Development and application of hybrid artificial intelligence models for groundwater potential mapping and assessment

Duong Hai Ha, Huong Thi Thanh Ngo, Tran Van Phong, Nguyen Duc Dam, Mohammadtaghi Avand, Huu Duy Nguyen, Mahdis Amiri, Hiep Van Le, Indra Prakash, Binh Thai Pham
Author affiliations

Authors

  • Duong Hai Ha Institute for Water and Environment, Hanoi 100000, Vietnam
  • Huong Thi Thanh Ngo University of Transport Technology, Hanoi 100000, Vietnam
  • Tran Van Phong Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, Hanoi, Vietnam
  • Nguyen Duc Dam University of Transport Technology, Hanoi 100000, Vietnam
  • Mohammadtaghi Avand Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran 14115-111, Iran
  • Huu Duy Nguyen Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, Vietnam
  • Mahdis Amiri Gorgan University of Agricultural Sciences & Natural Resources, Department of Watershed & Arid
  • Hiep Van Le University of Transport Technology, Hanoi 100000, Vietnam
  • Indra Prakash Dy. Director General (R), Geological Survey of India, Gandhinagar (Guj.) 382002, India
  • Binh Thai Pham University of Transport Technology, Hanoi 100000, Vietnam

DOI:

https://doi.org/10.15625/2615-9783/17240

Keywords:

Groundwater potential mapping, Random Forest, artificial intelligence, hybrid models, Vietnam

Abstract

Groundwater potential assessment is essential for optimum utilization and recharge of groundwater resources for the proper development and management of an area. The main aim of this study is to develop an accurate groundwater potential map of the Dak Nong Province (Vietnam) using hybrid artificial intelligence models, which are a combination of Random Forest (RF) and its Ensemble Framework (AdaBoost - ABRF, Bagging - BRF and LogitBoost - LBRF). In this study, twelve conditioning factors, namely topography (aspect, elevation, Topographic Wetness Index - TWI, slope, and curvature), hydrology (infiltration and river density, rainfall, Sediment Transport Index - STI, Stream Power Index - SPI), land use, and soil were used to develop the models. Well, yield data was also utilized to develop and validate potential groundwater zones.

One Rule (R) feature selection method was utilized to prioritize the importance of groundwater potential affecting parameters. The results indicated that the Average Merit (AM) of the rainfall factor was the highest (68.039), and river density was the lowest (53,969). Performance evaluation of ML models was done using standard statistical indicators, including Area Under the Receiver Operating Characteristic (ROC) curve (AUC). The results showed that all the four models performed well in the training (AUC ≥ 0.967) and testing (AUC ≥ 0.734) phases, but the performance of the ABRF (AUC=0.992) model is the best in the training phase, whereas LBRF is the best in the testing phase (AUC=0.776). The present model study would be helpful in the proper groundwater potential assessment and management of groundwater resources for sustainable development.  

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References

An T.-K., Kim M.-H., 2010. A new diverse AdaBoost classifier, 2010 International Conference on Artificial Intelligence and Computational Intelligence. IEEE, 359-363.

Arkoprovo B., Adarsa J., Prakash S.S., 2012. Delineation of groundwater potential zones using satellite remote sensing and geographic information system techniques: a case study from Ganjam district. Orissa. India. Research Journal of Recent Sciences, 1(9), 59-66.

Avand M., et al., 2020a. A Tree-based Intelligence Ensemble Approach for Spatial Prediction of Potential Groundwater. International Journal of Digital Earth, 13(12), 1408-1429.

Avand M., et al., 2019. A Comparative Assessment of Random Forest and k- Nearest Neighbor Classifiers for Gully Erosion Susceptibility Mapping. Water, 11(10), 2076.

Avand M., et al., 2020b. A tree-based intelligence ensemble approach for spatial prediction of potential groundwater. International Journal of Digital Earth, 1-22.

Barzegar R., Asghari Moghaddam A., Adamowski J., Nazemi A., 2019. Delimitation of groundwater zones under contamination risk using a bagged ensemble of optimized DRASTIC frameworks. Environmental Science and Pollution Research, 26, 1-15.

Binh Thai P., et al., 2019. A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping, Sustainability, 11(22), 6323.

Bonham-Carter G.F., 2014. Geographic information systems for geoscientists: modelling with GIS. Elsevier.

Bourque C.P.-A., Bayat M., 2015. Landscape variation in tree species richness in northern Iran forests. PloS one, 10(4), e0121172.

Breiman L., 1996. Bagging predictors. Machine Learning, 24, 123-140.

Breiman L., 2001. Random forests. Machine Learning, 45, 5-32.

Bui Q.-T., et al., 2020. Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping. Journal of Hydrology, 581, 124379.

Catani F., Lagomarsino D., Segoni S., Tofani V., 2013. Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Natural Hazards and Earth System Sciences, 13, 2815.

Chai T., Draxler R.R., 2014. Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7, 1247-1250.

Chen W., et al., 2018. GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models. Science of the Total Environment, 634, 853-867.

Chen W., Pourghasemi H.R., Zhao Z., 2017. A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping. Geocarto International, 32, 367-385.

Chen W., et al., 2019a. Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis. Natural Resources Research, 28(4), 1239-1258.

Chen W., et al., 2019b. Novel hybrid integration approach of bagging-based fisher’s linear discriminant function for groundwater potential analysis. Natural Resources Research, 28, 1239-1258.

El-Hoz M., Mohsen A., Iaaly A., 2014. Assessing groundwater quality in a coastal area using the GIS technique. Desalination and Water Treatment, 52, 1967-1979.

Elmahdy S.I., Mohamed M.M., 2014. Groundwater potential modelling using remote sensing and GIS: a case study of the Al Dhaid area, United Arab Emirates. Geocarto International, 29, 433-450.

Ganapuram S., Kumar G.T.V., Krishna I.V.M., Kahya E., Demirel M.C., 2009. Mapping of groundwater potential zones in the Musi basin using remote sensing data and GIS. Advances in Engineering Software, 40, 506-518.

Ha D.H., et al., 2021. Quadratic discriminant analysis based ensemble machine learning models for groundwater potential modeling and mapping. Water Resources Management, 35, 4415-4433.

Hadzima-Nyarko M., Trinh S.H., 2022. Prediction of compressive strength of concrete at high heating conditions by using artificial neural network-based Bayesian regularization. Journal of Science and Transport Technology, 2, 9-21.

Hess S., 2005. Advanced discrete choice models with applications to transport demand.

Janizadeh S., et al., 2019. Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran. Sustainability, 11, 5426.

Jha M.K., Chowdhury A., Chowdary V., Peiffer S., 2007. Groundwater management and development by integrated remote sensing and geographic information systems: prospects and constraints. Water Resources Management, 21, 427-467.

Jha M.K., Kamii Y., Chikamori K., 2009. Cost-effective approaches for sustainable groundwater management in alluvial aquifer systems. Water resources management, 23, 219.

Jou R.-C., Hensher D.A., Hsu T.-L., 2011. Airport ground access mode choice behavior after the introduction of a new mode: A case study of Taoyuan International Airport in Taiwan. Transportation Research Part E: Logistics and Transportation Review, 47, 371-381.

Kumar R., 2022. Prediction and sensitivity analysis of self compacting concrete slump flow by random forest algorithm. Journal of Science and Transport Technology, 2, 32-43.

Le H.-A., Nguyen T.-A., Nguyen D.-D., Prakash I., 2020. Prediction of soil unconfined compressive strength using Artificial Neural Network Model. Vietnam Journal of Earth Sciences, 42, 255-264.

Lee S., Hong S.-M., Jung H.-S., 2018. GIS-based groundwater potential mapping using artificial neural network and support vector machine models: the case of Boryeong city in Korea. Geocarto international, 33, 847-861.

Lee S., Song K.-Y., Kim Y., Park I., 2012. Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model. Hydrogeology Journal, 20, 1511-1527.

Li H., Huang H., Liu J., 2010. Parameter Estimation of the Mixed Logit Model and Its Application. Journal of Transportation Systems Engineering and Information Technology, 10, 73-78.

Liaw A., Wiener M., 2002. Classification and regression by randomForest. R news, 2, 18-22.

Maclin R., Opitz D., 1998. An Empirical Evaluation of Bagging and Boosting. Proceedings of the National Conference on Artificial Intelligence.

Mafi-Gholami D., Zenner E.K., Jaafari A., Bakhtiari H.R., Bui D.T., 2019. Multi-hazards vulnerability assessment of southern coasts of Iran. Journal of environmental management, 252, 109628.

McClish D., 1989. Analyzing a portion of the ROC Curve. Medical decision making : an international journal of the Society for Medical Decision Making, 9, 190-5.

Miraki S., et al., 2019. Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach. Water Resources Management, 33, 281-302.

Moeck C., et al., 2020. A global-scale dataset of direct natural groundwater recharge rates: A review of variables, processes and relationships. Science of The Total Environment, 717, 137042.

Moghaddam D.D., Rezaei M., Pourghasemi H., Pourtaghie Z., Pradhan B., 2015. Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed, Iran. Arabian Journal of Geosciences, 8, 913-929.

Mokarram M., Roshan G., Negahban S., 2015. Landform classification using topography position index (case study: salt dome of Korsia-Darab plain, Iran). Modeling Earth Systems and Environment, 1(4), 1-7.

Morariu D., Vintan L., Tresp V., 2005. Meta-classification using SVM classifiers for text documents. Intl. Jrnl. of Applied Mathematics and Computer Sciences, 1(1), 15-20.

Mousavi S.M., Golkarian A., Naghibi S.A., Kalantar B., Pradhan B., 2017. GIS-based groundwater spring potential mapping using data mining boosted regression tree and probabilistic frequency ratio models in Iran. Aims Geosci, 3, 91-115.

Mul M.L., Mutiibwa R.K., Foppen J.W.A., Uhlenbrook S., Savenije H.H.G., 2007. Identification of groundwater flow systems using geological mapping and chemical spring analysis in South Pare Mountains, Tanzania. Physics and Chemistry of the Earth, Parts A/B/C, 32, 1015-1022.

Naghibi S.A., Ahmadi K., Daneshi A., 2017. Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resources Management, 31, 2761-2775.

Naghibi S.A., Dashtpagerdi M.M., 2017. Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features. Hydrogeology Journal, 25, 169-189.

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

Nguyen P., et al., 2020a. Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping. Applied Sciences, 10, 2469.

Nguyen P., et al., 2020b. Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case- study, Vietnam. International Journal of Environmental Research and Public Health, 17, 2473.

Nguyen P.T., et al., 2020c. Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping. Applied Sciences, 10, 2469.

Nguyen P.T., et al., 2020d. Groundwater potential mapping combining artificial neural network and real AdaBoost ensemble technique: the DakNong province case-study, Vietnam. International Journal of Environmental Research and Public Health, 17, 2473.

Nhu V.-H., et al., 2020. Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and its Ensembles in a Semi-Arid Region of Iran. Forests, 11, 421.

Oh H.-J., Kim Y.-S., Choi J.-K., Park E., Lee S., 2011. GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. Journal of Hydrology, 399, 158-172.

Opitz D., Maclin R., 1999. Popular Ensemble Methods: An Empirical Study, 11, 169-198.

Pham B.T., et al., 2020. GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment. Water, 12, 683.

Pham B.T., et al., 2019. Hybrid computational intelligence models for groundwater potential mapping. Catena, 182, 104101.

Pourghasemi H.R., et al., 2020. Using machine learning algorithms to map the groundwater recharge potential zones. Journal of Environmental Management, 265, 110525.

Rahmati O., Pourghasemi H.R., Melesse A.M., 2016. Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: a case study at Mehran Region, Iran. Catena, 137, 360-372.

Rizeei H.M., Pradhan B., Saharkhiz M.A., Lee S., 2019. Groundwater aquifer potential modeling using an ensemble multi-adoptive boosting logistic regression technique. Journal of Hydrology, 579, 124172.

Saraf A., et al., 2004. GIS based surface hydrological modelling in identification of groundwater recharge zones. International Journal of Remote Sensing, 25, 5759-5770.

Seenipandi D., Chandrasekar N., Magesh N.S., 2013. Identification of potential groundwater recharge zones in Vaigai upper basin, Tamil Nadu, using GIS-based analytical hierarchical process (AHP) technique. Arabian Journal of Geosciences, 7(4), 1385-1401.

Senanayake I., Dissanayake D., Mayadunna B., Weerasekera W., 2016. An approach to delineate groundwater recharge potential sites in Ambalantota, Sri Lanka using GIS techniques. Geoscience Frontiers, 7, 115-124.

Shahabi H., et al., 2019a. A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia. Sensors, 19, 4893.

Shahabi H., et al., 2019b. A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia. Sensors, 19, 4893.

Shirzadi A., et al., 2017. Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environmental Earth Sciences, 76, 60.

Souissi D., et al., 2018. Mapping groundwater recharge potential zones in arid region using GIS and Landsat approaches, southeast Tunisia. Hydrological Sciences Journal, 63, 251-268.

Tien Bui D., et al., 2019. A hybrid computational intelligence approach to groundwater spring potential mapping. Water, 11, 2013.

Train K., 2009. Discrete Choice Methods With Simulation, Cambridge university press.

Tran A.-T., Le T.-H., Nguyen H.M., 2022. Forecast of surface chloride concentration of concrete utilizing ensemble decision tree boosted. Journal of Science and Transport Technology, 2, 44-56.

Vadiati M., Adamowski J., Beynaghi A., 2018. A brief overview of trends in groundwater research: Progress towards sustainability? Journal of Environmental Management, 223, 849-851.

Visa S., Ramsay B., Ralescu A., Knaap E., 2011. Confusion Matrix-based Feature Selection, 710(1), 120-127.

Zaidi F.K., Nazzal Y., Ahmed I., Naeem M., Jafri M.K., 2015. Identification of potential artificial groundwater recharge zones in Northwestern Saudi Arabia using GIS and Boolean logic. Journal of African Earth Sciences, 111, 156-169.

Zhang B. et al., 2019. Potential hazards to a tunnel caused by adjacent reservoir impoundment. Bulletin of Engineering Geology and the Environment, 78, 397-415.

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Published

24-06-2022

How to Cite

Duong Hai, H., Thi Thanh Ngo, H., Tran Van, P., Nguyen Duc, D., Avand, M. ., Nguyen Huu, D., Amiri, M. ., Van Le, H., Prakash, I. ., & Binh Thai, P. (2022). Development and application of hybrid artificial intelligence models for groundwater potential mapping and assessment. Vietnam Journal of Earth Sciences, 44(3), 410–429. https://doi.org/10.15625/2615-9783/17240

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