.art-4c90e9916b254eecb13499dcc9ffc5e2 |
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| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-12T02:43:13Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4c90e9916b254eecb13499dcc9ffc5e22022-12-22T00:41:06ZengMDPI AGApplied Sciences2076-34172019-11-01921471510.3390/app9214715app9214715Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index PredictionHoang-Long Nguyen0Binh Thai Pham1Le Hoang Son2Nguyen Trung Thang3Hai-Bang Ly4Tien-Thinh Le5Lanh Si Ho6Thanh-Hai Le7Dieu Tien Bui8University of Transport Technology, Hanoi 100000, VietnamGeotechnical Engineering and Artificial Intelligence Research Group (GEOAI), University of Transport Technology, Hanoi 100000, VietnamVNU Information Technology Institute, Vietnam National University, Hanoi 100000, VietnamVNU University of Science, Vietnam National University, Hanoi 100000, VietnamUniversity of Transport Technology, Hanoi 100000, VietnamInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamDepartment of Civil and Environmental Engineering, Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima-Shi, Hiroshima 739-8527, JapanUniversity of Transport Technology, Hanoi 100000, VietnamGeographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City 700000, VietnamThe 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.https://www.mdpi.com/2076-3417/9/21/4715international roughness indexanfismachine learningannparticle swarm optimization |
| spellingShingle | Hoang-Long Nguyen Binh Thai Pham Le Hoang Son Nguyen Trung Thang Hai-Bang Ly Tien-Thinh Le Lanh Si Ho Thanh-Hai Le Dieu Tien Bui Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction Applied Sciences international roughness index anfis machine learning ann particle swarm optimization |
| title | Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction |
| title_full | Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction |
| title_fullStr | Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction |
| title_full_unstemmed | Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction |
| title_short | Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction |
| title_sort | adaptive network based fuzzy inference system with meta heuristic optimizations for international roughness index prediction |
| topic | international roughness index anfis machine learning ann particle swarm optimization |
| url | https://www.mdpi.com/2076-3417/9/21/4715 |
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