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http://dx.doi.org/10.3741/JKWRA.2022.55.11.903

Improvement of multi layer perceptron performance using combination of gradient descent and harmony search for prediction of ground water level  

Lee, Won Jin (Department of Civil Engineering, Chungbuk National University)
Lee, Eui Hoon (School of Civil Engineering, Chungbuk National University)
Publication Information
Journal of Korea Water Resources Association / v.55, no.11, 2022 , pp. 903-911 More about this Journal
Abstract
Groundwater, one of the resources for supplying water, fluctuates in water level due to various natural factors. Recently, research has been conducted to predict fluctuations in groundwater levels using Artificial Neural Network (ANN). Previously, among operators in ANN, Gradient Descent (GD)-based Optimizers were used as Optimizer that affect learning. GD-based Optimizers have disadvantages of initial correlation dependence and absence of solution comparison and storage structure. This study developed Gradient Descent combined with Harmony Search (GDHS), a new Optimizer that combined GD and Harmony Search (HS) to improve the shortcomings of GD-based Optimizers. To evaluate the performance of GDHS, groundwater level at Icheon Yullhyeon observation station were learned and predicted using Multi Layer Perceptron (MLP). Mean Squared Error (MSE) and Mean Absolute Error (MAE) were used to compare the performance of MLP using GD and GDHS. Comparing the learning results, GDHS had lower maximum, minimum, average and Standard Deviation (SD) of MSE than GD. Comparing the prediction results, GDHS was evaluated to have a lower error in all of the evaluation index than GD.
Keywords
Prediction of ground water level; Multi layer perceptron; Optimizer; Gradient descent; Gradient descent combined with harmony search;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 Batelaan, O., De Smedt, F., and Triest, L. (2003). "Regional groundwater discharge: phreatophyte mapping, groundwater modelling and impact analysis of land-use change." Journal of Hydrology, Vol. 275, No. 1-2, pp. 86-108.   DOI
2 Daliakopoulos, I.N., Coulibaly, P., and Tsanis, I.K. (2005). "Ground water level forecasting using artificial neural networks." Journal of Hydrology, Vol. 309, No. 1-4, pp. 229-240.   DOI
3 Derbela, M., and Nouiri, I. (2020). "Intelligent approach to predict future groundwater level based on artificial neural networks (ANN)." Euro-Mediterranean Journal for Environmental Integration, Vol. 5, No. 3, pp. 1-11.   DOI
4 Ebrahimi, H., and 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.   DOI
5 Geem, Z.W., Kim, J.H., and Loganathan, G.V. (2001). "A new heuristic optimization algorithm: harmony search." Simulation, Vol. 76, No. 2, pp. 60-68.   DOI
6 Joo, G., Park, C., and Im, H. (2020). "Performance evaluation of machine learning optimizers." Journal of Korean Electrical and Electronics Engineers, Vol. 24, No. 3, pp. 766-776.
7 Kennedy, J., and Eberhart, R. (1995). "Particle swarm optimization." Proceedings of the IEEE International Conference on Neural Networks, Indianapolis, IN, Vol. 4, pp. 1942-1948.
8 Kim, I., and Lee, J. (2018). "Prediction model for spatial and temporal variation of groundwater level based on river stage." Journal of Hydrologic Engineering, Vol. 23, No. 6, 06018002.   DOI
9 Goldberg, D.E., and Holland, J.H. (1988). "Genetic algorithms and machine learning." Machine Learning, Vol. 3, pp. 95-99.   DOI
10 Kim, Y.N., and Lee, E.H. (2020). "Development of the meta-heuristic optimization algorithm: Exponential bandwidth harmony search with centralized global search." Journal of the Korea AcademiaIndustrial cooperation Society, Vol. 21, No. 2, pp. 8-18.
11 Knotters, M., and Bierkens, M.F. (2000). "Physical basis of time series models for water table depths." Water Resources Research, Vol. 36, No. 1, pp. 181-188.   DOI
12 Aqil, M., Kita, I., Yano, A., and Nishiyama, S. (2007). "A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff." Journal of Hydrology, Vol. 337, No. 1-2, pp. 22-34.   DOI
13 Barthel, R., and Banzhaf, S. (2016). "Groundwater and surface water interaction at the regional-scale - a review with focus on regional integrated models." Water Resources Management, Vol. 30, No. 1, pp. 1-32.   DOI
14 Suryanarayana, C., Sudheer, C., Mahammood, V., and Panigrahi, B.K. (2014). "An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India." Neurocomputing, Vol. 145, pp. 324-335.   DOI
15 Mahdavi, M., Fesanghary, M., and Damangir, E. (2007). "An improved harmony search algorithm for solving optimization problems." Applied mathematics and computation, Vol. 188, No. 2, pp. 1567-1579.   DOI
16 Maxwell, R.M., Condon, L.E., and Kollet, S.J. (2015). "A highresolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3." Geoscientific Model Devel opment, Vol. 8, No. 3, pp. 923-937.   DOI
17 Park, C., and Chung, I.M. (2020). "Evaluating the groundwater prediction using LSTM model." Journal of Korea Water Resources Association, Vol. 53, No. 4, pp. 273-283.   DOI
18 Ryu, Y.M., and Lee, E.H. (2022). "Application of neural networks to predict Daecheong Dam water levels." Journal of the Korean Society of Hazard Mitigation, Vol. 22, No. 1, pp. 67-78.   DOI
19 Sattari, M.T., Mirabbasi, R., Sushab, R.S., and Abraham, J. (2018). "Prediction of groundwater level in Ardebil plain using support vector regression and M5 tree model." Groundwater, Vol. 56, No. 4, pp. 636-646.   DOI
20 White, J.T., Knowling, M.J., and Moore, C.R. (2020). "Consequences of groundwater-model vertical discretization in risk-based decision-Making." Groundwater, Vol. 58, No. 5, pp. 695-709.
21 Yoo, Y., Kim, D., and Lee, J. (2020). "Performance analysis of various activation functions in super resolution model." Proceedings of the Korea Information Processing Society Conference, KIPS, Vol. 27, No. 1, pp. 504-507.
22 Rosenblatt, F. (1958). "The perceptron: a probabilistic model for information storage and organization in the brain." Psychological Review, Vol. 65, No. 6, 386.   DOI
23 Kim, I., Lee, J., Kim, J., Lee, H., and Lee, J. (2021). "Analysis of groundwater level prediction performance with influencing factors by artificial neural network." Journal of the Korean Geotechnical Society, Vol. 37, No. 5, pp. 19-31.   DOI
24 Liu, Q., Jian, W., and Nie, W. (2021). "Rainstorm-induced landslides early warning system in mountainous cities based on groundwater level change fast prediction." Sustainable Cities and Society, Vol. 69, 102817.   DOI
25 Affandia, AK., Watanabe, K., and Tirtomihardjo, H. (2007). "Application of an artificial neural network to estimate groundwater level fluctuation." Journal of Spatial Hydrology, Vol. 7, No. 2, pp. 17-32.
26 McCulloch, W.S., and Pitts, W. (1943). "A logical calculus of the ideas immanent in nervous activity." The Bulletin of Mathematical Biophysics, Vol. 5, No. 4, pp. 115-133.   DOI
27 Ministry of Land, Infrastructure and Transport (MOLIT) (2011). Bok-stream basic plan (change) report, p. 34.
28 Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1986). "Learning representations by back-propagating errors." Nature, Vol. 323, No. 6088, pp. 533-536.   DOI
29 Sahoo, S., and Jha, M.K. (2013). "Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment." Hydrogeology Journal, Vol. 21, No. 8, pp. 1865-1887.   DOI
30 Sedki, A., Ouazar, D., and El Mazoudi, E. (2009). "Evolving neural network using real coded genetic algorithm for daily rainfall - runoff forecasting." Expert Systems with Applications, Vol. 36, No. 3, pp. 4523-4527.   DOI
31 Shin, M.J., Moon, S.H., Kang, K.G., Moon, D.C., and Koh, H.J. (2020). "Analysis of groundwater level variations caused by the changes in groundwater withdrawals using long short-term memory network." Hydrology, Vol. 7, No. 3, 64.   DOI
32 Trichakis, I.C., Nikolos, I.K., and Karatzas, G.P. (2011). "Artificial neural network (ANN) based modeling for karstic groundwater level simulation." Water Resources Management, Vol. 25, No. 4, pp. 1143-1152.   DOI
33 Yousefi, H., Zahedi, S., Niksokhan, M.H., and Momeni, M. (2019). "Ten-year prediction of groundwater level in Karaj plain (Iran) using MODFLOW2005-NWT in MATLAB." Environmental Earth Sciences, Vol. 78, No. 12, pp. 1-14.   DOI
34 Sahoo, S., Russo, T.A., Elliott, J., and Foster, I. (2017). "Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US." Water Resources Research, Vol. 53, No. 5, pp. 3878-3895.   DOI