qanat-discharge-prediction-using-a-comparative-analysis-of-machine-learning-methods-–-springer

Qanat discharge prediction using a comparative analysis of machine learning methods – Springer

  • Aghelpour P, Varshavian V (2020) Evaluation of stochastic and artificial intelligence models in modeling and predicting of river daily flow time series. Stoch Env Res Risk Assess 34(1):33–50. https://doi.org/10.1007/s00477-019-01761-4

    Article  Google Scholar 

  • Ahmadi A, Olyaei M, Heydari Z, Emami M, Zeynolabedin A, Ghomlaghi A, Sadegh M (2022) Groundwater Level modeling with machine learning: a systematic review and Meta-analysis. Water 14(6):949. https://doi.org/10.3390/w14060949

    Article  CAS  Google Scholar 

  • Antonopoulos VZ, Gianniou SK (2022) Analysis and modelling of temperature at the water–atmosphere interface of a Lake by Energy Budget and ANNs models. Environ Processes 9(1):1–20. https://doi.org/10.21203/rs.3.rs-843456/v1

    Article  Google Scholar 

  • Arya Azar N, Kayhomayoon Z, Ghordoyee Milan S, Zarif Sanayei H, Berndtsson R, Nematollahi Z (2022) A hybrid approach based on simulation, optimization, and estimation of conjunctive use of surface water and groundwater resources. Environ Sci Pollut Res 1–17. https://doi.org/10.1007/s11356-022-19762-2

  • Azimi H, Shiri H (2020) Ice-seabed interaction analysis in sand using a gene expression programming-based approach. Appl Ocean Res 98:102120. https://doi.org/10.1016/j.apor.2020.102120

    Article  Google Scholar 

  • Azari Rad M, Ziaei AN, Naghedifar MR (2018) Three-dimensional numerical modeling of submerged zone of Qanat hydraulics in unsteady conditions. J Hydrol Eng 23(3):04017063.

  • Bahmani R, Ouarda TB (2021) Groundwater level modeling with hybrid artificial intelligence techniques. J Hydrol 595:125659. https://doi.org/10.1016/j.jhydrol.2020.125659

    Article  Google Scholar 

  • Banadkooki FB, Ehteram M, Ahmed AN, Teo FY, Fai CM, Afan HA, El-Shafie A (2020) Enhancement of groundwater-level prediction using an integrated machine learning model optimized by whale algorithm. Nat Resour Res 29(5):3233–3252. https://doi.org/10.1007/s11053-020-09634-2

    Article  Google Scholar 

  • Boustani F (2008) Sustainable water utilization in arid region of Iran by Qanats. In Proceeding of world Academy of science, engineering and technology, 33, 213–216

  • Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):1–27. https://doi.org/10.1145/1961189.1961199

    Article  Google Scholar 

  • Cui F, Al-Sudani ZA, Hassan GS, Afan HA, Ahammed SJ, Yaseen ZM (2022) Boosted artificial intelligence model using improved alpha-guided grey wolf optimizer for groundwater level prediction: comparative study and insight for federated learning technology. J Hydrol 606:127384. https://doi.org/10.1016/j.jhydrol.2021.127384

    Article  Google Scholar 

  • Dehghani R, Poudeh T, H (2022) Application of novel hybrid artificial intelligence algorithms to groundwater simulation. Int J Environ Sci Technol 19(5):4351–4368. https://doi.org/10.1007/s13762-021-03596-5

    Article  CAS  Google Scholar 

  • Ebrahimi M, Sarikhani MR, Shiri J, Shahbazi F (2021) Modeling soil enzyme activity using easily measured variables: Heuristic alternatives. Appl Soil Ecol 157:103753.

  • Fels AEA, Ghorfi E, M (2022) Using remote sensing data for geological mapping in semi-arid environment: a machine learning approach. Earth Sci Inf 15(1):485–496

    Article  Google Scholar 

  • Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. arXiv preprint cs/0102027. https://doi.org/10.48550/arXiv.cs/0102027

  • Ganjeizadeh Rohani F, Mohamadi N, Ganjei-Zadeh K (2024) Heavy metal distribution and assessment in Qanat system water sourced from the mountains surrounding the copper mine. Sustainable Water Resour Manage 10(3):107

    Article  Google Scholar 

  • Ghazi B, Jeihouni E, Kalantari Z (2021a) Predicting groundwater level fluctuations under climate change scenarios for Tasuj plain, Iran. Arab J Geosci 14(2):1–12. https://doi.org/10.1007/s12517-021-06508-6

    Article  CAS  Google Scholar 

  • Ghazi B, Jeihouni E, Kouzehgar K, Haghighi AT (2021b) Assessment of probable groundwater changes under representative concentration pathway (RCP) scenarios through the wavelet–GEP model. Environ Earth Sci 80(12):1–15. https://doi.org/10.1007/s12665-021-09746-9

    Article  Google Scholar 

  • Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison Read. https://doi.org/10.5555/534133

    Article  Google Scholar 

  • Gu Y, Zhao W, Wu Z (2010) Least squares support vector machine algorithm [J]. J Tsinghua Univ (Science Technology) 7:1063–1066

    Google Scholar 

  • Guzman SM, Paz JO, Tagert MLM, Mercer AE (2019) Evaluation of seasonally classified inputs for the prediction of daily groundwater levels: NARX networks vs support vector machines. Environ Model Assess 24(2):223–234. https://doi.org/10.1007/s10666-018-9639-x

    Article  Google Scholar 

  • Haykin S (2004) Neural networks: a Comprehensive Foundation. Prentice Hall, New Jersey

    Google Scholar 

  • Iqbal M, Naeem UA, Ahmad A, Ghani U, Farid T (2020) Relating groundwater levels with meteorological parameters using ANN technique. Measurement 166:108163. https://doi.org/10.1016/j.measurement.2020.108163

    Article  Google Scholar 

  • Ivakhnenko AG (1968) The group method of data of handling; a rival of the method of stochastic approximation. Soviet Automatic Control 13:43–55

    Google Scholar 

  • Jaafari A, Panahi M, Mafi-Gholami D, Rahmati O, Shahabi H, Shirzadi A, Pradhan B (2022) Appl Soft Comput 116:108254. https://doi.org/10.1016/j.asoc.2021.108254. Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides.

  • Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst man Cybernetics 23(3):665–685. https://doi.org/10.1109/21.256541

    Article  Google Scholar 

  • Kamali MZ, Davoodi S, Ghorbani H, Wood DA, Mohamadian N, Lajmorak S, Band SS (2022) Permeability prediction of heterogeneous carbonate gas condensate reservoirs applying group method of data handling. Mar Pet Geol 139:105597. https://doi.org/10.1016/j.marpetgeo.2022.105597

    Article  Google Scholar 

  • Khedri A, Kalantari N, Vadiati M (2020) Comparison study of artificial intelligence method for short term groundwater level prediction in the northeast Gachsaran unconfined aquifer. Water Supply 20(3):909–921. https://doi.org/10.2166/ws.2020.015

    Article  Google Scholar 

  • Khodakhah H, Aghelpour P, Hamedi Z (2022) Comparing linear and nonlinear data-driven approaches in monthly river flow prediction, based on the models SARIMA, LSSVM, ANFIS, and GMDH. Environ Sci Pollut Res 29(15):21935–21954. https://doi.org/10.1007/s11356-020-10543-3

    Article  CAS  Google Scholar 

  • Kiyani V, Esmaili A, Alijani F, Samani S, Vasić L (2022) Investigation of drainage structures in the karst aquifer system through turbidity anomaly, hydrological, geochemical and stable isotope analysis (Kiyan springs, western Iran). Environ Earth Sci 81(22):517

    Article  Google Scholar 

  • Koza JRGP (1992) On the programming of computers by means of natural selection. Genetic programming

  • Kumar M, Kar IN (2009) Nonlinear HVAC computations using least square support vector machines. Energy Conv Manag 50(6):1411–1418. https://doi.org/10.1016/j.enconman.2009.03.009

    Article  Google Scholar 

  • Lemke F (1997) Knowledge extraction from data using self-organizing modeling technologies. In Proceedings of the SEAM’97 Conference

  • Li D, Armaghani DJ, Zhou J, Lai SH, Hasanipanah M (2020) A GMDH predictive model to predict rock material strength using three non-destructive tests. J Nondestr Eval 39(4):1–14. https://doi.org/10.1007/s10921-020-00725-x

    Article  Google Scholar 

  • Lin L, Li S, Sun S, Yuan Y, Yang M (2020) A novel efcient model for gas compressibility factor based on GMDH network. Flow Meas Instrum 71:101677. https://doi.org/10.1016/j.flowmeasinst.2019.101677

    Article  Google Scholar 

  • Mathworks M (2014) Fuzzy logic toolbox. User’s Guide, The Mathworks, Massachusetts

    Google Scholar 

  • McGarry KJ, Wermter S, MacIntyre J (1999) Knowledge extraction from radial basis function networks and multilayer perceptrons. In IJCNN’99. International Joint Conference on Neural Networks. Proceedings (Cat. No. 99CH36339) (Vol. 4, pp. 2494–2497. IEEE https://doi.org/10.1109/IJCNN.1999.833464

  • Mehdizadeh S, Behmanesh J, Khalili K (2017) Application of gene expression programming to predict daily dew point temperature. Appl Therm Eng 112:1097–1107. https://doi.org/10.1016/j.applthermaleng.2016.10.181

    Article  Google Scholar 

  • Miraki S, Zanganeh SH, Chapi K, Singh VP, Shirzadi A, Shahabi H, Pham BT (2019) Mapping groundwater potential using a novel hybrid intelligence approach. Water Resour Manage 33(1):281–302. https://doi.org/10.1007/s11269-018-2102-6

    Article  Google Scholar 

  • Moghaddam HK, Milan SG, Kayhomayoon Z, Azar NA (2021) The prediction of aquifer groundwater level based on spatial clustering approach using machine learning. Environ Monit Assess 193(4):1–20. https://doi.org/10.1007/s10661-021-08961-y

    Article  CAS  Google Scholar 

  • Mohajerani M, Dokhanian F, Estaji H, Boer D, Norouzi M (2024) Geospatial distribution of qanats in middle eastern countries: potential for sustainable groundwater system. J Arid Environ 222:105170

    Article  Google Scholar 

  • Molle F, Mamanpoush A, Miranzadeh M (2004) Robbing Yadullah’s water to irrigate Saeid’s garden: Hydrology and water rights in a village of central Iran (Vol. 80). IWMI

  • 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 Sustainable Dev 11:100447. https://doi.org/10.1016/j.gsd.2020.100447

    Article  Google Scholar 

  • Moriasi DN, Gitau MW, Pai N, Daggupati P (2015) Hydrologic and water quality models: performance measures and evaluation criteria. Trans ASABE 58(6):1763–1785. https://doi.org/10.13031/trans.58.10715

    Article  Google Scholar 

  • Mozaffari S, Javadi S, Moghaddam HK, Randhir TO (2022) Forecasting Groundwater levels using a hybrid of support Vector Regression and particle swarm optimization. Water Resour Manage 1–18. https://doi.org/10.1007/s11269-022-03118-z

  • Mueller JA, Ivachnenko AG, Lemke F (1998) GMDH algorithms for complex systems modelling. Math Comp Model Dyn Sys 4(4):275–316.

  • Mulashani AK, Shen C, Nkurlu BM, Mkono CN, Kawamala M (2022) Enhanced group method of data handling (GMDH) for permeability prediction based on the modified Levenberg Marquardt technique from well log data. Energy 239:121915. https://doi.org/10.1016/j.energy.2021.121915

    Article  Google Scholar 

  • Nadiri AA, Habibi I, Gharekhani M, Sadeghfam S, Barzegar R, Karimzadeh S (2022) Introducing dynamic land subsidence index based on the ALPRIFT framework using artificial intelligence techniques. Earth Sci Inf 1–15. https://doi.org/10.1007/s12145-021-00760-w

  • Naghibi SA, Pourghasemi HR, Abbaspour K (2018) A comparison between ten advanced and soft computing models for groundwater Qanat potential assessment in Iran using R and GIS. Theoret Appl Climatol 131(3):967–984. https://doi.org/10.1007/s00704-016-2022-4

    Article  Google Scholar 

  • Najafabadipour A, Kamali G, Nezamabadi-Pour H (2022) Application of Artificial Intelligence techniques for the determination of Groundwater Level using spatio–temporal parameters. ACS Omega 7(12):10751–10764. https://doi.org/10.1021/acsomega.2c00536

    Article  CAS  Google Scholar 

  • Nariman-Zadeh N, Darvizeh A, Darvizeh M, Gharababaei H (2002) Modelling of explosive cutting process of plates using GMDH-type neural network and singular value decomposition. J Mater Process Technol 128(1–3):80–87. https://doi.org/10.1016/S0924-0136(02)00264-9

    Article  Google Scholar 

  • Nasiri F, Mafakheri MS (2015) Qanat water supply systems: a revisit of sustainability perspectives. Environ Syst Res 4(1):1–5. https://doi.org/10.1186/s40068-015-0039-9

    Article  Google Scholar 

  • Patel MB, Patel JN, Bhilota UM (2022) Comprehensive Modelling of ANN. In Research Anthology on Artificial neural network applications. IGI Global 31–40. https://doi.org/10.4018/978-1-6684-2408-7.ch002

  • Pham QB, Kumar M, Di Nunno F, Elbeltagi A, Granata F, Islam ARM, Anh DT (2022) Groundwater level prediction using machine learning algorithms in a drought-prone area. Neural Comput Appl 1–23. https://doi.org/10.1007/s00521-022-07009-7

  • Platt JC (1999) Fast training of support vector machines using sequential minimal optimization, advances in kernel methods. Support Vector Learn 185–208. https://doi.org/10.1109/ISKE.2008.4731075

  • Poursaeid M, Poursaeid AH, Shabanlou S (2022) A comparative study of Artificial Intelligence models and A Statistical Method for Groundwater Level Prediction. Water Resour Manage 1–21. https://doi.org/10.1007/s11269-022-03070-y

  • 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. https://doi.org/10.1007/s10040-013-1029-5

    Article  Google Scholar 

  • Samani S (2024) Unraveling aquifer dynamics: Time series evaluation for informed groundwater management. Groundw Sustainable Dev 25:101174

    Article  Google Scholar 

  • Samani S, Boustani F, Hojati MH (2013) Screen for heavy metals from groundwater samples from industrialized zones in Marvdasht, Kharameh and Zarghan plains, Shiraz, Iran. World Appl Sci J 22(3):380–388

    CAS  Google Scholar 

  • Samani S, Vadiati M, Azizi F, Zamani E, Kisi O (2022) Groundwater level simulation using soft computing methods with emphasis on major meteorological components. Water Resour Manage 36(10):3627–3647

    Article  Google Scholar 

  • Samani S, Vadiati M, Delkash M, Bonakdari H (2023) A hybrid wavelet–machine learning model for qanat water flow prediction. Acta Geophys 71(4):1895–1913

    Article  Google Scholar 

  • Samani S, Vadiati M, Nejatijahromi Z, Etebari B, Kisi O (2023b) Groundwater level response identification by hybrid wavelet–machine learning conjunction models using meteorological data. Environ Sci Pollut Res 30(9):22863–22884

    Article  Google Scholar 

  • Samantaray S, Sahoo A (2023) Prediction of flow discharge in Mahanadi River Basin, India, based on novel hybrid SVM approaches. Environment, Development and Sustainability, pp 1–25

  • Samantaray S, Biswakalyani C, Singh DK, Sahoo A, Satapathy P, D (2022) Prediction of groundwater fluctuation based on hybrid ANFIS-GWO approach in arid Watershed, India. Soft Comput 1–23. https://doi.org/10.1007/s00500-022-07097-6

  • Samantaray S, Sahoo A, Agnihotri A (2023a) Prediction of flood discharge using hybrid PSO-SVM algorithm in Barak River Basin. MethodsX 10:102060

    Article  Google Scholar 

  • Samantaray S, Sahoo P, Sahoo A, Satapathy DP (2023b) Flood discharge prediction using improved ANFIS model combined with hybrid particle swarm optimisation and slime mould algorithm. Environ Sci Pollut Res 30(35):83845–83872

    Article  Google Scholar 

  • Sanikhani H, Kisi O (2012) River flow estimation and forecasting by using two different adaptive neuro-fuzzy approaches. Water Resour Manage 26(6):1715–1729. https://doi.org/10.1007/s11269-012-9982-7

    Article  Google Scholar 

  • Sedghi MM, Zhan H (2020) Semi-analytical solutions of discharge variation of a Qanat in an unconfined aquifer subjected to general areal recharge and nearby pumping well discharge. J Hydrol 584:124691. https://doi.org/10.1016/j.jhydrol.2020.124691

    Article  Google Scholar 

  • Sedghi MM, Zhan H (2024) Discharge variations of qanat near an ephemeral stream. J Hydrol, 131367

  • Sreelakshmi S, Shaji E (2022) Landslide identification using machine learning techniques: review, motivation, and future prospects. Earth Sci Inf 15(4):2063–2090

    Article  Google Scholar 

  • Sridharam S, Sahoo A, Samantaray S, Ghose DK (2021) Estimation of water table depth using wavelet-ANFIS: a case study. Communication Software and Networks. Springer, Singapore, pp 747–754. https://doi.org/10.1007/978-981-15-5397-4_76

    Chapter  Google Scholar 

  • Sun J, Hu L, Li D, Sun K, Yang Z (2022) Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management. J Hydrol 608:127630. https://doi.org/10.1016/j.jhydrol.2022.127630

    Article  Google Scholar 

  • 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.2014.05.026

    Article  Google Scholar 

  • Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300. https://doi.org/10.1023/A:1018628609742

    Article  Google Scholar 

  • Tao H, Hameed MM, Marhoon HA, Zounemat-Kermani M, Salim H, Sungwon K, Yaseen ZM (2022) Groundwater Level Prediction using machine learning models: a Comprehensive Review. Neurocomputing. https://doi.org/10.1016/j.neucom.2022.03.014

    Article  Google Scholar 

  • Tao H, Abba SI, Al-Areeq AM, Tangang F, Samantaray S, Sahoo A, Yaseen ZM (2024) Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: a comprehensive review, assessment, and possible future research directions. Eng Appl Artif Intell 129:107559

    Article  Google Scholar 

  • Tayebi HA, Ghanei M, Aghajani K, Zohrevandi M (2019) Modeling of reactive orange 16 dye removal from aqueous media by mesoporous silica/crosslinked polymer hybrid using RBF, MLP and GMDH neural network models. J Mol Struct 1178:514–523. https://doi.org/10.1016/j.molstruc.2018.10.040

    Article  CAS  Google Scholar 

  • Tijani IA, Zayed T (2022) Gene expression programming based mathematical modeling for leak detection of water distribution networks. Measurement 188:110611. https://doi.org/10.1016/j.measurement.2021.110611

    Article  Google Scholar 

  • Vapnik V (1998) Statistical learning theory. John wiley&sons. Inc., New York, p 1

    Google Scholar 

  • Wee WJ, Zaini NAB, Ahmed AN, El-Shafie A (2021) A review of models for water level forecasting based on machine learning. Earth Sci Inf 14:1707–1728

    Article  Google Scholar 

  • Yazdi AAS, Khaneiki ML (2016) Qanat knowledge: construction and maintenance. Springer