COMPARATIVE STUDY OF MACHINE LEARNING MODELS IN PREDICTING WATER TABLE FLUCTUATIONS IN AZARSHAHR PLAIN, IRAN

S.M. FATEMI, A. MOLAVI

Abstract


Managing drought and water scarcity concerning underground water resources requires modeling methodologies with a simple yet effective framework. Due to temporal and financial constraints, machine learning models (MLMs) are crucial in this context. This study aims to predict daily underground water levels (UWL) in Azarshar Plain, Tabriz, Iran, using three MLMs (SVM, GEP, MLP). Covering 126 annual datasets for 34 wells from 2018 to 2021, various combinations were tested with different UWLs and lag times. Performance evaluation metrics including RMSE, MAE, R2, and DDR were employed. Results show satisfactory accuracy for all three MLMs, with SVM, GEP, and MLP being more accurate in 53%, 26%, and 20% of cases respectively among the 34 wells. The input configuration with a lag-time of two days (M2) emerged as the most optimal, yielding the most accurate simulations. Average values of RMSE, MAE, R2, and DDR for M2 during the testing period were calculated as 0.2457, 0.2077, 0.9482, and 31.53 respectively. In conclusion, these MLMs offer viable alternatives to numerical models for managing and predicting UWL, facilitating better water resource management in areas prone to drought and water scarcity.


Keywords


Simulation, Aquifer, Prediction, Performance Assessment, Groundwater.

Full Text:

PDF

References


ABAIDIA S., REMINI B. (2020). Ghrib and Boukourdane (Algeria): when the water discharged from the dams feed the alluvial aquifer, Larhyss Journal, No 44, pp. 133-159.

ABBASZADEH SHAHRI A., SHAN C., LARSSON S. (2022). A novel approach to uncertainty quantification in groundwater table modeling by automated predictive deep learning, Natural Resources Research, Vol. 31, Issue 3, pp. 1351-1373.

ADEREMI B.A., OLWAL T.O., NDAMBUKI J.M., RWANGA S.S. (2023). Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa, Systems and Soft Computing, Vol. 5, Paper ID 200049.

AFZAAL H., FAROOQUE A.A., ABBAS F., ACHARYA B., ESAU T. (2019). Groundwater estimation from major physical hydrology components using artificial neural networks and deep learning, Water, Vol. 12, Issue 5, pp 1-18.

AHMED A.K.A., EL-RAWY M., IBRAHEEM A.M., AL-ARIFI N., ABD-ELLAH M. K. (2023). Forecasting of Groundwater Quality by Using Deep Learning Time Series Techniques in an Arid Region, Sustainability, Vol. 15, Issue 8, pp. 1-16.

ALSHEHRI F., RAHMAN A. (2023). Coupling Machine and Deep Learning with Explainable Artificial Intelligence for Improving Prediction of Groundwater Quality and Decision-Making in Arid Region, Saudi Arabia, Water, Vol. 15, Issue 12, pp. 1-28.

ARGAZ A. (2018). 1d model application for integrated water resources planning and evaluation: case study of Souss river basin, Morocco, Larhyss Journal, No 36, pp. 217-229.

AROUA N. (2018). Water resources in SNAT 2030. between economic needs and ecological requirements, Larhyss Journal, No 35, pp. 153-168. (In French)

AZAMATHULLA H.M., AHMAD Z. (2013). Estimation of critical velocity for slurry transport through pipeline using adaptive neuro-fuzzy interference system and gene-expression programming, Journal of Pipeline Systems Engineering and Practice, Vol. 4, Issue 2, pp. 131-137.

AZAMATHULLA H.M., RATHNAYAKE U., SHATNAWI A. (2018). Gene expression programming and artificial neural network to estimate atmospheric temperature in Tabuk, Saudi Arabia, Applied Water Science, Vol. 8, pp. 1-7.

BAHIR M., EL MOUKHAYAR R., CARREIRA P. SOUHEL A. (2015). Isotopic tools for groundwater management in semi-arid area: case of the wadi Ouazzi basin (Morocco), Larhyss Journal, No 23, pp. 23-39.

BAHMANI R., OUARDA T.B. (2021). Groundwater level modeling with hybrid artificial intelligence techniques, Journal of Hydrology, Vol. 595, Paper ID 125659.

BAICHE A., SIDI MOHAMED H., ABLAOUI H. (2015). Overexploitation of water resources of the Mostaganem plateau aquifer, Larhyss Journal, No 22, pp. 153-165. (In French)

BALAHANG S., GHODSIAN M. (2023). Estimation of rectangular and triangular side weir discharge, ISH Journal of Hydraulic Engineering, Vol. 29, Issue 1, pp. 12-23.

BAUDHANWALA D., MEHTA D., KUMAR V. (2024). Machine learning approaches for improving precipitation forecasting in the Ambica River basin of Navsari District, Gujarat, Water Practice & Technology, Vol., 19, Issue 4, pp. 1315-1329.

BELHADJ M.Z., BOUDOUKHA A., AMROUNE A., GAAGAI A., ZIANI D. (2017). Statistical characterization of groundwater quality of the northern area of the basin of Hodna, M’sila, southeastern Algeria, Larhyss Journal, No 31, pp. 177-194. (In French)

BEMMOUSSAT A., ADJIM M., BENSAOULA F (2017). Use of the ZYGOS model for the estimation of groundwater recharge in Sikkak watershed (Northen west of Algeria), Larhyss Journal, No 30, pp. 105-119. (In French)

BIRBAL P., AZAMATHULLA H., LEON L., KUMAR V., HOSEIN J. (2021). Predictive modelling of the stage–discharge relationship using gene-expression programming, Water supply, Vol. 21, Issue 7, pp. 3503-3514.

CHIBANE B., ALI-RAHMANI S.E. (2015). Hydrological based model to estimate groundwater recharge, real- evapotranspiration and runoff in semi-arid area, Larhyss Journal, No 23, pp. 231-242.

CORTES C., VAPNIK V. (1995). Support-vector networks, Machine learning, Vol. 20, pp. 273-297

CORTES C., VAPNIK V.N. (1995). Support-vector networks, Machine learning, Vol. 20, Issue 3, pp. 273-297.

DADHICH A.P., GOYAL R., DADHICH, P.N. (2021). Assessment and prediction of groundwater using geospatial and ANN modeling, Water resources management, Vol. 35, pp. 2879-2893.

DAS U.K., ROY P., GHOSE D.K. (2019). Modeling water table depth using adaptive neuro-fuzzy inference system, ISH journal of hydraulic engineering, Vol. 25, Issue 3, pp. 291-297.

DEB S. (2024). Optimizing hydrological exploration through GIS-based groundwater potential zoning in Gomati district, Tripura, India, Larhyss Journal, No 60, pp. 231-256.

CHIBANE B., ALI-RAHMANI S.E. (2015). Hydrological based model to estimate groundwater recharge, real- evapotranspiration and runoff in semi-arid area, Larhyss Journal, No 23, pp. 231-242.

EBRAHIMI H., RAJAEE T. (2017). Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine, Global and planetary change, Vol. 148, pp. 181-191.

EL FELLAH IDRISSI B., CHERAI B., HINAJE S., MEHDI K. (2017). Climatic variability and its influence on water resources in the northern part of the middle atlas Moroccan: the case of the Sefrou and the Anoceur causses, Larhyss Journal, No 32, pp. 155-179. (In French)

EL MOUKHAYAR R., BAHIR M., CARREIRA P. (2015). Estimation of groundwater recharge in arid region through hydrochemistry and isotope: a case study Kourimat basin Morocco, Larhyss Journal, No 23, pp. 87-104.

FERREIRA C. (2001). Gene expression programming a new adaptive algorithm for solving problems, Complex systems, Vol. 13, Issue 2, pp. 87-129.

FULADIPANAH M., AZAMATHULLA H.M., TOTA-MAHARAJ K., MANDALA V., CHADEE A. (2023). Precise forecasting of scour depth downstream of flip bucket spillway through data-driven models, Results in engineering, Vol. 20, Paper ID 101604.

FULADIPANAH M., MAJEDI ASL M., JAFARINIA R. (2020). Application and assessment of svm algorithm to simulate the geometry of scour hole downstream of a siphon spillway, Iranian journal of irrigation & drainage, Vol. 14, Issue 3, pp. 1032-1045.

FULADIPANAH M., MAJEDIASL M. (2021). Assessment of the geometric shape of bridge pier on the scour depth using the support vector machine, Jwss-isfahan university of technology, Vol. 24, Issue 4, pp. 197-210.

GAALOUL N. (2015). Modeling of underground flows in unsaturated porous medium: application to artificial recharge by treated wastewater - Korba coastal water table (cap-bon, Tunisia), Larhyss Journal, No 21, pp. 181-190. (In French)

HAOUCHINE A., HAOUCHINE F.Z., LABADI A. (2015). Climatic change and anthropic activities: impacts on coastal aquifers in Algeria, Larhyss Journal, No 24, pp. 227-241. (In French)

HOUNTONDJI B., CODO F.P., AINA M.P. (2020). Characterization of hydrogeological conditions from the Monzoungoudo groundwater reservoir in Benin, Larhyss Journal, No 41, pp. 223-232. (In French)

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, Environmental earth sciences, Vol. 78, Issue 10, Paper ID 293.

KANTHARIA V., MEHTA D., KUMAR V., SHAIKH M.P., JHA S. (2024). Rainfall–runoff modeling using an Adaptive Neuro-Fuzzy Inference System considering soil moisture for the Damanganga basin, Journal of Water and Climate Change, Vol. 15, Issue 5, pp. 2518-2531.

KAUSHIK V., KUMAR M. (2023). Water surface profile prediction in non-prismatic compound channel using support vector machine (SVM), AI in civil engineering, Vol. 2, Issue 6, pp. 1-12.

KHEBIZI H., BENLAOUKLI B., CHAOUCHE M., CHACHA B. (2023). The Ghout of El Oued in Algeria: a patrimony and a natural hydroagrarian alarm system to advance, Larhyss Journal, No 55, pp. 107-124.

KOUASSI A.M., KOUAME K.F., SALEY M.B., BIEMI J. (2013). Impacts of climate change on groundwater of crystalline and Crystalophyllian basement aquifers in west Africa: case of the N’zi-Bandama watershed (Ivory Coast), Larhyss Journal, No 16, pp. 121-138. (In French)

KOUSSA M., BERHAIL S. (2021). Evaluation of spatial interpolation techniques for mapping groundwater nitrates concentrations - case study of Ain Elbel-sidi Makhlouf syncline in the Djelfa region (Algeria), Larhyss Journal, No 45, pp. 119-140.

LAGHZAL A., SALMOUN F., BOUDINAR B., ARGAZ A. (2019). Potential impact of anthropogenic activities on groundwater in the Tangier-Tetouan-Alhoceima region (Morocco), Larhyss Journal, No 34, pp. 39-50.

LATER F., LABADI A.S. (2024). Origin of the alluvial aquifer’s groundwater in wadi Biskra (Algeria), Larhyss Journal, No 57, pp. 145-158.

LEON L.P., AZAMATHULLA H., FELIX P., PRASAD C.V.S.R. (2023). Prediction of stiffness modulus of bituminous mixtures using the applications of multi-expression programming and gene expression programming, Road materials and pavement design, Vol. 24, Issue 9, pp. 2192-2208.

LI H., LU Y., ZHENG C., YANG M., LI S. (2019). Groundwater level prediction for the arid oasis of northwest china based on the artificial bee colony algorithm and a back-propagation neural network with double hidden layers, Water, Vol. 11, Issue 4, p. 860.

MAJEDI-ASL M., FULADIPANAH M., ARUN V., TRIPATHI R.P. (2022). Using data mining methods to improve discharge coefficient prediction in piano key and labyrinth weirs, Water supply, Vol. 22, Issue 2, pp. 1964-1982.

MEHTA D., DHABUWALA J., YADAV S.M., KUMAR V., AZAMATHULLA H.M. (2023). Improving flood forecasting in Narmada River basin using hierarchical clustering and hydrological modelling, Results in Engineering, Vol. 20, Paper ID 101571.

MERONI E., PIÑEIRO G., GOMBERT P. (2021). Geological and hydrogeological reappraisal of the Guaraní aquifer system in the Uruguayan area, Larhyss Journal, No 48, pp. 109-133.

MOHAMMED K.S., SHABANLOU S., RAJABI A., YOSEFVAND F., IZADBAKHSH M.A. (2023). Prediction of groundwater level fluctuations using artificial intelligence-based models and GMS, Applied water science, Vol. 13, Issue 2, pp. 1-14.

MOZAFFARI S., JAVADI S., MOGHADDAM H.K., RANDHIR T.O. (2022). Forecasting groundwater levels using a hybrid of support vector regression and particle swarm optimization, Water resources management, Vol. 36, Issue 6, pp. 1955-1972.

NAKOU T.R., SENOU L., ELEGBEDE B., CODO F.P. (2023). Climate variability and its impact on water resources in the lower mono river valley in Benin from 1960 to 2018, Larhyss Journal, No 56, pp. 215-234.

NGOUALA M.M., MBILOU U.G., TCHOUMOU M., SAMBA-KIMBATA M.J. (2016). Characterization surface water - groundwater aquifer in coastal watershed of the republic of Congo Loémé, Larhyss Journal, No 28, pp. 237-256. (In French)

NICHANE M., KHELIL M.A. (2015). Climate change and water resources in Algeria - vulnerability, impact and adaptation strategy, Larhyss Journal, No 21, pp. 25-33. (In French)

NOORI R., KHAKPOUR A., OMIDVAR B., FAROKHNIA A. (2010) comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistics, Expert systems with applications, Vol. 37, pp. 5856-5862.

OUHAMDOUCH S, BAHIR M., CARREIRA P., CHKIR N., GOUMIH A. (2016). Climate change impact on Hauterivian aquifer of Essaouira basin (Morocco), Larhyss Journal, No 25, pp. 269-283. (In French)

QURESHI H.U., ABBAS I., SHAH S.M.H., TEO F.Y. (2024). Hydrologic evaluation of monthly and annual groundwater recharge dynamics for a sustainable groundwater resources management in Quetta city, Pakistan, Larhyss Journal, No 60, pp. 27-53.

RAJPUT D.C., MISTRY K.P., BHORANIYA J.K., UMRIGAR J.N., WAIKHOM S.I. (2023). Assessing the decadal groundwater level fluctuation - a case study of Gujarat, India, Larhyss Journal, No 54, pp. 175-191.

REMINI B. (2018). The foggaras of the oasis of Ghardaia (Algeria): the sharing of flood waters, Larhyss Journal, No 36, pp. 157-178. (In French)

SATTARI M.T., MIRABBASI R., SUSHAB R.S., ABRAHAM J. (2018). Prediction of groundwater level in Ardebil plain using support vector regression and m5 tree model, Groundwater, Vol. 56, Issue 4, pp. 636-646.

SEIFI A., EHTERAM M., SINGH V.P., MOSAVI A. (2020). Modeling and uncertainty analysis of groundwater level using six evolutionary optimization algorithms hybridized with ANFIS, SVM, and ANN, Sustainability, Vol. 12, Issue 10, pp. 1-42.

SRIVASTAVA D.K., SHUKLA A., JEMNI D. (2023). Prediction of ground water level in rajasthan state using machine learning, Procedia computer science, Vol. 218, pp. 1702-1711.

UMRIGAR J., MEHTA D.J., CALOIERO T., AZAMATHULLA H.M., KUMAR V. (2023). A comparative study for provision of environmental flows in the Tapi River, Earth, Vol. 4, Issue 3, pp. 570-583.

VADIATI M., RAJABI YAMI Z., ESKANDARI E., NAKHAEI M., KISI O. (2022). Application of artificial intelligence models for prediction of groundwater level fluctuations: case study (tehran-karaj alluvial aquifer), Environmental monitoring and assessment, Vol. 194, Issue 9, Paper ID 619.

VU M.T., JARDANI A., MASSEI N., FOURNIER M. (2021). Reconstruction of missing groundwater level data by using Long Short-Term Memory (LSTM) deep neural network, Journal of Hydrology, Vol. 597, Paper ID 125776.

YAO Z., WANG Z., WU T., LU W. (2024). A hybrid data-driven deep learning prediction framework for lake water level based on fusion of meteorological and hydrological multi-source data, Natural Resources Research, Vol. 33, Issue 1, pp. 163-190.

YI S., KONDOLF G.M., SANDOVAL SOLIS S., DALE L. (2024). Groundwater Level forecasting using machine learning: a case study of the Baekje Weir in Four Major Rivers Project, South Korea, Water Resources Research, Vol. 60, Issue 5, Paper ID e2022WR032779.

YOON H., JUN S.C., HYUN Y., BAE G.O., LEE K.K. (2011). A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer, Journal of hydrology, Vol. 396, Issues 1-2, pp. 128-138.

ZEGAIT R., REMINI B., BENSAHA H., (2021). Groundwater vulnerability assessment in the M’zab valley - southern Algeria, Larhyss Journal, No 48, pp. 211-234.

ZELLA L., SMADHI D. (2010). Water shortage in Arab countries and the need for the use of non-conventional water, Larhyss Journal, No 8, pp. 149-166. (In French)


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.