A surrogate based multi-objective management model to control saltwater intrusion in multi-layered coastal aquifer systems

References

• Adamowski, J., H. F. Chan, S. O. Prasher, and V. N. Sharda. 2012. “Comparison of Multivariate Adaptive Regression Splines with Coupled Wavelet Transform Artificial Neural Networks for Runoff Forecasting in Himalayan Micro-watersheds with Limited Data.” Journal of Hydroinformatics 14 (3): 731–744. doi:10.2166/hydro.2011.044.
   Web of Science ®Google Scholar
• Banerjee, P., V. S. Singh, K. Chatttopadhyay, P. C. Chandra, and B. Singh. 2011. “Artificial Neural Network Model as a Potential Alternative for Groundwater Salinity Forecasting.” Journal of Hydrology 398 (3–4): 212–220. doi:10.1016/j.jhydrol.2010.12.016.
   Web of Science ®Google Scholar
• Bera, P., S. O. Prasher, R. M. Patel, A. Madani, R. Lacroix, J. D. Gaynor, C. S. Tan, and S. H. Kim. 2006. “Application of MARS in Simulating Pesticide Concentrations in Soil.” Transactions of the Asabe 49 (1): 297–307. doi: 10.13031/2013.20228
   Web of Science ®Google Scholar
• Beuchat, X., B. Schaefli, M. Soutter, and A. Mermoud. 2011. “Toward a Robust Method for Subdaily Rainfall Downscaling From Daily Data.” Water Resources Research 47: 703. doi:10.1029/2010wr010342.
   Web of Science ®Google Scholar
• Bhattacharjya, R. K., and B. Datta. 2009. “ANN-GA-based Model for Multiple Objective Management of Coastal Aquifers.” Journal of Water Resources Planning and Management 135 (5): 314–322. doi:10.1061/(asce)0733-9496(2009)135:5(314).
   Web of Science ®Google Scholar
• Bhattacharjya, R. K., B. Datta, and M. G. Satish. 2007. “Artificial Neural Networks Approximation of Density Dependent Saltwater Intrusion Process in Coastal Aquifers.” Journal of Hydrologic Engineering 12 (3): 273–282. doi:10.1061/(ASCE)1084-0699(2007)12:3(273).
   Web of Science ®Google Scholar
• Christelis, V., and A. Mantoglou. 2016. “Pumping Optimization of Coastal Aquifers Assisted by Adaptive Metamodelling Methods and Radial Basis Functions.” Water Resources Management 30: 1–15. doi:10.1007/s11269-016-1337-3 doi: 10.1007/s11269-015-1163-z
   Web of Science ®Google Scholar
• Coulibaly, P., and C. K. Baldwin. 2005. “Nonstationary Hydrological Time Series Forecasting Using Nonlinear Dynamic Methods.” Journal of Hydrology 307 (1–4): 164–174. doi:10.1016/j.hydrol.2004.10.008 doi: 10.1016/j.jhydrol.2004.10.008
   Web of Science ®Google Scholar
• Datta, B., and R. C. Peralta. 1986. “Interactive Computer Graphics-based Multiobjective Decision-making for Regional Groundwater Management.” Agricultural Water Management 11 (2): 91–116. doi:10.1016/0378-3774(86)90023-5.
   Web of Science ®Google Scholar
• Deb, K. 2001. Multi-objective Optimization Using Evolutionary Algorithms. New York, USA: John Wiley & Sons.
   Google Scholar
• Deb, K., and T. Goel. 2001. “Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence.” Proceedings of the First International Conference on Evolutionary Multi-criterion Optimization, 67–81. Springer-Verlag.
   Google Scholar
• Dhar, A., and B. Datta. 2009. “Saltwater Intrusion Management of Coastal Aquifers. I: Linked Simulation-optimization.” Journal of Hydrologic Engineering 14 (12): 1263–1272. doi:10.1061/(asce)he.1943-5584.0000097.
   Web of Science ®Google Scholar
• Friedman, J. H. 1991. “Multivariate Adaptive Regression Splines (with Discussion).” The Annals of Statistics 19 (1): 1–67. doi:10.1214/aos/1176347963.
   Web of Science ®Google Scholar
• Hussain, M. S., A. A. Javadi, A. Ahangar-Asr, and R. Farmani. 2015. “A Surrogate Model for Simulation–Optimization of Aquifer Systems Subjected to Seawater Intrusion.” Journal of Hydrology 523: 542–554. doi:10.1016/j.jhydrol.2015.01.079.
   Web of Science ®Google Scholar
• Ketabchi, H., and B. Ataie-Ashtiani. 2015a. “Evolutionary Algorithms for the Optimal Management of Coastal Groundwater: A Comparative Study Toward Future Challenges.” Journal of Hydrology 520: 193–213. doi:10.1016/j.jhydrol.2014.11.043.
   Web of Science ®Google Scholar
• Ketabchi, H., and B. Ataie-Ashtiani. 2015b. “Review: Coastal Groundwater Optimization—Advances, Challenges, and Practical Solutions.” Hydrogeology Journal 23 (6): 1129–1154. doi:10.1007/s10040-015-1254-1.
   Web of Science ®Google Scholar
• Kourakos, G., and A. Mantoglou. 2009. “Pumping Optimization of Coastal Aquifers Based on Evolutionary Algorithms and Surrogate Modular Neural Network Models.” Advances in Water Resources 32 (4): 507–521. doi:10.1016/j.advwatres.2009.01.001.
   Web of Science ®Google Scholar
• Kourakos, G., and A. Mantoglou. 2013. “Development of a Multi-objective Optimization Algorithm Using Surrogate Models for Coastal Aquifer Management.” Journal of Hydrology 479: 13–23. doi:10.1016/j.jhydrol.2012.10.050.
   Web of Science ®Google Scholar
• Lin, H. J., D. R. Rechards, C. A. Talbot, G. T. Yeh, J. R. Cheng, H. P. Cheng, and N. L. Jones. 1997. A Three-dimensional Finite-element Computer Model for Simulating Density-dependent Flow and Transport in Variable Saturated Media: Version 3.0. Vicksburg, Miss: U. S. Army Engineering Research and Development Center. 143.
   Google Scholar
• Lu, C. H., A. D. Werner, C. T. Simmons, N. I. Robinson, and J. Luo. 2013. “Maximizing net Extraction Using an Injection-extraction Well Pair in a Coastal Aquifer.” Ground Water 51 (2): 219–228. doi:10.1111/j.1745-6584.2012.00973.x.
   PubMedGoogle Scholar
• Luyun, R., K. Momii, and K. Nakagawa. 2009. “Laboratory-scale Saltwater Behavior due to Subsurface Cutoff Wall.” Journal of Hydrology 377 (3–4): 227–236. doi:10.1016/j.jhydrol.2009.08.019.
   Web of Science ®Google Scholar
• Luyun, R., K. Momii, and K. Nakagawa. 2011. “Effects of Recharge Wells and Flow Barriers on Seawater Intrusion.” Ground Water 49 (2): 239–249. doi:10.1111/j.1745-6584.2010.00719.x.
   PubMedGoogle Scholar
• Mahesha, A. 1996. “Control of Seawater Intrusion Through Injection-extraction Well System.” Journal of Irrigation and Drainage Engineering 122 (5): 314–317. doi:10.1061/(asce)0733-9437(1996)122:5(314).
   Web of Science ®Google Scholar
• MathWorks. 2016. MATLAB Version R2016b. Mathworks, Natick: The Mathworks.
   Google Scholar
• Nasseri, M., H. Tavakol-Davani, and B. Zahraie. 2013. “Performance Assessment of Different Data Mining Methods in Statistical Downscaling of Daily Precipitation.” Journal of Hydrology 492: 1–14. doi:10.1016/j.jhydrol.2013.04.017.
   Web of Science ®Google Scholar
• Nocchi, M., and M. Salleolini. 2013. “A 3D Density-dependent Model for Assessment and Optimization of Water Management Policy in a Coastal Carbonate Aquifer Exploited for Water Supply and Fish Farming.” Journal of Hydrology 492: 200–218. doi:10.1016/j.jhydrol.2013.03.048.
   Web of Science ®Google Scholar
• Park, N., L. Cui, and L. Shi. 2009. “Analytical Design Curves to Maximize Pumping or Minimize Injection in Coastal Aquifers.” Ground Water 47 (6): 797–805. doi:10.1111/j.1745-6584.2009.00589.x.
   PubMedGoogle Scholar
• Pebesma, E. J., and G. B. M. Heuvelink. 1999. “Latin Hypercube Sampling of Gaussian Random Fields.” Technometrics 41 (4): 303–312. doi:10.2307/1271347 doi: 10.1080/00401706.1999.10485930
   Web of Science ®Google Scholar
• Pyne, R. D. G. 1995. Groundwater Recharge and Wells: A Guide to Aquifer Storage and Recovery. Florida: Lewis Publishers. ISBN 1-56670-097-3.
   Google Scholar
• Raghavendra, N. S., and P. C. Deka. 2015. “Forecasting Monthly Groundwater Level Fluctuations in Coastal Aquifers Using Hybrid Wavelet Packet–Support Vector Regression.” Cogent Engineering 2 (1): 999414. doi:10.1080/23311916.2014.999414.
   Web of Science ®Google Scholar
• Roy, D. K., and B. Datta. 2017a. “Fuzzy C-mean Clustering Based Inference System for Saltwater Intrusion Processes Prediction in Coastal Aquifers.” Water Resources Management 31 (1): 355–376. doi:10.1007/s11269-016-1531-3.
   Web of Science ®Google Scholar
• Roy, D. K., and B. Datta. 2017b. “Multivariate Adaptive Regression Spline Ensembles for Management of Multilayered Coastal Aquifers.” Journal of Hydrologic Engineering 22 (9): 04017031. doi:10.1061/(ASCE)HE.1943-5584.0001550.
   Web of Science ®Google Scholar
• Saeed, M. M., M. Bruen, and M. N. Asghar. 2002. “A Review of Modeling Approaches to Simulate Saline-upconing Under Skimming Wells.” Nordic Hydrology 33 (2–3): 165–188. doi: 10.2166/nh.2002.0021
   Google Scholar
• Salford-Systems. 2013. SPM Users Guide: Introducing MARS. San Diego, CA: Salford Systems.
   Google Scholar
• Salford-Systems. 2016. MARS® (Version 8.0 Ultra), Multivariate Adaptive Regression Spline. San Diego, CA: Salford Systems.
   Google Scholar
• Samadi, M., E. Jabbari, H. M. Azamathulla, and M. Mojallal. 2015. “Estimation of Scour Depth Below Free Overfall Spillways Using Multivariate Adaptive Regression Splines and Artificial Neural Networks.” Engineering Applications of Computational Fluid Mechanics 9 (1): 291–300. doi:10.1080/19942060.2015.1011826.
   Web of Science ®Google Scholar
• Sharda, V. N., R. M. Patel, S. O. Prasher, P. R. Ojasvi, and C. Prakash. 2006. “Modeling Runoff From Middle Himalayan Watersheds Employing Artificial Intelligence Techniques.” Agricultural Water Management 83 (3): 233–242. doi:10.1016/j.agwat.2006.01.003.
   Web of Science ®Google Scholar
• Sharda, V. N., S. O. Prasher, R. M. Patel, P. R. Ojasvi, and C. Prakash. 2008. “Performance of Multivariate Adaptive Regression Splines (MARS) in Predicting Runoff in Mid-himalayan Micro-watersheds with Limited Data.” Hydrological Sciences Journal 53 (6): 1165–1175. doi:10.1623/hysj.53.6.1165.
   Web of Science ®Google Scholar
• Shu, C., and T. B. M. J. Ouarda. 2008. “Regional Flood Frequency Analysis at Ungauged Sites Using the Adaptive Neuro-fuzzy Inference System.” Journal of Hydrology 349 (1–2): 31–43. doi:10.1016/j.jhydrol.2007.10.050.
   Web of Science ®Google Scholar
• Sreekanth, J., and B. Datta. 2010. “Multi-objective Management of Saltwater Intrusion in Coastal Aquifers Using Genetic Programming and Modular Neural Network Based Surrogate Models.” Journal of Hydrology 393 (3–4): 245–256. doi:10.1016/j.jhydrol.2010.08.023.
   Web of Science ®Google Scholar
• Sreekanth, J., and B. Datta. 2011a. “Comparative Evaluation of Genetic Programming and Neural Network as Potential Surrogate Models for Coastal Aquifer Management.” Water Resources Management 25 (13): 3201–3218. doi:10.1007/s11269-011-9852-8.
   Web of Science ®Google Scholar
• Sreekanth, J., and B. Datta. 2011b. “Optimal Combined Operation of Production and Barrier Wells for the Control of Saltwater Intrusion in Coastal Groundwater Well Fields.” Desalination and Water Treatment 32 (1–3): 72–78. doi:10.5004/dwt.2011.2680.
   Web of Science ®Google Scholar
• Yang, C. C., S. O. Prasher, R. Lacroix, and S. H. Kim. 2003. “A Multivariate Adaptive Regression Splines Model for Simulation of Pesticide Transport in Soils.” Biosystems Engineering 86 (1): 9–15. doi:10.1016/s1537-5110(03)00099-0.
   Web of Science ®Google Scholar
• Zabihi, M., H. R. Pourghasemi, Z. S. Pourtaghi, and M. Behzadfar. 2016. “GIS-based Multivariate Adaptive Regression Spline and Random Forest Models for Groundwater Potential Mapping in Iran.” Environmental Earth Sciences 75 (8): 578. doi:10.1007/s12665-016-5424-9.
   Web of Science ®Google Scholar