Sustainable groundwater management through an optimal water supply system using a coupled simulation-optimization approach.
Conjunctive use
Genetic algorithm
MODFLOW
Optimal water supply systems
Wavelet support vector regression
Journal
Environmental monitoring and assessment
ISSN: 1573-2959
Titre abrégé: Environ Monit Assess
Pays: Netherlands
ID NLM: 8508350
Informations de publication
Date de publication:
14 Oct 2022
14 Oct 2022
Historique:
received:
28
12
2021
accepted:
21
09
2022
entrez:
14
10
2022
pubmed:
15
10
2022
medline:
19
10
2022
Statut:
epublish
Résumé
Meeting people's water supply needs in cities is an urgent challenge. Especially in coastal cities, surface water sources are usually inadequate and so groundwater is frequently over-exploited, resulting in its rapid decrease and intrusion of saltwater. Conjunctive use of surface water and groundwater is the best alternative approach to mitigate the overuse of groundwater through effective distribution of surface water sources. It requires numerous runs of the simulation-optimization model to select an optimal pattern of water distribution by keeping the groundwater levels under control. In this study, a new simulation-optimization model is developed using wavelet support vector regression (WSVR) and genetic algorithm (GA) to propose an optimal water distribution system to Visakhapatnam city on the East Coast of India, fulfilling the constraints of surface water quantity, aquifer pumping, and drawdown. Estimation of groundwater pumping and groundwater level variation in spatial context is challenging in urban environment. To overcome this, the calibrated modular finite-difference flow (MODFLOW) model for this study area has been used to prepare the spatial and temporal variation of model inputs such as groundwater pumping and groundwater levels. The WSVR-GA model's performance to reduce the groundwater pumping is evaluated in three distinct cases. The surface water resources from three sources are distributed to different wards in the city source-wise in case I and centralized in cases II and III, while the source-wise surface water constraints are limited to monthly in cases I and II and annual in case III. The WSVR-GA management model suggested ward-wise groundwater pumping restrictions, resulting in 9.57 MCM, 11.64 MCM, and 12.54 MCM increase in total groundwater storage capacity in cases I, II, and III respectively. Cases II and III offer 21% and 25%, respectively, more storage than case I. Thus, centralized distribution systems have increased the sustainability of groundwater supplies by preventing overdrafts caused by a lack of surface water resources. Validation of results using MODFLOW indicates a substantial rise in groundwater levels in the study area.
Identifiants
pubmed: 36239843
doi: 10.1007/s10661-022-10520-y
pii: 10.1007/s10661-022-10520-y
doi:
Substances chimiques
Water
059QF0KO0R
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
888Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
Références
Adamowski, J., & Chan, H. F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1), 28–40.
Afan, H. A., et al. (2021). Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques. Engineering Applications of Computational Fluid Mechanics, 15(1), 1420–1439.
Amir, J., & Navid, J. (2011). Groundwater modeling using hybrid of artificial neural network with genetic algorithm. African Journal of Agricultural Research, 6(26), 5775–5784.
Band, S. S., et al. (2021). Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression. Engineering Applications of Computational Fluid Mechanics, 15(1), 1147–1158.
Barlow, M. P., Ahlfeld, P. D., & Dickerman, C. D. (2003). Conjunctive-management models for sustained yield of stream-aquifer systems. Journal of Water Resources Planning and Management, ASCE, 129(1), 35–48.
Bhaskara Rao, G. (2012). Ground water brochure for Visakhapatnam District, Andhra Pradesh. Ministry of Water Resources, Government of India. Report by CGWB.
Bhattacharjya, R. K., & Datta, B. (2005). Optimal management of coastal aquifer using linked simulation-optimization approach. Water Resources Management, 19, 295–320.
Bhattacharjya, R. K., & Datta, B. (2009). ANN-GA-based model for multiple objective management of coastal aquifers. Journal of Water Resources Planning and Management, 135(5), 314–322.
Chakraei, I., Safavi, H., Dandy, G., & Golmohammadi, M. (2021). Integrated simulation-optimization framework for water allocation based on sustainability of surface water and groundwater resources. Journal of Water Resources Planning and Management, 147(3), 05021001.
Dash, N. B., Panda, S. N., Remesan, R., & Sahoo, N. (2010). Hybrid neural modeling for groundwater level prediction. Neural Computing and Applications, 19(8), 1251–1263.
Daubechies, I. (1990). The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory, 36(5), 6–7.
Gaur, S., Sudheer, C., Graillot, D., Chahar, B. R., & Nagesh, K. D. (2013). Application of artificial neural networks and particle swarm optimization for the management of groundwater resources. Water Resources Management, 27, 927–941.
Gholami, V., et al. (2015). Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers. Journal of Hydrology, 529(3), 1060–1069.
Goldberg, D. E. (2000). Genetic algorithms in search, optimization, and in machine learning. Bangalore, India: Addison Wiley.
Ground Water Estimation Committee (GEC). (2015). India: Ground Water Resource Estimation Committee, Ministry of Water Resources, River Development & Ganga Rejuvenation. Government of India. Report.
Kalteh, A. M. (2013). Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform. Computers & Geosciences, 54, 1–8.
Karamouz, M., Mahmoud, M., Rezapour, T., & Reza, K. (2007). Application of genetic algorithms and artificial neural networks in conjunctive use of surface and groundwater resources. Water International, 32(1), 163–176.
Khalid, Q., Abdelkader, L., Driss, O., Ahmed, N., & Alexander, H. D. C. (2009). Optimal extraction of groundwater in Gaza Coastal Aquifer. Journal of Water Resource and Protection, 4, 249–259.
Kisi, O. (2010). Wavelet regression model for short-term streamflow forecasting. Journal of Hydrology, 389, 344–353.
Kisi, O., & Cimen, M. (2011). A wavelet-support vector machine conjunction model for monthly streamflow forecasting. Journal of Hydrology, 399, 132–140.
Kisi, O., & Jalal, S. (2011). Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models. Water Resource Management, 25, 3135–3152.
Loucks, D. P., & Beek, E. V. (2017). Water resources planning and management: An overview. Water Resource Systems Planning and Management. Cham: Springer.
Maheswaran, R., & Khosa, R. (2012). Comparative study of different wavelets for hydrologic forecasting. Computers and Geosciences, 46, 284–295.
Maheswaran, R., & Khosa, R. (2013). Wavelets-based non-linear model for real-time daily flow forecasting in Krishna River. Journal of Hydroinformatics, 15, 1022–1041.
Mantoglou, A., & Maria, P. (2008). Optimal design of pumping networks in coastal aquifers using sharp interface models. Journal of Hydrology, 361, 52–63.
Moosavi, V., Vafakhah, M., Shirmohammadi, B., & Behnia, N. (2013). A wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods. Water Resources Management, 27, 1301–1321.
Nanakorn, P., & Meesomklin, K. (2001). An adaptive penalty function in genetic algorithms for structural design optimization. Computers & Structures, 79, 2527–2539.
Pandey, K., et al. (2020). Artificial neural network optimized with a genetic algorithm for seasonal groundwater table depth prediction in Uttar Pradesh, India. Sustainability, 12(11), 8932.
Partal, T., & Cigizoglu, H. K. (2008). Estimation and forecasting of daily suspended sediment data using wavelet-neural networks. Journal of Hydrology, 358(3), 317–331.
Peralta, R. C. (2001). Simulation/optimization applications and software for optimal groundwater and conjunctive water management. Proceedings of MODFLOW and Other Modeling Odysseys (pp. 691–694). IGWMC.
Raghavendra, N. S., & Deka, P. C. (2015). Forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid wavelet packet-support vector regression. Cogent Engineering, 2(1), 999414. https://doi.org/10.1080/23311916.2014.999414
doi: 10.1080/23311916.2014.999414
Raj, M. S., & Datta, B. (2006). Identification of groundwater pollution sources using GA-based linked simulation optimization model. Journal of Hydrologic Engineering, ASCE, 11(2), 101–109.
Rajaee, T., et al. (2019). A review of the artificial intelligence methods in groundwater level modeling. Journal of Hydrology, 572, 336–351.
Ramakrishnan, K., Suribabu, C. R., & Neelakantan, T. R. (2010). Crop calendar adjustment study for Sathanur irrigation system in India using genetic algorithm. Water Resources Management, 24(14), 3835–3851.
Rao, S. V. N., Murty, B. S., Thandaveswara, B. S., & Sreenivasulu, V. (2005). Planning groundwater development in coastal deltas with paleo channels. Water Resources Management, 19, 625–639.
Rao, S. V. N., Sreenivasulu, V., Murty, B. S., Thandaveswara, B. S., & Sudheer, K. P. (2004). Planning groundwater development in coastal aquifers. Hydrological Sciences Journal, 49(1), 155–170.
Rathinasamy, M., Rakesh, K., Jan, A., Sudheer, C., Partheepan, G., Jatin, A., & Boini, N. (2014). Wavelet-based multiscale performance analysis: An approach to assess and improve hydrological models. Water Resources Research, 50, 9721–9737.
Safavi, H. R., Darzi, F., & Marino, M. A. (2010). Simulation-optimization modeling of conjunctive use of surface water and groundwater. Water Resources Management, 24(10), 1965–1988.
Safavi, H. R., & Esmikhani, M. (2013). Conjunctive use of surface water and groundwater: Application of support vector machines (SVMs) and genetic algorithms. Water Resource Management, 27(7), 2623–2644.
Safavi, H. R., & Rezae, F. (2015). Conjunctive use of surface and ground water using fuzzy neural network and genetic algorithm. Iranian Journal of Science and Technology, 39(C2), 365–377.
Sepahvand, R., Safavi, H. R., & Rezaei, F. (2019). Multi-objective planning for conjunctive use of surface and ground water resources using genetic programming. Water Resources Management, 33, 2123–2137.
Shreedhar, M., Andreja, J., & Dimitri, P. S. (2002). Groundwater remediation strategy using global optimization algorithms. Journal of Water Resources Planning and Management, ASCE, 128, 431–440.
Singh, A., & Panda, S. N. (2013). Optimization and simulation modelling for managing the problems of water resources. Water Resources Management, 27(9), 3421–3431.
Singh, A. (2014). Simulation-optimization modeling for conjunctive water use management. Journal of Agricultural Water Management, 141, 23–29.
Singh, A., Panda, S. N., Saxena, C. K., Verma, C. L., Uzokwe, V. N., Krause, P., & Gupta, S. K. (2016). Optimization modeling for conjunctive use planning of surface water and groundwater for irrigation. Journal of Irrigation and Drainage Engineering, 142(3), 04015060.
Sudheer, C., Anand, N., Panigrahi, B. K., & Mathur, S. (2013). Streamflow forecasting by SVM with quantum behaved particle swarm optimization. Neurocomputing, 101, 18–23.
Sudheer, C., & Mathur, S. (2012). Groundwater level forecasting using SVM-PSO. International Journal of Hydrology Science and Technology, 2(2), 202–218.
Sudheer, C., Mathur, S., Maheswaran, R., & Panigrahi, B. K. (2014). A hybrid SVM-PSO model for forecasting monthly streamflow. Neural Computing and Applications, 24(6), 1381–1389.
Suryanarayana, C. (2018). Sustainable groundwater management using coupled simulation-optimization approach for Visakhapatnam city, Andhra Pradesh, India. Visakhapatnam, India: Andhra University. Dissertation.
Suryanarayana, C., & Mahammood, V. (2019). Groundwater-level assessment and prediction using realistic pumping and recharge rates for semi-arid coastal regions: A case study of Visakhapatnam city, India. Hydrogeology Journal, 27, 249–272.
Suryanarayana, C., Sudheer, C., Mahammood, V., & Panigrahi, B. K. (2014). An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam. India, Neurocomputing, 145, 324–335.
Taormina, R., et al. (2012). Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon. Engineering Applications of Artificial Intelligence, 25(8), 1670–1676.
Urban Small Water Enterprises for Smarter Cities (USWESC). (2015). Report of rapid assessment of water supply: City of Visakhapatnam. Safe Water Network and US Agency for International Development (USAID), Ministry of Urban Development, Swachh Bharat Mission.
Zhou, T., Faxin, W., & Zhi, Y. (2017). Comparative analysis of ANN and SVM models combined with wavelet preprocess for groundwater depth prediction. Water, 9(10), 781.