Optimum use of underground water table using artificial intelligence FNN-LM model (case study: Khuzestan plain)

Hashemi Ghand Ali, Farkhonde

Optimum use of underground water table using artificial intelligence FNN-LM model (case study: Khuzestan plain)

Document Type : Origional Article

Author

Farkhonde Hashemi Ghand Ali

MAs in geography and urban planning, Shahid Chamran University of Ahvaz , Ahvaz,Iran

Abstract

Abstract
Introduction and statement of the problem: The use of neural intelligence in predicting the variables of water resources, including underground water, is widely increasing. Purpose: This research through artificial intelligence and FNN-LM model pursues several goals, which include determining the parameters affecting the fluctuations of the underground water level in the Khuzestan plain, as well as investigating the spatial and temporal effects of the water level parameters through 10-year time data and Then, the modeling of groundwater level fluctuations in selected piezometers in the plain is studied. Method: The use of artificial intelligence and the FNN-LM model method, and at the end, by changing the percentage of the last month of the input data in the model, hypothetical conditions were created and according to the obtained neural network models, the fluctuations of the underground water level were predicted. In this hypothetical situation, it was discussed. Findings: The effect of the discharge parameter from the wells is far more than the effect of the rainfall parameter, so that the prediction of drought and drought conditions is only due to the change of the rainfall. Conclusion: By using the created neural network models for each observation well and using the most accepted method of geostatistical models, an appropriate spatial and temporal prediction of the groundwater level was made. The best modeling of water level fluctuations with the FNN-LM model was achieved by choosing appropriate parameters and with the most acceptable time delay.
Keywords: artificial intelligence, underground water level, FNN-LM model, Khuzestan plain.

Keywords

artificial intelligence

underground water level

FNN-LM model

Khuzestan plain

Subjects

Regional & urban planning

اصغری مقدم، الف، ندیری، ع، فیجانی، الف. (1385). مطالعه توانایی مدلهای مختلف شبکه عصبی مصنوعی برای ارزیابی سطح آب زیرزمینی در سازند سخت، دهمین همایش انجمن زمین شناسی ایران، دانشگاه تربیت مدرس، ایران.

درویش زاده، ع. (1375). زمین شناسی ایران .انتشارات امیرکبیر، چاپ دوم، تهران.

گیلانی مکنونی، س. (1383). تعیین هدایت هیدرولیکی خاک های غیراشباع زاهدان به وسیله مدل شبکه عصبی. پایان نامه کارشناسی ارشد، دانشگاه زاهدان.

محمدی، ک. (1381). برآورد سطح آب زیرزمینی با استفاده از مدل MODFLOW و شبکه عصبی مصنوعی، دانشگاه تربیت مدرس، دانشکده کشاورزی.

منهاج، م. (1377). مبانی شبکه های عصبی، دانشگاه صنعتی امیرکبیر، صفحه 21.

Anderson, D., McNeill, G. (1992). Artificial Neural Networks Technology. Kaman Sciences Corp., New York, 13502-4627.

Copola, E., Szidarovszky, F., Poulton, M., Charles, E., 2003, Artificial neural network approach for predicting transient water levels in multilayered groundwater system under variable state, pumping, and climate conditions. J. Hydro. Eng., 8(6), 348-380.

Coulibaly, P., Anctil, F., Aravena, R., Bobée, B. (2001). Artificial neural network modeling of water table depth fluctuation. Water Resour. Rese., 37, 8

Coulibaly, P., Anctil, F., Bobée, B. (2000). Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J. Hydrol. 230, 244-257.

Coulibaly, P., Anctil, F., Bobée, B. (2001). Multivariate reservoir inflow forecasting using temporal neural networks. J. Hydrol. Eng. 9-10, 367-376.

Coulibaly, P., F. Anctil, B., Bobe´e. (1999). Hydrological forecasting using artificial neural networks: The state of the art, Can. J. Civ. Eng., 26 (3), 293–304.

Daliakopoulos, N. I., Coulibaly, P., Tsanis, I. K. (2005). Ground water level forecasting using artificial neural networks. J. Hydro., 309, 229-240.

Garcia, L. A., Shigidi, A. (2006). Using neural networks for parameter estimation in ground water. J. Hydro., 318, 215-231.

Grossberg, M. Hassoun MH. (1980). Fundamentals of artificial neural networks and Adeline network in use. Cambridge: MIT Press.

Haykin, S. (1994). Neural network: A comprehensive foundation, MacMillan College publishing Co., New York.

Hopfield, J. J. (1982). Neural network and physical systems with emergent collective computational abilities. Proc., Nat. Academy of scientists, 79, 2554-2558.

Karunanithi, N., Grenney, W. J., Whitley, D., and Bovee, K. (1994). Neural networks for river flow prediction. J. Comp. in Civ. Engrg., ASCE, 8 (2), 201– 220.

Lallahem S., Mania, J., Hani, A., Najjar, Y. (2005). On the use of neural networks to evaluate ground water levels in fractured media. J. Hydro., 307, 92-111.