Hybrid support vector regression models with algorithm of innovative gunner for the simulation of groundwater level

Acta Geophysica - Tập 70 Số 4 - Trang 1885-1898 - 2022
Thendiyath Roshni1, Ehsan Mirzania2, Mahsa H. Kashani3, Quynh-Anh Thi Bui4, Shahab Shamshirband5
1National Institute of Technology Patna
2Department of Water Engineering, University of Tabriz, Tabriz, Iran
3Department of Water Engineering, Water Management Research Center, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran
4University of Transport Technology, Thanh Xuan, Hanoi, Vietnam
5Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan, ROC

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Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407:28–40

Almasri MN, Kaluarachchi JJ (2005) Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrogen loading and recharge data. Environ Model Softw 20(7):851–871

Awad M, Khanna R (2015) Support vector regression. In: Efficient learning machines. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4302-5990-9_4.

Band SS, Heggy E, Bateni SM, Karami H, Rabiee M, Samadianfard S, Chau KW, Mosavi A (2021) Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression. Eng Appl Comput Fluid Mech 15(1):1147–1158. https://doi.org/10.1080/19942060.2021.1944913

Charmahineh AR, Zounemat Kermani M (2017) Evaluation of the efficiency of support vector regression, multi-layer perceptron neural network and multivariate linear regression on groundwater level prediction (case study: Shahrekord plain). J Watershed Manag Res 8(15):1–12. https://www.sid.ir/en/journal/viewpaper.aspx?id=541805.

Chen ST, Yu PS (2007) Real-time probabilistic forecasting of flood stages. J Hydrol 340:63

Coulibaly P, Anctil F, Aravena R, Bobee B (2001) Artificial neural network modeling of water table depth fluctuations. Water Resour Res 37(4):885–896

Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Groundwater level forecasting using artificial neural networks. J Hydrol 309(1–4):229–240

Dastvareh J, Naserianasl Z, Hasanvand H, Amiri Domari S (2020) Modeling groundwater level and investigating the aquifer status of Minab plain. Geogr Human Relationships 3(2):50–59. https://doi.org/10.22034/gahr.2020.247817.1442.

Dehghani R, Poudeh HT (2021a) Application of novel hybrid artificial intelligence algorithms to groundwater simulation. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-021-03596-5

Dehghani R, Poudeh HT (2021b) Applying hybrid artificial algorithms to the estimation of river flow: a case study of Karkheh catchment area. Arab J Geosci 14:768. https://doi.org/10.1007/s12517-021-07079-2

Ebrahimi H, Rajaee T (2017) Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine. Global Planet Change 148:181–191

Gholami V, Khaleghi MR, Salimi ET (2020) Groundwater quality modeling using self-organizing map (SOM) and geographic information system (GIS) on the Caspian southern coasts. J Mt Sci 17(7):1724–1734

Gholami V, Khaleghi MR, Pirasteh S, Booij MJ (2022) Comparison of self-organizing map, artificial neural network, and co-active neuro-fuzzy inference system methods in simulating groundwater quality: geospatial artificial. Water Resour Manage 36(2):451–469

Gill MK, Asefa T, Kaheil Y, McKee M (2007) Effect of missing data on performance of learning algorithms for hydrologic prediction: implication to and imputation technique. Water Resour Res 43(7):W07416

Guo XR, Zuo R, Wang JS, Meng L, Teng Y, Shi R, Gao X, Ding F (2019) Hydrogeochemical evolution of interaction between surface water and groundwater affected by exploitation. Groundwater 57:430–442. https://doi.org/10.1111/gwat.12805

Hoque MDA, Adhikary SK (2020) Prediction of groundwater level using artificial neural network and multivariate time series models. In: 5th International conference on civil engineering for sustainable development (ICCESD 2020) at: KUET, Khulna, Bangladesh.

Hsieh PC, Tong WA, Wang YC (2019) A hybrid approach of artificial neural network and multiple regression to forecast typhoon rainfall and groundwater-level change. Hydrol Sci J 64(14):1793–1802. https://doi.org/10.1080/02626667.2019.1677905

Isazadeh M, Biazar SM, Ashrafzadeh A (2017) Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters. Environ Earth Sci 76(17):p610

Jalali M, Kamangar M, Razmi R (2019) prediction of the water table surface model using the hyperbolic tangent function of the neural, network case study: Sarkhoon Plain. Hydrogeomorphol Tabriz Univ 6(20):101–119

Jeihouni, E., Eslamian, S., Mohammadi, M., & Zareian, M. J. (2019). Simulation of groundwater level fluctuations in response to main climate parameters using a wavelet–ANN hybrid technique for the Shabestar Plain, Iran. Environ Earth Sci 78(10). https://doi.org/10.1007/s12665-019-8283-3.

Kayhomayoon Z, Ghordoyee Milan S, Arya Azar N, Moghaddam HK (2021) A new approach for regional groundwater level simulation: clustering, simulation, and optimization. Nat Resour Res 30:4165–4185. https://doi.org/10.1007/s11053-021-09913-6

Kumar D, Roshni T, Singh A, Jha MK, Samui P (2020) Predicting groundwater depth fluctuations using deep learning, extreme learning machine and Gaussian process: a comparative study. Earth Sci Inf 13:1237–1250

Lee S, Lee KK, Yoon H (2019) Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors. Hydrogeol J 27:567–579. https://doi.org/10.1007/s10040-018-1866-3

Liu D, Mishra AK, Yu Z, Lu H, Li Y (2021) Support vector machine and data assimilation framework for Groundwater Level Forecasting using GRACE satellite data. J Hydrol 603 (A):126929.

Lorenzo-Lacruz J, Garcia C, Morán-Tejeda E (2017) Groundwater level responses to precipitation variability in Mediterranean insular aquifers. J Hydrol 2017(552):516–531

Malik A, Bhagwat A (2021) Modelling groundwater level fluctuations in urban areas using artificial neural network. Groundw Sustain Dev 12. https://doi.org/10.1016/j.gsd.2020.100484

Milan SG, Roozbahani A, Banihabib ME (2018) Fuzzy optimization model and fuzzy inference system for conjunctive use of surface and groundwater resources. J Hydrol 566:421–434

Mirarabi A, Nassery HR, Nakhaei M, Adamowski J, Akbarzadeh AH, Alijani F (2019) Evaluation of data-driven models (SVR and ANN) for groundwater-level prediction in confined and unconfined systems. Environ Earth Sci 78:489. https://doi.org/10.1007/s12665-019-8474-y

Moghaddam HK, Moghaddam HK, Rahimzadeh Kivi Z, Bahreinimotlagh M, Alizadeh MJ ( 2019) Developing comparative mathematic models, BN and ANN for forecasting of groundwater levels. Groundwater Sustain Dev. https://doi.org/10.1016/j.gsd.2019.100237.

Moosavi V, Mahjoobi J, Hayatzadeh M (2021) Combining group method of data handling with signal processing approaches to improve accuracy of groundwater level modeling. Natural Resources Res 30(2). https://doi.org/10.1007/s11053-020-09799-w.

Moravej M, Amani P, Hosseini-Moghari SM (2020) Groundwater level simulation and forecasting using interior search algorithm-least square support vector regression (ISA-LSSVR). Groundw Sustain Dev 11:100447. https://doi.org/10.1016/j.gsd.2020.100447

Nadiri AA, Naderi K, Khatibi R, Gharekhani M (2019) Modelling groundwater level variations by learning from multiple models using fuzzy logic. Hydrol Sci J 64(2):210–226. https://doi.org/10.1080/02626667.2018.1554940

Nadiri AA, Razzagh S, Khatibi R, Sedghi Z (2021) Predictive groundwater levels modelling by Inclusive Multiple Modelling (IMM) at multiple levels. Earth Sci Inf 14(14):749–763. https://doi.org/10.1007/s12145-021-00572-y

Natarajan N, Sudheer Ch (2019) Groundwater level forecasting using soft computing techniques. Neural Comput Appl 32(12):7691–7708.

Nourani V, Mogaddam AA, Nadiri AO (2008) An ANN-based model for spatiotemporal groundwater level forecasting. Hydrol Process 22:5054–5066. https://doi.org/10.1002/hyp.7129

Nourani V, Alami MT, Vousoughi FD (2015) Wavelet-entropy data pre-processing approach for ANN-based groundwater level modeling. J Hydrol 524:255–269

Di Nunno F, Granata F (2020) Groundwater level prediction in Apulia region (Southern Italy) using NARX neural network. Environ Res 190:110062. doi: https://doi.org/10.1016/j.envres.2020.110062.

Osman AAI, Ahmed AN, Fai Chow M, Feng Huang Y, El-Shafie (2021) Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia. Ain Shams Eng J 12(2):1545–1556. https://doi.org/10.1016/j.asej.2020.11.011

Panahi M, Misaqi F, Qanbari F (2017) Determining of trend variation in quality parameters of Shabestar plain underground water. Environ Sci 15(3):19–38

Pijarski P, Kacejko P (2019) A new metaheuristic optimization method: the algorithm of the innovative gunner (AIG). Eng Optim 51(12):2049–2068.

Raghavendra N, Deka PC (2015) Forecasting monthly groundwater level fluctuations in coastal aquifers using hybrid Wavelet packet-support vector regression. Cogent Eng 2(1):p999414

Rajaee T, Ebrahimi H, Nourani V (2019) A reviewof the artificial intelligencemethods in groundwater level modeling. J Hydrol 572:336–351. https://doi.org/10.1016/j.jhydrol.2018.12.037

Ranjpisheh M, Karimpour Reihan M, Zehtabian GhR, Khosravi H (2018) Assessment of drought and landuse changes: impacts on groundwater quality in Shabestar basin. North of Lake Urmia Desert 23(1):9–19

Reinecke R, Wachholz A, Mehl S, Foglia L, Niemann C, Döll P (2020) (2020) Importance of spatial resolution in global groundwater modeling. Groundwater 58:363–376

Roshni T, Jha MK, Deo RC, Vandana A (2019) Development and evaluation of hybrid artificial neural network architectures for modeling spatio-temporal groundwater fluctuations in a complex aquifer system. Water Resour Manage 33:2381–2397. https://doi.org/10.1007/s11269-019-02253-4

Roshni T, Jha MK, Drisya J (2020) Neural network modeling for groundwater-level forecasting in coastal aquifers. Neural Comput & Appl 32:12737–12754. https://doi.org/10.1007/s00521-020-04722-z

Roshni T, Jha MK, Kamii Y (2008) Modeling groundwater fluctuations in unconfined aquifers by Artificial Neural Networks. In: Conference: second international junior researcher and engineer workshop on hydraulic structures at: Pisa, Italy.

Roy DK, Biswas SK, Mattar MA, El-Shafei AA, Murad KFI, Saha KK, Datta B, Dewidar AZ (2021) Groundwater level prediction using a multiple objective genetic algorithm-grey relational analysis based weighted ensemble of ANFIS models. Water 13:3130. https://doi.org/10.3390/w13213130.

Safavi HR, Esmikhani M (2013) Conjunctive use of surface water and groundwater: application of support vector machines (SVMs) and genetic algorithms. Water Resour Manag 27(7):2623–2644

Sahoo S, Russo TA, Elliott J, Foster I (2017) Machine learning algorithms for modeling groundwater level changes in agricultural regions of the U.S. Water Resour Res 53:3878–3895.

Sahoo S, Jha MK (2013) Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment. Hydrogeol J 21(8):1865–1887

Seifi A, Ehteram M, Singh VP, Mosavi A (2020) Modeling and uncertainty analysis of groundwater level using six evolutionary optimization algorithms hybridized with ANFIS, SVM, and ANN. Sustainability 2020(12):4023. https://doi.org/10.3390/su12104023

Sharafati A, Asadollah SBHS, Neshat A (2020) A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran. J Hydrol 591:125468. https://doi.org/10.1016/j.jhydrol.2020.125468

Sun Y, Wendi D, Kim DE, Liong S-Y (2016) Technical note: Application of artificial neural networks in groundwater table forecasting - a case study in a Singapore swamp forest. Hydrol Earth Syst Sci 20:1405–1412. https://doi.org/10.5194/hess-20-1405-2016

Supreetha B, Nayak PK, Shenoy NK (2015) Groundwater level prediction using hybrid artificial neural network with genetic algorithm. Int J Earth Sci Eng 8:2609–2615

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

Vapnik V (1995) The nature of statistical learning theory. Springer-Verlag, New York

Vapnik V (2013) The nature of statistical learning theory. Springer, New York

Wu C, Zhang X, Wang W, Lu C, Zhang Y, Qin W, Tick GR, Li B, Shu L (2021) Groundwater level modeling framework by combining the wavelet transform with a long short-term memory data-driven model. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2021.146948

Wunsch A, Liesch T, Broda S (2021) Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX). Hydrol Earth Syst Sci 25:1671–1687. https://doi.org/10.5194/hess-25-1671-2021

Xing B, Gan R, Liu G, Liu Z, Zhang J, Ren Y (2016) Monthly mean streamflow prediction based on bat algorithm-support vector machine. J Hydrol Eng 21(2):04015057

Yoon H, Jun S-C, Hyun Y, Bae G-O, Lee K-K (2011a) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol 396(1–2):128–138

Yoon H, Hyun Y, Ha K, Lee K-K, Kim G-B (2011b) A method to improve the stability and accuracy of ANN- and SVM-based time series models for longterm groundwater level predictions. Comp Geosci. https://doi.org/10.1016/j.cageo.2016.03.002

Zounemat-Kermani M, Kişi Ö, Adamowski J, Ramezani-Charmahineh A (2016) Evaluation of data driven models for river suspended sediment concentration modeling. J Hydrol 535:457–472