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2018 | OriginalPaper | Buchkapitel

37. Potential Application of Advanced Computational Techniques in Prediction of Groundwater Resource of India

verfasst von : Pragnaditya Malakar, Abhijit Mukherjee, Sudeshna Sarkar

Erschienen in: Groundwater of South Asia

Verlag: Springer Singapore

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Abstract

It is a challenging task now and it will be even more challenging in the future to provide an adequate amount of water to the people in this planet. A proper understanding of present day and future water resources is a serious issue, where worldwide increase in the population further promotes water scarcity problem. Groundwater is the major source of freshwater in most parts of South Asia. Some parts are becoming extensively vulnerable as the consumption groundwater in those areas is prominently faster than restored naturally. Application of artificial intelligence (AI) and data mining techniques can be a big help to determine the influence and interdependence of controlling parameters to delineate future groundwater trends and resources. Use of data mining and AI with available spatio-temporal data (satellite and field-based measurements) of governing factors with the help of high-performance computing, and scalable algorithms would help to explore such complex influences and interferences on the groundwater resource dynamics. The outcome would be to predict future trends of available groundwater in changing socio-economic scenarios.

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Vorheriges Kapitel Groundwater Management in India: Status, Challenges and a Framework for Responses

Nächstes Kapitel Private Investments in Groundwater Irrigation and Smallholder Agriculture in West Bengal: Opportunities and Constraints

Banerjee P, Prasad RK, Singh VS (2009) Forecasting of groundwater level in hard rock region using artificial neural network. Environ Geol 58(6):1239–1246. https://doi.org/10.1007/s00254-008-1619-zCrossRef

Banerjee P, Singh VS, Chatttopadhyay K, Chandra PC, Singh B (2011) Artificial neural network model as a potential alternative for groundwater salinity forecasting. J Hydrol 398(3–4):212–220. https://doi.org/10.1016/j.jhydrol.2010.12.016CrossRef

Chattopadhyay M, Chattopadhyay S (2016) Elucidating the role of topological pattern discovery and support vector machine in generating predictive models for Indian summer monsoon rainfall. Theoret Appl Climatol 126(1–2):93–104. https://doi.org/10.1007/s00704-015-1544-5CrossRef

Coppola E, Szidarovszky F, Poulton M, Charles E (2003) Artificial neural network approach for predicting transient water levels in a multilayered groundwater system under variable state, pumping, and climate conditions. J Hydrol Eng 8(6):348–360. https://doi.org/10.1061/(ASCE)1084-0699(2003)8:6(348)CrossRef

Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297. https://doi.org/10.1023/A:1022627411411CrossRef

Dash NB, Panda SN, Remesan R, Sahoo N (2010) Hybrid neural modeling for groundwater level prediction. Neural Comput Appl 19(8):1251–1263. https://doi.org/10.1007/s00521-010-0360-1CrossRef

Dhanya CT, Kumar DN (2009a) Data mining for evolution of association rules for droughts and floods in India using climate inputs. J Geophys Res Atmos 114(2):1–15. https://doi.org/10.1029/2008JD010485CrossRef

Dhanya CT, Kumar DN (2009b) Data mining for evolving fuzzy association rules for predicting monsoon rainfall of India. J Intell Syst 15(1):25–51. https://doi.org/10.1515/JISYS.2009.18.3.193CrossRef

Husna NEA, Bari SH, Hussain MM, Rahman MTU, Rahman M (2016) Ground water level prediction using artificial neural network. Int J Hydrol Sci Technol 6. https://doi.org/10.1504/IJHST.2016.079356

Jovanovic BB, Reljin IS, Reljin BD (2004) Modified ANFIS architecture—improving efficiency of ANFIS technique. In: 2004 7th seminar on neural network applications in electrical engineering, 2004. NEUREL 2004, pp 215–220. https://doi.org/10.1109/NEUREL.2004.1416577

Lohani AK, Krishan G (2015) Groundwater level simulation using artificial neural network in Southeast, Punjab, India. J Geol Geophys 4(3). https://doi.org/10.4172/jgg.1000206

Luk KC, Ball JE, Sharma A (2000) A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. J Hydrol 227:56–65CrossRef

Maiti S, Tiwari RK (2014) A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction. Environ Earth Sci 71(7):3147–3160. https://doi.org/10.1007/s12665-013-2702-7CrossRef

Mohanty S, Jha MK, Kumar A, Panda DK (2013) Comparative evaluation of numerical model and artificial neural network for simulating groundwater flow in Kathajodi-Surua Inter-basin of Odisha, India. J Hydrol 495:38–51. https://doi.org/10.1016/j.jhydrol.2013.04.041CrossRef

Mohanty S, Jha MK, Kumar A, Sudheer KP (2010) Artificial neural network modeling for groundwater level forecasting in a river island of eastern India. Water Resour Manag 24(9):1845–1865. https://doi.org/10.1007/s11269-009-9527-xCrossRef

Mukherjee A (2018) Groundwater of South Asia. Springer Nature, Singapore. ISBN 978-981-10-3888-4

Sujay Raghavendra N, Deka PC (2015) Selection of optimal parameters : GRID search forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid wavelet packet—support vector regression forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid. Cogent Eng 26(1). https://doi.org/10.1080/23311916.2014.999414

Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS (2004) A neuro-fuzzy computing technique for modeling hydrological time series. J Hydrol 291(1–2):52–66. https://doi.org/10.1016/j.jhydrol.2003.12.010CrossRef

Nourani V, Hosseini Baghanam A, Adamowski J, Kisi O (2014) Applications of hybrid wavelet-Artificial Intelligence models in hydrology: a review. J Hydrol 514:358–377. https://doi.org/10.1016/j.jhydrol.2014.03.057CrossRef

Purkait B, Kadam SS, Das SK (2008) Application of artificial neural network model to study arsenic contamination in groundwater of Malda district, Eastern India. J Environ Inform 12(2):140–149. https://doi.org/10.3808/jei.200800132CrossRef

Sharma N, Harinarayan Z, Dheeraj T (2015) Runoff and sediment yield modeling using ANN and support vector machines: a case study from Nepal watershed. Model Earth Syst Environ 1(3):1–8. https://doi.org/10.1007/s40808-015-0027-0CrossRef

Singh KP, Gupta S (2012) Artificial intelligence based modeling for predicting the disinfection by-products in water. Chemometr Intell Lab Syst 114:122–131. https://doi.org/10.1016/j.chemolab.2012.03.014CrossRef

Singh R, Datta B (2006) Identification of groundwater pollution sources using GA-based linked simulation optimization model. J Hydrol Eng 11(2):101–109. Retrieved from http://ascelibrary.org/doi/abs/10.1061/(ASCE)1084-0699(2006)11:2(101)CrossRef

Sudheer C, Shrivastava NA, Panigrahi BK, Mathur S (2011) Groundwater level forecasting using SVM-QPSO. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), 7076 LNCS(Part 1), pp 731–741. https://doi.org/10.1007/978-3-642-27172-4_86CrossRef

Suryanarayana C, Sudheer C, Mahammood V, Panigrahi BK (2014) An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India. Neurocomputing 145:324–335. https://doi.org/10.1016/j.neucom.2014.05.026CrossRef

Thirumalaiah K, Deo MC (1998) River stage forecasting using artificial neural networks. J Hydrol Eng 3(1):26–32CrossRef

Vapnik VN (1998) Statistical learning theory. Wiley, New York, pp 252–257

Wang WC, Chau KW, Cheng CT, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374(3–4):294–306. https://doi.org/10.1016/j.jhydrol.2009.06.019CrossRef

World Bank Metadata. http://databank.worldbank.org/data/reports.aspx?source=2&country=SAS

Titel: Potential Application of Advanced Computational Techniques in Prediction of Groundwater Resource of India
verfasst von: Pragnaditya Malakar
Abhijit Mukherjee
Sudeshna Sarkar
Verlag: Springer Singapore
Buch: Groundwater of South Asia
Print ISBN: 978-981-10-3888-4

Electronic ISBN: 978-981-10-3889-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-981-10-3889-1_37