Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Casestudy, Vietnam

The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

1. Introduction

2. Study Area

3. Materials and Methods

3.1. Data Used

3.2. Methods Used

3.2.1. Artificial Neural Networks

3.2.2. RealAdaBoost

3.2.3. Validation Methods

3.2.4. Modeling Methodology

4. Results and Discussion

4.1. Factor Importance

4.2. Model Performance

4.3. Groundwater Potential Mapping

5. Conclusions

References

1. Carrard, N.; Foster, T.; Willetts, J. Groundwater as a Source of Drinking Water in Southeast Asia and the Pacific: A Multi-Country Review of Current Reliance and Resource Concerns. Water 2019, 11, 1605.

2. Grönwall, J.; Oduro-Kwarteng, S. Groundwater as a strategic resource for improved resilience: a case study from peri-urban Accra. Environ. Earth Sci. 2018, 77, 6.

3. Magesh, N.; Chandrasekar, N.; Soundranayagam, J.P. Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques. Geosci. Front. 2012, 3, 189–196.

4. Amarasinghe, U.A.; Smakhtin, V. Global water demand projections: past, present and future; IWMI: Colombo, Sri Lanka, 2014; Vol. 156.

5 Vo, P. Urbanization and water management in Ho Chi Minh City, Vietnam-issues, challenges and perspectives. GeoJournal 2008, 70, 75–89, doi:10.1007/s10708-008-9115-2.

6. Razavi-Termeh, S.V.; Sadeghi-Niaraki, A.; Choi, S.-M. Groundwater Potential Mapping Using an Integrated Ensemble of Three Bivariate Statistical Models with Random Forest and Logistic Model Tree Models. Water 2019, 11, 1596.

7. Rahmati, O.; Melesse, A.M. Application of Dempster–Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan, Iran. Sci. Total Environ. 2016, 568, 1110–1123, doi:/10.1016/j.scitotenv.2016.06.176.

8. Khosravi, K.; Sartaj, M.; Tsai, F.T.-C.; Singh, V.P.; Kazakis, N.; Melesse, A.M.; Prakash, I.; Bui, D.T.; Pham, B.T. A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment. Sci. Total Environ. 2018, 642, 1032–1049.

9. Oikonomidis, D.; Dimogianni, S.; Kazakis, N.; Voudouris, K. A GIS/remote sensing-based methodology for groundwater potentiality assessment in Tirnavos area, Greece. J. Hydrol. 2015, 525, 197–208.

10. Kordestani, M.D.; Naghibi, S.A.; Hashemi, H.; Ahmadi, K.; Kalantar, B.; Pradhan, B. Groundwater potential mapping using a novel data-mining ensemble model. Hydrogeology journal 2019, 27, 211-224.

11. Rahmati, O.; Choubin, B.; Fathabadi, A.; Coulon, F.; Soltani, E.; Shahabi, H.; Mollaefar, E.; Tiefenbacher, J.; Cipullo, S.; Ahmad, B.B. Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and uneec methods. Sci. Total Environ. 2019, 688, 855–866.

12. Pal, S.; Kundu, S.; Mahato, S. Groundwater potential zones for sustainable management plans in a river basin of India and Bangladesh. J. Clean. Prod. 2020, 257, 120311, doi:/10.1016/j.jclepro.2020.120311.

13. Naghibi, S.A.; Pourghasemi, H.R.; Dixon, B. GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environ. Monit. Assess. 2016, 188, 44.

14. Miraki, S.; Zanganeh, S.H.; Chapi, K.; Singh, V.P.; Shirzadi, A.; Shahabi, H.; Pham, B.T. Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach. Water Resour. Manag. 2019, 33, 281–302, doi:10.1007/s11269-018-2102-6.

15. Al-Fugara, A.k.; Ahmadlou, M.; Al-Shabeeb, A.R.; AlAyyash, S.; Al-Amoush, H.; Al-Adamat, R. Spatial mapping of groundwater springs potentiality using grid search-based and genetic algorithm-based support vector regression. Geocarto Int. 2020, 1–20, https://doi.org/10.1080/10106049.2020.1716396.

16. Naghibi, S.A.; Moghaddam, D.D.; Kalantar, B.; Pradhan, B.; Kisi, O. A comparative assessment of GIS based data mining models and a novel ensemble model in groundwater well potential mapping. J. Hydrol. 2017, 548, 471–483, doi:10.1016/j.jhydrol.2017.03.020.

17. Banadkooki, F.B.; Ehteram, M.; Ahmed, A.N.; Teo, F.Y.; Fai, C.M.; Afan, H.A.; Sapitang, M.; El-Shafie, A. Enhancement of Groundwater-Level Prediction Using an Integrated Machine Learning Model Optimized by Whale Algorithm. Nat. Resour. Res. 2020, 1–20, https://doi.org/10.1007/s11053-020-09634-2.

18. Khosravi, K.; Pham, B.T.; Chapi, K.; Shirzadi, A.; Shahabi, H.; Revhaug, I.; Prakash, I.; Bui, D.T. A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Sci. Total Environ. 2018, 627, 744–755.

19. Bui, D.T.; Hoang, N.-D. A Bayesian framework based on a Gaussian mixture model and radial-basisfunction Fisher discriminant analysis (BayGmmKda V1. 1) for spatial prediction of floods. Geosci. Model Dev. 2017, 10, 3391.

20. Chen, W.; Li, H.; Hou, E.; Wang, S.; Wang, G.; Panahi, M.; Li, T.; Peng, T.; Guo, C.; Niu, C. GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models. Sci. Total Environ. 2018, 634, 853–867.

21. Solomon, S.; Quiel, F. Groundwater study using remote sensing and geographic information systems (GIS) in the central highlands of Eritrea. Hydrogeol. J. 2006, 14, 1029–1041.

22. Jaafari, A.; Pourghasemi, H.R. Factors Influencing Regional-Scale Wildfire Probability in Iran: An Application of Random Forest and Support Vector Machine. In Spatial Modeling in GIS and R for Earth and Environmental Sciences, Elsevier: Amsterdam, Netherlands, 2019; pp. 607–619.

23. Jaafari, A.; Mafi-Gholami, D.; Pham, B.T.; Tien Bui, D. Wildfire probability mapping: bivariate vs. multivariate statistics. Remote Sens. 2019, 11, 618.

24. Ercanoglu, M.; Gokceoglu, C. Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ. Geol. 2002, 41, 720–730.

25. Moore, I.D.; Grayson, R.; Ladson, A. Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol. Process. 1991, 5, 3–30.

26. Bourque, C.P.-A.; Bayat, M. Landscape variation in tree species richness in northern Iran forests. PloS ONE 2015, 10, https://doi.org/10.1371/journal.pone.0121172.

27. Vorpahl, P.; Elsenbeer, H.; Märker, M.; Schröder, B. How can statistical models help to determine driving factors of landslides? Ecol. Model. 2012, 239, 27–39.

28. Jaafari, A. LiDAR-supported prediction of slope failures using an integrated ensemble weights-ofevidence and analytical hierarchy process. Environ. Earth Sci. 2018, 77, 42. doi:10.1007/s12665-017-7207-3.

29. Tien Bui, D.; Shirzadi, A.; Chapi, K.; Shahabi, H.; Pradhan, B.; Pham, T.B.; Singh, P.V.; Chen, W.; Khosravi, K.; Bin Ahmad, B., et al. A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping. Water 2019, 11, doi:10.3390/w11102013.

30. Jaafari, A.; Najafi, A.; Rezaeian, J.; Sattarian, A. Modeling erosion and sediment delivery from unpaved roads in the north mountainous forest of Iran. Gem. Int. J. Geomath. 2015, 6, 343–356, doi:10.1007/s13137-014-0062-4.

31. Nhu, V.-H.; Janizadeh, S.; Avand, M.; Chen, W.; Farzin, M.; Omidvar, E.; Shirzadi, A.; Shahabi, H.; Clague, J.J.; Jaafari, A., et al. GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models. Appl. Sci. 2020, 10, 2039.

32. Dinesh Kumar, P.; Gopinath, G.; Seralathan, P. Application of remote sensing and GIS for the demarcation of groundwater potential zones of a river basin in Kerala, southwest coast of India. Int. J. Remote Sens. 2007, 28, 5583–5601.

33. Lerner, D.N.; Harris, B. The relationship between land use and groundwater resources and quality. Land Use Policy 2009, 26, S265–S273.

34. Mafi-Gholami, D.; Zenner, E.K.; Jaafari, A.; Ward, R.D. Modeling multi-decadal mangrove leaf area index in response to drought along the semi-arid southern coasts of Iran. Sci. Total Environ. 2019, 656, 1326–1336, doi:10.1016/j.scitotenv.2018.11.462.

35. Mafi-Gholami, D.; Zenner, E.K.; Jaafari, A.; Bakhtiari, H.R.; Tien Bui, D. Multi-hazards vulnerability assessment of southern coasts of Iran. J. Environ. Manag. 2019, 252, 109628.

36. Pinto, D.; Shrestha, S.; Babel, M.S.; Ninsawat, S. Delineation of groundwater potential zones in the Comoro watershed, Timor Leste using GIS, remote sensing and analytic hierarchy process (AHP) technique. Appl. Water Sci. 2017, 7, 503–519.

37. Jaafari, A.; Najafi, A.; Zenner, E.K. Ground-based skidder traffic changes chemical soil properties in a mountainous Oriental beech (Fagus orientalis Lipsky) forest in Iran. J. Terramechanics 2014, 55, 39–46, doi:10.1016/j.jterra.2014.06.001.

38. Lazarevska, M.; Knezevic, M.; Cvetkovska, M.; Trombeva-Gavriloska, A. Application of artificial neural networks in civil engineering. Teh. Vjesn. 2014, 21, 135–1359.

39. Janizadeh, S.; Avand, M.; Jaafari, A.; Phong, T.V.; Bayat, M.; Ahmadisharaf, E.; Prakash, I.; Pham, B.T.; Lee, S. Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran. Sustainability 2019, 11, 5426.

40. Bayat, M.; Ghorbanpour, M.; Zare, R.; Jaafari, A.; Thai Pham, B. Application of artificial neural networks for predicting tree survival and mortality in the Hyrcanian forest of Iran. Comput. Electron. Agric. 2019, 164, 104929. doi:10.1016/j.compag.2019.104929.

41. Nasiri, V.; Darvishsefat, A.A.; Rafiee, R.; Shirvany, A.; Hemat, M.A. Land use change modeling through an integrated multi-layer perceptron neural network and Markov chain analysis (case study: Arasbaran region, Iran). J. For. Res. 2019, 30, 943–957.

42. Khashei, M.; Bijari, M. An artificial neural network (p,d,q) model for timeseries forecasting. Expert Syst. Appl. 2010, 37, 479–489, doi:/10.1016/j.eswa.2009.05.044.

43. Gardner, M.W.; Dorling, S. Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos. Environ. 1998, 32, 2627–2636.

44. Almonacid, F.; Fernandez, E.F.; Mellit, A.; Kalogirou, S. Review of techniques based on artificial neural networks for the electrical characterization of concentrator photovoltaic technology. Renew. Sustain. Energy Rev. 2017, 75, 938–953.

45. Lanillos, P.; Oliva, D.; Philippsen, A.; Yamashita, Y.; Nagai, Y.; Cheng, G. A review on neural network models of schizophrenia and autism spectrum disorder. Neural Netw. 2020, 122, 338–363.

46. Schapire, R.E.; Singer, Y. Improved boosting algorithms using confidence–rated predictions. Mach. Learn. 1999, 37, 297–336.

47. Huang, C.; Wu, B.; Haizhou, A.; Lao, S. Omni-directional face detection based on real adaboost. Proceedings of 2004 International Conference on Image Processing, Singapore, 24–27 October 2004.

48. Wu, B.; Ai, H.; Huang, C.; Lao, S. Fast rotation invariant multi-view face detection based on real adaboost. Proceedings of Sixth IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, South Korea, 19 May 2004.

49. Sajedi-Hosseini, F.; Malekian, A.; Choubin, B.; Rahmati, O.; Cipullo, S.; Coulon, F.; Pradhan, B. A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination. Sci. Total Environ. 2018, 644, 954–962.

50. Ma, M.; Liu, C.; Zhao, G.; Xie, H.; Jia, P.; Wang, D.; Wang, H.; Hong, Y. Flash Flood Risk Analysis Based on Machine Learning Techniques in the Yunnan Province, China. Remote Sens. 2019, 11, 170.

51. Abedini, M.; Ghasemian, B.; Shirzadi, A.; Shahabi, H.; Chapi, K.; Pham, B.T.; Bin Ahmad, B.; Tien Bui, D. A novel hybrid approach of bayesian logistic regression and its ensembles for landslide susceptibility assessment. Geocarto Int. 2019, 34, 1427–1457.

52. Pham, B.T.; Prakash, I.; Jaafari, A.; Bui, D.T. Spatial prediction of rainfall-induced landslides using aggregating one-dependence estimators classifier. J. Indian Soc. Remote Sens. 2018, 46, 1457–1470.

53. Nguyen, V.-T.; Tran, T.H.; Ha, N.A.; Ngo, V.L.; Nadhir, A.-A.; Tran, V.P.; Duy Nguyen, H.; MA, M.; Amini, A.; Prakash, I. GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam. Sustainability 2019, 11, 7118.

54. Pham, B.T.; Prakash, I.; Chen, W.; Ly, H.-B.; Ho, L.S.; Omidvar, E.; Tran, V.P.; Bui, D.T. A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping. Sustainability 2019, 11, 6323.

55. Bui, D.T.; Tsangaratos, P.; Ngo, P.-T.T.; Pham, T.D.; Pham, B.T. Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods. Sci. Total Environ. 2019, 668, 1038–1054.

56. Dou, J.; Yunus, A.P.; Tien Bui, D.; Sahana, M.; Chen, C.-W.; Zhu, Z.; Wang, W.; Thai Pham, B. Evaluating GIS-based multiple statistical models and data mining for earthquake and rainfall-induced landslide susceptibility using the LiDAR DEM. Remote Sens. 2019, 11, 638.

57. Pham, B.T.; Nguyen-Thoi, T.; Ly, H.-B.; Nguyen, M.D.; Al-Ansari, N.; Tran, V.-Q.; Le, T.-T. Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination. Sustainability 2020, 12, 2339.

58. Pham, B.T.; Qi, C.; Ho, L.S.; Nguyen-Thoi, T.; Al-Ansari, N.; Nguyen, M.D.; Nguyen, H.D.; Ly, H.-B.; Le, H.V.; Prakash, I. A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil. Sustainability 2020, 12, 2218.

59. Nguyen, H.Q.; Ly, H.-B.; Tran, V.Q.; Nguyen, T.-A.; Le, T.-T.; Pham, B.T. Optimization of Artificial Intelligence System by Evolutionary Algorithm for Prediction of Axial Capacity of Rectangular Concrete Filled Steel Tubes under Compression. Materials 2020, 13, 1205.

60. Dao, D.V.; Adeli, H.; Ly, H.-B.; Le, L.M.; Le, V.M.; Le, T.-T.; Pham, B.T. A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation. Sustainability 2020, 12, 830.

61. Pham, B.T.; Le, L.M.; Le, T.-T.; Bui, K.-T.T.; Le, V.M.; Ly, H.-B.; Prakash, I. Development of advanced artificial intelligence models for daily rainfall prediction. Atmos. Res. 2020, 237, 104845.

62. Ly, H.-B.; Le, L.M.; Duong, H.T.; Nguyen, T.C.; Pham, T.A.; Le, T.-T.; Le, V.M.; Nguyen-Ngoc, L.; Pham, B.T. Hybrid artificial intelligence approaches for predicting critical buckling load of structural members under compression considering the influence of initial geometric imperfections. Appl. Sci. 2019, 9, 2258.

63. Ly, H.-B.; Desceliers, C.; Minh Le, L.; Le, T.-T.; Thai Pham, B.; Nguyen-Ngoc, L.; Doan, V.T.; Le, M. Quantification of uncertainties on the critical buckling load of columns under axial compression with uncertain random materials. Materials 2019, 12, 1828.

64. Ly, H.-B.; Pham, B.T.; Dao, D.V.; Le, V.M.; Le, L.M.; Le, T.-T. Improvement of ANFIS model for prediction of compressive strength of manufactured sand concrete. Appl. Sci. 2019, 9, 3841.

65. Pham, B.T.; Phong, T.V.; Nguyen-Thoi, T.; Parial, K.; K. Singh, S.; Ly, H.-B.; Nguyen, K.T.; Ho, L.S.; Le, H.V.; Prakash, I. Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers. Geocarto Int. 2020, 1–23, https://doi.org/10.1080/10106049.2020.1737972.

66. Nguyen, P.T.; Ha, D.H.; Nguyen, H.D.; Van Phong, T.; Trinh, P.T.; Al-Ansari, N.; Le, H.V.; Pham, B.T.; Ho, L.S.; Prakash, I. Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling. Sustainability 2020, 12, 2622.

67. Thai Pham, B.; Tien Bui, D.; Prakash, I. Landslide susceptibility modelling using different advanced decision trees methods. Civ. Eng. Environ. Syst. 2018, 35, 139–157.

68. Pham, B.T. A novel classifier based on composite hyper-cubes on iterated random projections for assessment of landslide susceptibility. J. Geol. Soc. India 2018, 91, 355–362.

69. Thai Pham, B.; Shirzadi, A.; Shahabi, H.; Omidvar, E.; Singh, S.K.; Sahana, M.; Talebpour Asl, D.; Bin Ahmad, B.; Kim Quoc, N.; Lee, S. Landslide susceptibility assessment by novel hybrid machine learning algorithms. Sustainability 2019, 11, 4386.

70. Pham, B.T.; Phong, T.V.; Nguyen, H.D.; Qi, C.; Al-Ansari, N.; Amini, A.; Ho, L.S.; Tuyen, T.T.; Yen, H.P.H.; Ly, H.-B. A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping. Water 2020, 12, 239.

71. Qi, C.; Ly, H.-B.; Chen, Q.; Le, T.-T.; Le, V.M.; Pham, B.T. Flocculation-dewatering prediction of fine mineral tailings using a hybrid machine learning approach. Chemosphere 2020, 244, 125450.

72. Van Dao, D.; Jaafari, A.; Bayat, M.; Mafi-Gholami, D.; Qi, C.; Moayedi, H.; Van Phong, T.; Ly, H.-B.; Le, T.-T.; Trinh, P.T. A spatially explicit deep learning neural network model for the prediction of landslide susceptibility. Catena 2020, 188, 104451.

73. Pham, B.T.; Prakash, I. A novel hybrid model of bagging-based naïve bayes trees for landslide susceptibility assessment. Bull. Eng. Geol. Environ. 2019, 78, 1911–1925.

74. Pham, B.T.; Prakash, I. Evaluation and comparison of LogitBoost Ensemble, Fisher’s Linear Discriminant Analysis, logistic regression and support vector machines methods for landslide susceptibility mapping. Geocarto Int. 2019, 34, 316–333.

75. Ly, H.-B.; Le, L.M.; Phi, L.V.; Phan, V.-H.; Tran, V.Q.; Pham, B.T.; Le, T.-T.; Derrible, S. Development of an AI model to measure traffic air pollution from multisensor and weather data. Sensors 2019, 19, 4941.

76. Phong, T.V.; Phan, T.T.; Prakash, I.; Singh, S.K.; Shirzadi, A.; Chapi, K.; Ly, H.-B.; Ho, L.S.; Quoc, N.K.; Pham, B.T. Landslide susceptibility modeling using different artificial intelligence methods: A case study at Muong Lay district, Vietnam. Geocarto Int. 2019, 1–24, https://doi.org/10.1080/10106049.2019.1665715.

77. Pham, B.T.; Nguyen, M.D.; Bui, K.-T.T.; Prakash, I.; Chapi, K.; Bui, D.T. A novel artificial intelligence approach based on Multi-layer Perceptron Neural Network and Biogeography-based Optimization for predicting coefficient of consolidation of soil. Catena 2019, 173, 302–311.

78. Pham, B.T.; Avand, M.; Janizadeh, S.; Phong, T.V.; Al-Ansari, N.; Ho, L.S.; Das, S.; Le, H.V.; Amini, A.; Bozchaloei, S.K. GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment. Water 2020, 12, 683.

79. Dao, D.V.; Ly, H.-B.; Vu, H.-L.T.; Le, T.-T.; Pham, B.T. Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete. Materials 2020, 13, 1072.

80. Nguyen, V.V.; Pham, B.T.; Vu, B.T.; Prakash, I.; Jha, S.; Shahabi, H.; Shirzadi, A.; Ba, D.N.; Kumar, R.; Chatterjee, J.M. Hybrid machine learning approaches for landslide susceptibility modeling. Forests 2019, 10, 157.

81. Pham, B.T.; Bui, D.T.; Pham, H.V.; Le, H.Q.; Prakash, I.; Dholakia, M. 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). J. Indian Soc. Remote Sens. 2017, 45, 673–683.

82. Pham, B.T.; Tien Bui, D.; Indra, P.; Dholakia, M. Landslide susceptibility assessment at a part of Uttarakhand Himalaya, India using GIS–based statistical approach of frequency ratio method. Int J Eng Res Technol 2015, 4, 338–344.

83. Jaafari, A.; Rezaeian, J.; Omrani, M.S. Spatial prediction of slope failures in support of forestry operations safety. Croat. J. For. Eng. 2017, 38, 107–118.

84. Jaafari, A.; Zenner, E.K.; Pham, B.T. Wildfire spatial pattern analysis in the Zagros Mountains, Iran: A comparative study of decision tree based classifiers. Ecol. Inform. 2018, 43, 200–211, doi:10.1016/j.ecoinf.2017.12.006.

85. Pham, B.T.; Prakash, I.; Khosravi, K.; Chapi, K.; Trinh, P.T.; Ngo, T.Q.; Hosseini, S.V.; Bui, D.T. A comparison of Support Vector Machines and Bayesian algorithms for landslide susceptibility modelling. Geocarto Int. 2019, 34, 1385–1407.

86. Pham, B.T.; Bui, D.T.; Prakash, I.; Nguyen, L.H.; Dholakia, M. A comparative study of sequential minimal optimization-based support vector machines, vote feature intervals, and logistic regression in landslide susceptibility assessment using GIS. Environ. Earth Sci. 2017, 76, 371.

87. Pham, B.T.; Nguyen, M.D.; Van Dao, D.; Prakash, I.; Ly, H.-B.; Le, T.-T.; Ho, L.S.; Nguyen, K.T.; Ngo, T.Q.; Hoang, V. Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis. Sci. Total Environ. 2019, 679, 172–184.

88. Tien Bui, D.; Moayedi, H.; Gör, M.; Jaafari, A.; Foong, L.K. Predicting slope stability failure through machine learning paradigms. Isprs Int. J. Geo-Inf. 2019, 8, 395.

89. Moayedi, H.; Tien Bui, D.; Gör, M.; Pradhan, B.; Jaafari, A. The feasibility of three prediction techniques of the artificial neural network, adaptive neuro-fuzzy inference system, and hybrid particle swarm optimization for assessing the safety factor of cohesive slopes. Isprs Int. J. Geo-Inf. 2019, 8, 391.

90. Nguyen, P.T.; Tuyen, T.T.; Shirzadi, A.; Pham, B.T.; Shahabi, H.; Omidvar, E.; Amini, A.; Entezami, H.; Prakash, I.; Phong, T.V. Development of a novel hybrid intelligence approach for landslide spatial prediction. Appl. Sci. 2019, 9, 2824.

91. Hall, M.A. Correlation-based feature selection for machine learning. Available online: https://www.lri.fr/~pierres/donn%E9es/save/these/articles/lpr-queue/hall99correlationbased.pdf (accessed on 15 March 2020).

92. Jaafari, A.; Zenner, E.K.; Panahi, M.; Shahabi, H. Hybrid artificial intelligence models based on a neurofuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. Agric. For. Meteorol. 2019, 266–267, 198–207, doi:10.1016/j.agrformet.2018.12.015.

93. Jaafari, A.; Razavi Termeh, S.V.; Bui, D.T. Genetic and firefly metaheuristic algorithms for an optimized neuro-fuzzy prediction modeling of wildfire probability. J. Environ. Manag. 2019, 243, 358–369, doi:10.1016/j.jenvman.2019.04.117.

94. Jaafari, A.; Panahi, M.; Pham, B.T.; Shahabi, H.; Bui, D.T.; Rezaie, F.; Lee, S. 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 2019, 175, 430–445, doi:10.1016/j.catena.2018.12.033.

95. Chen, W.; Li, Y.; Tsangaratos, P.; Shahabi, H.; Ilia, I.; Xue, W.; Bian, H. Groundwater Spring Potential Mapping Using Artificial Intelligence Approach Based on Kernel Logistic Regression, Random Forest, and Alternating Decision Tree Models. Appl. Sci. 2020, 10, 425. doi:10.3390/app10020425.

96. Kalantar, B.; Al-Najjar, A.H.H.; Pradhan, B.; Saeidi, V.; Halin, A.A.; Ueda, N.; Naghibi, A.S. Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping. Water 2019, 11, 1909. doi:10.3390/w11091909.

97. Bui, D.T.; Ho, T.C.; Revhaug, I.; Pradhan, B.; Nguyen, D.B. Landslide susceptibility mapping along the national road 32 of Vietnam using GIS-based J48 decision tree classifier and its ensembles. Cartography from pole to pole, Springer: Berlin, Germany, 2014; pp. 303–317.

98. Shirzadi, A.; Bui, D.T.; Pham, B.T.; Solaimani, K.; Chapi, K.; Kavian, A.; Shahabi, H.; Revhaug, I. Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environ. Earth Sci. 2017, 76, 60. doi:10.1007/s12665-016-6374-y.

99. Chen, W.; Pradhan, B.; Li, S.; Shahabi, H.; Rizeei, H.M.; Hou, E.; Wang, S. Novel hybrid integration approach of bagging-based fisher’s linear discriminant function for groundwater potential analysis. Nat. Resour. Res. 2019, 28, 1239–1258.

100. Pham, B.T.; Jaafari, A.; Prakash, I.; Singh, S.K.; Quoc, N.K.; Bui, D.T. Hybrid computational intelligence models for groundwater potential mapping. Catena 2019, 182, 104101. doi:10.1016/j.catena.2019.104101.

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

——————————————————————————————————————————

► See detail:  Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Casestudy, Vietnam

Phong Tung Nguyen 1,*, Duong Hai Ha 2, Abolfazl Jaafari 3, Huu Duy Nguyen 4, Tran Van Phong 5, Nadhir Al-Ansari 6,*, Indra Prakash 7, Hiep Van Le 8,* and Binh Thai Pham 9

1 Vietnam Academy for Water Resources, Hanoi 100000, Vietnam
2 Institute for Water and Environment, Hanoi 100000, Vietnam; hahaiduongcwe@yahoo.com
3 Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), P.O. Box 64414-356 Tehran, Iran; ajaafari@gmail.com
4 Faculty of Geography, VNU University of Science, Vietnam National University, 334 Nguyen Trai, Hanoi 100000, Vietnam; huuduy151189@gmail.com
5 Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi 100000, Vietnam; tphong1617@gmail.com
6 Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87 Lulea, Sweden
7 Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382002, India; indra52prakash@gmail.com
8 Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam; levanhiep2@duytan.edu.vn
9 University of Transport Technology, Hanoi 100000, Vietnam; binhpt@utt.edu.vn
* Correspondence: phongicd@gmail.com (P.T.N.); nadhir.alansari@ltu.se (N.A.-A.); levanhiep2@duytan.edu.vn (H.L.V.)

International Journal of Environment Research and Public Health