Dieu Tien Bui

Førsteamanuensis

Handelshøyskolen
Institutt for økonomi og IT
Campus Bø (1-324)
Jeg har hatt stillingen som førsteamanuensis i Geografisk Informasjonssystemer (GIS) siden April 2014, ved Institutt for økonomi og IT, Handelshøyskolen, Universitetet i Sørøst-Norge (USN). Jeg underviser og veileder studenter i Landmåling, Fjernanalyse, Database og Prosjektarbeid i GIS.

Ansvarsområder

Undervisning/veiledning

Hoved-forskningsområder (se på liste over prosjekter i Researchgate)

  • Geospatial kunstig intelligens, maskinlæring
  • GIS, geodata, digital kartografi, stordata
  • Fjernanalyse, GNSS
  • Drone fotogrammetri
  • Naturfare og miljøproblemer (jordskred, flom, erosjon, skogbrann, biomasse, jordssalt, jordbruk og klimaendringer)
  • Ingeniørgeologi, strukturelle forskyvninger

Forskningsgruppe

Vitenskapelig redaktør

Anmeldelse

PhD medveileder

  • Jasmine Anastasia Hayes, University of South-Eastern Norway, Bø i Telemark, Norway (2018-2021)
  • Nguyen Quang Minh, Hanoi University of Mining and Geology, Hanoi, Vietnam (2018-2021)
  • Nguyen Thanh Bang, Vietnam Institute of Meteorology, Hydrology and Climate Changes, Hanoi, Vietnam (2018-2021)
  • Gian Quoc Anh, VNU University of Engineering and Technology, Hanoi, Vietnam (2017-2020)
  • Ngo Thi Phuong Thao, Hanoi University of Mining and Geology, Hanoi, Vietnam (2017-2020)
  • Nguyen Thanh Le, Hanoi University of Mining and Geology, Hanoi, Vietnam (2017-2020)

Kompetanse

Publikasjoner

Utvalg publikasjoner (Se alle publikasjonene: (1) CRISTIN databasen; (2) Researchgate; og (3) Google Scholar)

Tidskrifters artikler (SCI, SCIE, Web of Science)

  1. Rahmati, Omid; Kornejady, Aiding; Samadi, Mahmood; Deo, Ravinesh C.; Conoscenti, Christian; Lombardo, Luigi; Dayal, Kavina; Taghizadeh-Mehrjardi, Ruhollah; Pourghasemi, Hamid Reza; Kumar, Sandeep; Tien Bui, Dieu. 2019. PMT: new analytical framework for automated evaluation of geo-environmental modelling approaches. Science of the Total Environment
  2. Dou, Jie; Yunus, Ali P.; Tien Bui, Dieu; Merghadi, Abdelaziz; Sahana, Mehebub; Zhu, Zhongfan; Chen, Chi-Wen; Khosravi, Khabat; Yang, Yong; Pham, Binh Thai, 2019. Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Science of the Total Environment
  3. Hoa, PV., Giang, NV., Binh, NA., Hai, LVH., Pham, TD., Hasanlou, M., Tien Bui, D. 2019. Soil Salinity Mapping Using SAR Sentinel-1 Data and Advanced Machine Learning Algorithms: A Case Study at Ben Tre Province of the Mekong River Delta (Vietnam). Remote Sensing ;Volum 11.(2)
  4. Jaafari, A.,; Panahi, M., Pham, BT., Shahabi, H., Tien Bui, D., Rezaie, F., Lee, S., 2019. Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility. Catena ;Volum 175. s. 430-445
  5. Wang,Y., Hong,H.,Chen, W., Li, S., Pamučar, D., Gigović, L., Drobnjak, S., Tien Bui, D., Duan, H.,, 2019.  A Hybrid GIS Multi-Criteria Decision-Making Method for Flood Susceptibility Mapping at Shangyou, China. Remote Sensing 
  6. Khosravi, K., Pham, B.T., Chapi, K., Shirzadi, A., Shahabi, H., Revhaug, I., Prakash, I. and Tien Bui, D., 2018. A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of The Total Environment, 627, 744-755.
  7. Khosravi, K., Panahi, M., and Tien Bui, D. 2018. A comprehensive study of new hybrid models for Adaptive Neuro-Fuzzy Inference System (ANFIS) with Invasive Weed Optimization (IWO), Differential Evolution (DE), Firefly (FA), Particle Swarm Optimization (PSO) and Bees (BA) algorithms for spatial prediction of groundwater spring potential mapping.  Hydrology and Earth System Sciences Discussion. 2018:1-45.
  8. Vafaei, S., Soosani, J., Adeli, K., Fadaei, H., Naghavi, H., Pham, T.D. and Tien Bui, D., 2018. Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran). Remote Sensing, 10(2), 172.
  9. Dang, V.H., Tien Bui, D., Tran, X.L., Hoang, N.D., 2018. Enhancing Accuracy of Rainfall-Induced Landslide Prediction along Mountain Road with GIS-Based Random Forest Classifier. Bulletin of Engineering Geology and the Environment (Accepted, In press)
  10. Pham, T.D, Le, N.N, Yoshino, K., Tien Bui, D., 2018. Estimating Aboveground Biomass of Mangrove Plantation in the Northern coast of Vietnam using machine learning techniques with an integration of ALOS-2 PALSAR-2 and Sentinel-2A data. International Journal of Remote Sensing (Accepted, In press). DOI:10.1080/01431161.2018.1471544
  11. Hoang, N-D., Nguyen, Q-L., Tien Bui, D., 2018. Image Processing Based Classification of Asphalt Pavement Cracks Using Support Vector Machine Optimized by Artificial Bee Colony. Journal of Computing in Civil Engineering (Accepted, In press)
  12. Hoang, N-D., Tien Bui, D., 2018. Spatial Prediction of Rainfall-Induced Shallow Landslides Using Gene Expression Programming Integrated with GIS: A Case Study in Vietnam. Natural Hazards. Doi:10.1007/s11069-018-3286-z
  13. Pham, B.T.,  Abolfazl,J., Prakash, I., Tien Bui, D., (2018). A Novel Hybrid Intelligent Model of Support Vector Machines and MultiBoost Ensemble for Landslide Susceptibility Modeling. Bulletin of Engineering Geology and the Environment (Accepted, In press)
  14. Pham, T.D., Tien Bui, D., Yoshino, K., Le, N.N (2018). Optimized Rule-Based Logistic Model Tree Algorithm for Mapping Mangrove Species Using ALOS PALSAR Imagery and GIS in the Tropical Region. Environmental Earth Sciences. 77:159  
  15. Pham, T.B., Prakash, I., Tien Bui, D., (2018). Bagging based Support Vector Machines for spatial prediction of landslides. Environmental Earth Sciences. 77:146
  16. Chen, W., Xie, X., Peng, J., Shahabi, H., Hong, H., Tien Bui, D., Duan, Z., Li, S. and Zhu, A.X., 2018. GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. CATENA, 36, 135-149
  17. Hong, H., Liu, J., Tien Bui, D., Pradhan, B., Acharya, T.D., Pham, B.T., Zhu, A.X., Chen, W. and Ahmad, B.B., 2018. Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). CATENA, 163, 399-413
  18. Tien Bui, D., Hoang, N-D., (2017). A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods. Geoscientific Model Development  , 10, 3391-3409
  19. Chapi, K., Singh, VP., Shirzadi, A., Shahabi, H., Tien Bui, D., Pham, BT., Khosravi K.,(2017) A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environmental Modelling & Software, 95:229-45
  20. Shirzadi, A., Shahabi, H., Chapi, K., Tien Bui, D., Pham, B.T., Shahedi, K. and Ahmad, B.B., (2017). A comparative study between popular statistical and machine learning methods for simulating volume of landslides. CATENA, 157, 213-226
  21. Quoc Anh, G., Tran, D-T., Nguyen, D-C., Nhu,VH., Tien Bui, D., (2017). Design and Implementation of Site-Specific Rainfall- Induced Landslide Early Warning and Monitoring System: A Case study at Nam Dan landslide (Vietnam). Geomatics, Natural Hazards and Risk. 8(2), 1978-1996.
  22. Pham, T.B., Shirzadi, A., Tien Bui, D.,Prakash, I., Dholakia, M.B (2017). A Hybrid Machine Learning Ensemble Approach Based on a Radial Basis Function Neural Network and Rotation Forest for Landslide Susceptibility Modeling: A Case Study in the Himalayan Area, India. International Journal of Sediment Research
  23. Hong, H., Liu,J., Zhu, A.X., Shahabi, H., Pham, T.B., Chen, W., Pradhan, B., Tien Bui, D. (2017). A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China). Environmental Earth Sciences. 76:652.
  24. Nguyen, QK., Tien Bui, D., Hoang, N., Trinh, P., Nguyen, V., Yilmaz, I (2017). A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides Using GIS. Sustainability. 9(5), 813
  25. Were, K., Tien Bui, D., Dick, Ø.B., Singh, B. R., (2017). A Novel Evolutionary Genetic Optimization-Based Adaptive Neuro-Fuzzy Inference System and GIS Predict and Map Soil Organic Carbon Stocks across an Afromontane Landscape. Pedosphere. 5, 877-889
  26. Pham,BT., Tien Bui,D., Prakash,I., Nguyen,LH., Dholakia.,MB (2017). A Comparative Study of Sequential Minimal Optimization Based Support Vector Machines, Vote Feature Intervals and Logistic Regression in Landslide Susceptibility Assessment Using GIS. Environmental Earth Sciences. 76:371
  27. Shirzadi, A., Tien Bui, D., Pham, T-B., Solaimani, K., Chapi, K., Kavian, A., Shahabi, H., Revhaug, I., (2017). Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environmental Earth Sciences. 76:60
  28. Chen,W., Xie, X., Wang, J., Pradhan, B., Hong, H., Tien Bui, D., Duan, Z., Ma, J.(2017). A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena 151, 147-160
  29. Pham, T.D., Yoshino, K., Tien Bui, D., (2017). Biomass Estimation of Sonneratia caseolaris (l.) Engler at A Coastal Area of Hai Phong city (Vietnam) using ALOS-2 PALSAR Imagery and GIS-Based Multi-Layer Perceptron Neural Networks. GIScience & Remote Sensing. 54(3), 329-353
  30. Tien Bui, D., Bui, Q-T., Nguyen, Q-P., Pradhan, B., Trinh, PT., (2016). A Hybrid Artificial Intelligence Approach Using GIS-Based Neural-Fuzzy Inference System and Particle Swarm Optimization for Forest Fire Susceptibility Modeling at A Tropical Area. Agricultural and Forest Meteorology. 233, 32-44
  31. Pham, BT., Tien Bui, D., Dholakia, M.B., Prakash, I., Pham, V-H., Mehmood, K., Le, Q-H., 2016. A Novel Ensemble Classifier of Rotation Forest and Naïve Bayer for Landslide Susceptibility Assessment at the Luc Yen District, Yen Bai Province (Viet Nam) Using GIS. Geomatics, Natural Hazards and Risk. 8(2), 649-671
  32. Bui, K.T.T., Tien Bui, D., Zou, J., Doan, C.V., Revhaug, I., (2016). A Novel Hybrid Artificial Intelligent Approach Based on Neural Fuzzy Inference Model and Particle Swarm Optimization for Horizontal Displacement Modeling of Hydropower Dam. Neural Computing and Applications. Doi:10.1007/s00521-016-2666-0
  33. Hong, H., Pradhan, B., Tien Bui., D; Xu, C., Youssef, A.M., Chen, W., (2016). Comparison of Four Kernel Functions Used in Support Vector Machines for Landslide Susceptibility Mapping: A Case Study at Suichuan area (China). Geomatics, Natural Hazards and Risk. 8(2), 544-569.
  34. Chen, W., Wang, J., Xie, X., Hong, H., Van Trung, N., Tien Bui, D., Wang, G., & Li, X. (2016). Spatial prediction of landslide susceptibility using integrated frequency ratio with entropy and support vector machines by different kernel functions. Environmental Earth Sciences, 75 (20), 1344
  35. Pham, BT., Tien Bui, D., Prakash, I., Dholakia, M.B., (2016). Hybrid Integration of Multilayer Perceptron Neural Networks and Machine Learning Ensembles for Landslide Susceptibility Assessment at Himalayan Area (India) using GIS. CATENA. 149, 52-63
  36.  Pham, BT., Tien Bui, D., Pham, V-H., Le, Q-H., Prakash, I., Dholakia, M.B., (2016). Landslide Hazard Assessment Using Random SubSpace fuzzy rules based Classifier Ensemble and Probability Analysis of Rainfall Data: A Case Study at Mu Cang Chai District, Yen Bai province (Viet Nam). Journal of the Indian Society of Remote Sensing. 45, 673–683
  37. Hoang, N.-D., Tien Bui, D., 2016. Predicting Earthquake-Induced Soil Liquefaction Based on a Hybridization of Kernel Fisher Discriminant Analysis and Least Squares Support Vector Machine: A Multi-Dataset Study. Bulletin of Engineering Geology and the Environment. 77, 191–204
  38. Tien Bui, D., Ho, TC., Pradhan, B., Pham, BT., Nhu, VH., Revhaug, I., (2016). GIS-Based Modeling of Rainfall-Induced Landslides Using Data Mining Based Functional Trees Classifier with AdaBoost, Bagging, and MultiBoost Ensemble Frameworks. Environmental Earth Sciences. 75:1101
  39. Pham, BT., Pradhan, B., Tien Bui, D, Prakash, I., Dholakia, M.B., (2016). A Comparative Study of Different Machine Learning Methods for Landslide Susceptibility Assessment: A Case Study of Uttarakhand Area (India). Environmental Modelling and Software. 84, 240-250
  40. Tien Bui, D., Pradhan, B., Nampak, H., Quang Bui, T., Tran, Q.-A., Nguyen, Q.P., (2016). Hybrid Artificial Intelligence Approach Based on Neural Fuzzy Inference Model and Metaheuristic Optimization for Flood Susceptibility Modelling in A High-Frequency Tropical Cyclone Area using GIS. Journal of Hydrology, 540, 317-330
  41. Hoang, N.-D., Tien Bui, D., & Liao, K.-W. (2016). Groutability estimation of grouting processes with cement grouts using Differential Flower Pollination Optimized Support Vector Machine. Applied Soft Computing, 45, 173-186
  42. Tien Bui, D., Le, T.K.T., Nguyen, VC., Le, DH., Revhaug, I., (2016). Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression. Remote Sensing. 8(4), 347
  43. Tien Bui, D., Tuan, TA., Hoang, ND., Thanh, NQ., Nguyen, BD., Liem, NV., Pradhan,B., (2016).  Spatial Prediction of Rainfall-induced Landslides for the Lao Cai area (Vietnam) Using a Novel hybrid Intelligent Approach of Least Squares Support Vector Machines Inference Model and Artificial Bee Colony OptimizationLandslides. 14, 447–458
  44. Tien Bui, D., Nguyen, QP., Hoang, ND., Klempe, H., (2016).  A Novel Fuzzy K-Nearest Neighbor Inference model with Differential Evolution for Spatial Prediction of Rainfall-Induced Shallow Landslides in a Tropical Hilly Area using GIS. Landslides. 14, 1–17
  45. Tien Bui, D., Pham, BT., Nguyen, Q.P., Hoang, ND. (2016). Spatial Prediction of Rainfall-Induced Shallow Landslides Using Hybrid Integration Approach of Least Squares Support Vector Machines and Differential Evolution Optimization: A Case Study in Central Vietnam. International Journal of Digital Earth. 9, 1077-1097
  46. Pham, BT., Tien Bui, D., Dholakia, M.B., Prakash, I., Pham, HV. (2016). Rotation forest fuzzy rule-based classifier ensemble for spatial prediction of landslides using GIS. Natural Hazards. 83, 97–127
  47. Hong, H., Chen,W.,Xu, C.,Youssef, A., Pradhan, B., Tien Bui, D.(2016). Rainfall-Induced Landslide Susceptibility Assessment at the Chongren Area (China) using Frequency Ratio, Certainty Factor, and Index of Entropy. Geocarto International. 32, 139-154
  48. Pham, T-B., Tien Bui, D., Pourghasemi, H., Prakash,I., Dholakia, M.B., (2015). Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of Naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theoretical and Applied Climatology. 128, 255–273
  49. Hong, H., Pradhan, B., Jebur, M., Tien Bui, D., Xu, C. & Akgun, A., (2015). Spatial prediction of landslide hazard at the Luxi area (china) using support vector machines. Environmental Earth Sciences, 75 (1), 1-14
  50. Hoang, N-D., Tien Bui, D. (2015). Novel Relevance Vector Machine Classifier with Cuckoo Search Optimization for Spatial Prediction of Landslides. Journal of Computing in Civil Engineering. DOI: 10.1061/(ASCE)CP.1943-5487.0000557
  51. Dou, J., Tien Bui, D., Yunus, A. P., Jia, K., Song, X., Revhaug, I., Xia, H., and  Zhu, Z. (2015). Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan. PloS one, 10(7), e0133262
  52. Hong, H., Pradhan, B., Xu, C., and Tien Bui, D., (2015). Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. CATENA, 133, 266-281
  53. Tien Bui, D., Tuan, T. A., Klempe, H., Pradhan, B., & Revhaug, I. (2015). Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides. 13, 361–378
  54. Were, K., Tien Bui, D., Dick, Ø. B., & Singh, B. R. (2015). A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators, 52, 394-403
  55. Tien Bui, D., Tran, C-T., Pradhan, B., Revhaug.I, Seidu, R., (2015). iGeoTrans –  An iOS application in geosciences. Geocarto International. 30, 202-217
  56. Tien Bui, D., Pradhan, B., Revhaug. I, Nguyen, D.B., Pham, V.H., Bui, Q.N., (2015). A novel hybrid evidential belief function based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam). Geomatics, Natural Hazards and Risk, 6, 243-271
  57. Tien Bui, D., Pradhan, B., Lofman, O., Revhaug, I., Dick, O.B., (2013). Regional prediction of landslide hazard in the Hoa Binh province (Vietnam) using probability analysis of intense rainfallNatural Hazards, 2, 707-730
  58. Tien Bui, D., Pradhan, B., Lofman, O., Revhaug, I., Dick, O.B., (2012). Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Computers & Geosciences, 45,199-21
  59. Tien Bui, D., Pradhan, B., Lofman, O., Revhaug, I., Dick, O.B., (2012). Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg-Marquardt and Bayesian regularized neural networks. Geomorphology, 171–172,12–29
  60. Tien Bui, D., Pradhan, B., Lofman, O., Revhaug, I., (2012). Landslide susceptibility assessment in Vietnam using Support vector machines, Decision tree and Naïve Bayes models. Mathematical Problems in Engineering. Open Access. DOI: 10.1155/2012/974638
  61. Tien Bui, D., Pradhan, B., Lofman, O., Revhaug, I., Dick, O.B., (2012). Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. CATENA 96, 25-40
  62. Tien Bui, D., Lofman, O., Revhaug, I., Dick, O., (2011). Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Natural Hazards, 59, 1413–1444

Book chapter

  1. D Tien Bui, QL Nguyen, XN Bui, VN Nguyen, CV Pham, CV Le, PTT Ngo, TD Bui,B Kristoffersen (2017). Lightweight Unmanned Aerial Vehicle and Structure-from-Motion Photogrammetry for Generating Digital Surface Model for Open-Pit Coal Mine Area and Its Accuracy Assessment. In Advances and Applications in Geospatial Technology and Earth Resources. Springer International Publishing AG
  2. ND Hoang, D Tien Bui (2017). GIS-Based Landslide Spatial Modeling Using Batch-Training Back-Propagation Artificial Neural Network: A Study of Model Parameters. In Advances and Applications in Geospatial Technology and Earth Resources. Springer International Publishing AG
  3. VN Nguyen, D Tien Bui, PTT Ngo, QP Nguyen, VC Nguyen, QL Nguyen, I Revhaug (2017). Integration of Least Squares Support Vector Machines and Firefly Optimization Algorithm for Flood Susceptible Modeling Using GIS. In Advances and Applications in Geospatial Technology and Earth Resources. Springer International Publishing AG
  4. Pradhan, B., Kalantar, B., Abdulwahid, W.M., and Tien Bui. D.(2017). Debris Flow Susceptibility Assessment Using Airborne Laser Scanning Data. In Laser Scanning Applications in Landslide Assessment (279-296). Springer International Publishing.
  5. TV Nguyen, NV Nguyen, HTT Le, HP La, D Tien Bui (2017). Detection and Prediction of Urban Expansion of Hanoi Area (Vietnam) Using SPOT-5 Satellite Imagery and Markov Chain Model. In Advances and Applications in Geospatial Technology and Earth Resources. Springer International Publishing AG
  6. D Tien Bui, KTT Bui, QT Bui, CV Doan, ND Hoang (2017). Hybrid Intelligent Model Based on Least Squared Support Vector Regression and Artificial Bee Colony Optimization for Time Series Modeling and Forecasting Horizontal Displacement of Hydropower Dam. In Handbook of Neural Computation (1st Edition). Academic Press (Elsevier)
  7. ND Hoang, D Tien Bui (2017). Slope Stability Evaluation Using Radial Basis Function Neural Network, Least Squares Support Vector Machines, and Extreme Learning Machine. In Handbook of Neural Computation (1st Edition). Academic Press (Elsevier)
  8. Hong, H., Xu, C., Revhaug, I., & Tien Bui, D. (2015). Spatial Prediction of Landslide Hazard at the Yihuang Area (China): A Comparative Study on the Predictive Ability of Backpropagation Multi-layer Perceptron Neural Networks and Radial Basic Function Neural Networks. In Cartography-Maps Connecting the World (pp. 175-188). Springer International Publishing
  9. Tien Bui, D., Pradhan, B., Ho, T.C., Revhaug, I., Nguyen, D.B., 2013. Landslide Susceptibility Mapping along the National Road 32 of Vietnam Using GIS-based J48 Decision Tree Classifier and Its Ensembles. In M. Buchroithner et al. (eds.). Lecture Notes in Geoinformation and Cartography, Volume “Cartography from Pole to Pole”, Springer-Verlag Berlin Heidelberg. ISBN: 978-3-642-32617-2.
  10. Tien Bui, D.,Pradhan, B., Revhaug.I, Tran, C-T., 2014. A Comparative Assessment Between the Application of Fuzzy Unordered Rules Induction Algorithms and J48 Decision Tree Models in Spatial Prediction of Shallow Landslide at Lang Son city (Vietnam).  In Mukherjee et al. (eds). “Remote Sensing Applications in Environmental Research”, Springer Verlag. ISBN: 978-3-319-05905-1

Book

  1. Tien Bui, D., Ngoc Do, A., Bui, H.-B., Hoang, N.-D. (Eds.), 2018. Advances and Applications in Geospatial Technology and Earth Resources. Proceedings of the International Conference on Geo-Spatial Technologies and Earth Resources 2017. Springer International Publishing.

  2. Samui, P., Tien Bui, D., Chakraborty, S., Deo, R.C. (Eds.), 2019. Handbook of Probabilistic Models for Engineers and Scientist. Elsevier (Book contract singed, planned to publish in February, 2019)

  3. Tien Bui, D., Martinez-Alvarez, F. (Eds.), 2019. Machine Learning for Natural Hazards. CRC Press, Taylor & Francis Group (Book contract singed, planned to publish in March, 2019).