|Citation:||Abdullah Al Jami, Meher Uddin Himel, Khairul Hasan, Shilpy Rani Basak, Ayesha Ferdous Mita. NARX neural network approach for the monthly prediction of groundwater levels in Sylhet Sadar, Bangladesh[J]. Journal of Groundwater Science and Engineering, 2020, 8(2): 118-126. doi: 10.19637/j.cnki.2305-7068.2020.02.003|
Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of groundwater resources. In this study, a new nonlinear autoregressive with exogenous inputs (NARX) network has been applied to simulate monthly groundwater levels in a well of Sylhet Sadar at a local scale. The Levenberg-Marquardt (LM) and Bayesian Regularization (BR) algorithms were used to train the NARX network, and the results were compared to determine the best architecture for predicting monthly groundwater levels over time. The comparison between LM and BR showed that NARX-BR has advantages over predicting monthly levels based on the Mean Squared Error (MSE), coefficient of determination (R2), and Nash-Sutcliffe coefficient of efficiency (NSE). The results show that BR is the most accurate method for predicting groundwater levels with an error of ± 0.35 m. This method is applied to the management of irrigation water source, which provides important information for the prediction of local groundwater fluctuation at local level during a short period.
Chan RWK, Yuen JKK, Lee EWM, et al. 2015. Application of nonlinear-autoregressiveexogenous model to predict the hysteretic behavior of passive control systems. Engineering Structures, 85: 1-10. https://doi.org/10.1016/j.engstruct.2014.12.007.
quality. CLEAN–Soil Air Water, 43(4): 551-560.
districts of Punjab, India. Journal of Earth Science and Climatic Change, 06(04): 1000274.https://doi.org/10.4172/2157-7617.1000274.
Ravikumar P, Somashekar RK, Angami M. 2011. Hydrochemistry and evaluation of groundwater suitability for irrigation and
Lin T, Horne BG, Tiiio P, et al. 1996. Learning long-term dependencies in NARX recurrent neural Networks. IEEE Transactions on Neural Networks, 7(6):1329-1338.
Sahoo S, Jha MK. 2013. Groundwater-level prediction using multiple linear regression and artificial neural network techniques: A comparative assessment. Hydrogeology Journal, 21(8): 1865-1887.
Guzman SM, Paz JO, Tagert MLM. 2017b. The use of NARX neural networks to forecast daily groundwater levels. Water Resources Management, 31(5): 1591-1603.
Coulibaly P, Anctil F, Aravena R, et al. 2001. Artificial neural network modeling of water table depth fluctuations. Water Resources Research, 37(4): 885-896. https://doi.org/10.1029/2000WR900368.
Kisi O. 2007. Streamflow forecasting using different artificial neural network algorithms.Journal of Hydrologic Engineering, 12(5):
drinking purposes in the Markandeya River basin, Belgaum District, Karnataka State, India. Environmental Monitoring and
Guzman SM, Paz JO, Tagert MLM, et al. 2019. Evaluation of seasonally classified inputs for the prediction of daily groundwater levels: NARX networks vs support vector machines. Environmental Modeling and Assessment, 24(2): 223-234. https://doi.org/10.1007/s10666-018-9639-x.
Resources Research, 41(12): W12409. https://doi.org/10.1029/2005WR004152.
Lehmann EL, Casella G. 1998. Theory of point estimation (2nd ed). New York: Springer.
Ahmadian M, Chavoshian M. 2012. Spatial variability zonation of groundwater-table by use geo-statistical methods in central region
Morris BL, Lawrence ARL, Chilton PJC, et al.2003. Groundwater and its susceptibility to degradation: A global assessment of the
Daliakopoulos IN, Coulibaly P, Tsanis IK. 2005. Groundwater level forecasting using artificial neural networks. Journal of Hydrology,309(1-4): 229-240. https://doi.org/10.1016/j.jhydrol.2004.12.001.
Izady A, Davary K, Alizadeh A, et al. 2013. Application of NN-ARX model to predict groundwater levels in the Neishaboor Plain, Iran. Water Resources Management, 27(14):4773-4794. https://doi.org/10.1007/s11269-013-0432-y.
Piotrowski AP, Napiorkowski JJ. 2013. A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling. Journal of Hydrology,
Nash JE, Sutcliffe JV. 1970. River flow forecasting through conceptual models part I: A discussion of principles. Journal of Hydrology,10(3): 282-290. https://doi.org/10.1016/0022-1694(70)90255-6.
Chen S, Billings SA, Grant PM. 1990. Non-linear system identification using neural networks. International Journal of Control, 51(6): 1191-1214.
Khaki M, Yusoff I, Islami N. 2015. Application of the artificial neural network and neurofuzzy system for assessment of groundwater
Mackay JD, Jackson CR, Brookshaw A, et al. 2015. Seasonal forecasting of groundwater levels in principal aquifers of the United Kingdom. Journal of Hydrology, 530:815-828. https://doi.org/10.1016/j.jhydrol. 2015.10.018.
6(1): 97-111. https://doi.org/10.1016/j.jhydrol.2012.10.019.
Hagan MT, Menhaj MB. 1994. Training feedforward networks with the Marquardt algorithm.IEEE Transactions on Neural Networks, 5(6):989-993.
Guzman SM, Paz JO, Tagert MLM. 2017a. The use of NARX neural networks to forecast daily groundwater levels. Water Resources Management, 31(5): 1591-1603. https://doi.org/10.1007/s11269-017-1598-5.
Zahid A, Ahmed SRU. 2006. Groundwater resources development in Bangladesh: Contribution to irrigation for food security and constraints to sustainability. Groundwater Governance in Asia, S1: 25-46.
Siegelmann HT, Horne BG, Giles CL. 1998. Computational capabilities of recurrent NARX neural networks. IEEE Transactions on Systems, Man and Cybernetics, Part B(Cybernetics), 27(2): 208-215. https://doi.org/10.1109/3477.558801.
Leontaritis IJ, Billings SA. 1985. Input-output parametric models for non-linear systems part I: Deterministic non-linear systems.International Journal of Control, 41(2): 303-328.
Cadenas E, Rivera W, Campos-Amezcua R, et al. 2016. Wind speed prediction using a univariate ARIMA model and a multivariate NARX model. Energies, 9(2): 109.
of Hamadan province. Annals of Biological Research, 3(11): 5304-5312.
(10): 1989-2006. https://doi.org/10.1007/s11269-009-9534-y.
Assessment, 173(1-4): 459-487. https://doi.org/10.1007/s10661-010-1399-2.
Kingston GB, Lambert MF, Maier HR. 2005.Bayesian training of artificial neural networks used for water resources modeling. Water
Shahid S, Hazarika MK. 2010. Groundwater drought in the northwestern districts of Bangladesh. Water Resources Management,
Adeloye AJ, De Munari A. 2006. Artificial neural network based generalized storage-yieldreliability models using the Levenberg-Marquardt algorithm. Journal of Hydrology,326(1-4): 215-230.
Barzegar R, Fijani E, Asghari M, et al. 2017.Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models. Science of the Total Environment, 599-600(1): 20-31. https://doi.org/10.1016/j.scitotenv.2017.04.189.
problem and options for management.
Lohani AK, Krishan G. 2015. Application of artificial neural network for groundwater level simulation in Amritsar and Gurdaspur
|||Abdulrahman Th Mohammad, Qassem H Jalut, Nadia L Abbas. Predicting groundwater level of wells in the Diyala River Basin in eastern Iraq using artificial neural network. Journal of Groundwater Science and Engineering, 2020, 8(1): 87-96. doi: 10.19637/j.cnki.2305-7068.2020.01.009|
|||SADIKI Moulay Lhassan, EL MANSOURI Bouabid, BENSEDDIK Badr, CHAO Jamal, KILI Malika, EL MEZOUARY Lhoussaine. Improvement of groundwater resources potential by artificial recharge technique: A case study of Charf El Akab aquifer in the Tangier region, Morocco. Journal of Groundwater Science and Engineering, 2019, 7(3): 224-236. doi: DOI: 10.19637/j.cnki.2305-7068.2019.03.003|
|||ZHOU Bo, WEI Shan-ming, WANG Tao, NIE Yu-peng, WANG Chuan-qi. Discussion on establishing monitoring networks for temperature fields of shallow thermal energy in Shandong, China. Journal of Groundwater Science and Engineering, 2019, 7(1): 86-93.|
|||YANG Liu, WEN Xue-ru, WU Xiao-li, PEI Li-xin, YUE Chen, LIU Bing, GUO Si-jia. Height prediction of water flowing fractured zones based on BP artificial neural network. Journal of Groundwater Science and Engineering, 2019, 7(4): 354-359. doi: DOI: 10.19637/j.cnki.2305-7068.2019.04.006|
|||XU Jun-xiang, WANG Shao-juan, LI Chang-suo, XING Li-ting. Numerical analysis and evaluation of groundwater recession in a flood detention basin. Journal of Groundwater Science and Engineering, 2019, 7(3): 253-263. doi: DOI: 10.19637/j.cnki.2305-7068.2019.03.006|
|||A Muthamilselvan, N Rajasekaran, R Suresh. Mapping of hard rock aquifer system and artificial recharge zonation through remote sensing and GIS approach in parts of Perambalur District of Tamil Nadu, India. Journal of Groundwater Science and Engineering, 2019, 7(3): 264-281. doi: DOI: 10.19637/j.cnki.2305-7068.2019.03.007|
|||SRISUK Kriengsak, NETTASANA Tussanee. Climate change and groundwater resources in Thailand. Journal of Groundwater Science and Engineering, 2017, 5(1): 67-75.|
|||Chamroeun SOK, Sokuntheara CHOUP. Climate change and groundwater resources in Cambodia. Journal of Groundwater Science and Engineering, 2017, 5(1): 31-43.|
|||LI Xiao-yuan, YUE Gao-fan, SU Ran, YU Juan. Research on Pisha-sandstone’s anti-erodibility based on grey multi-level comprehensive evaluation method. Journal of Groundwater Science and Engineering, 2016, 4(2): 103-109.|
|||NAN Tian, SHAO Jing-li, CUI Ya-li. Column test-based features analysis of clogging in artificial recharge of groundwater in Beijing. Journal of Groundwater Science and Engineering, 2016, 4(2): 88-95.|
|||ZHOU Zhi-chao, WANG Ju, SU Rui, GUO Yong-hai, LI Jie-biao, JI Rui-li, ZHANG Ming, DONG Jian-nan. Study on the residence time of deep groundwater for high-level radioactive waste geological disposal. Journal of Groundwater Science and Engineering, 2016, 4(1): 52-59.|
|||MA Luan, WANG Guang-cai, SHI Zhe-ming, GUO Yu-ying, XU Qing-yu, HUANG Xu-juan. Simulation of groundwater level recovery in abandoned mines, Fengfeng coalfield, China. Journal of Groundwater Science and Engineering, 2016, 4(4): 344-353.|
|||DONG Hua, GE Li-qiang. Groundwater ecological environment and the mapping of Asia. Journal of Groundwater Science and Engineering, 2015, 3(2): 118-126.|
|||YI Qing, GE Li-qiang, CHENG Yan-pei, DONG Hua, LIU Kun, ZHANG Jian-kang, YUE Chen. Compilation of Groundwater Quality Map and study of hydrogeochemical characteristics of groundwater in Asia. Journal of Groundwater Science and Engineering, 2015, 3(2): 176-185.|
|||CHENG Yan-pei, DONG Hua. Groundwater system division and compilation of Groundwater Resources Map of Asia. Journal of Groundwater Science and Engineering, 2015, 3(2): 127-135.|
|||GONG Xiao-ping, JIANG Guang-hui, CHEN Chang-jie, GUO Xiao-jiao, ZHANG Hua-sheng. Specific yield of phreatic variation zone in karst aquifer with the method of water level analysis. Journal of Groundwater Science and Engineering, 2015, 3(2): 192-201.|
|||Ramasamy Jayakumar. Groundwater level monitoring-importance global groundwater monitoring network. Journal of Groundwater Science and Engineering, 2015, 3(4): 295-305.|
|||HAN Kang-qin, LIU Jian, HAN Lei-lei, HAN Wen-ling, ZHANG Yun-xiao. Prediction of Impacts Caused by South-to-North Water Diversion on Underground Water Level in Shijiazhuang. Journal of Groundwater Science and Engineering, 2014, 2(2): 27-33.|
|||Zong-jun Gao, Yong-gui Liu. Groundwater Flow Driven by Heat. Journal of Groundwater Science and Engineering, 2013, 1(3): 22-27.|
|||Aizhong Ding, Lirong Cheng, Steve Thornton, Wei Huang, David Lerner. Groundwater quality Management in China. Journal of Groundwater Science and Engineering, 2013, 1(1): 54-59.|