Published May 30, 2023
| Version v1
Book chapter
Open
Groundwater level prediction using genetic algorithm based emotional and artificial neural networks
- 1. Erzincan Binali Yıldırım Üniversitesi
- 2. Siirt Üniversitesi
- 3. Abdelhafid Boussouf University Center
- 4. Hassiba Benbouali University of Chlef
Description
In this study, it has been evaluated whether the values of the neighboring well can be used to estimate the GWL values. For this, combinations of inputs for GWL estimation were determined by correlation analysis. In addition, the effect of various signal processing and optimization techniques on the ANN performance was analyzed in the prediction of GWL.
Files
GROUNDWATER LEVEL PREDICTION USING GENETIC ALGORITHMBASED EMOTIONAL AND ARTIFICIAL NEURAL NETWORKS.pdf
Files
(8.3 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:226b283ea715db6f1f3baecac66c7522
|
8.3 MB | Preview Download |
Additional details
References
- Adamowski J. and Chan, H.F. (2011). A Wavelet Neural Network Conjunction Model for Groundwater Level Forecasting. Journal of Hydrology, 407(1-4):28-40.
- 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, 12(1):5.
- Bhagat S.K. Tung T.M. Yaseen Z.M. (2020). Development of Artificial Intelligence for Modeling Wastewater Heavy Metal Removal: State of The Art, Application Assessment And Possible Future Research, J. Cleaner Prod. 250 119473
- Bisoyi N. Gupta H. Padhy N.P. Chakrapani G.J. (2019). Prediction of Daily Sediment Discharge Using A Back Propagation Neural Network Training Algorithm: A Case Study Of The Narmada River, India, Int. J. Sediment Res. 34(2): 125–135.
- Cai, Q., An, J.P., Li, H.Y. Guo, J Y. & Gao, Z.K. (2022). Cross-Subject Emotion Recognition Using Visibility Graph and Genetic Algorithm-Based Convolution Neural Network. Chaos: An Interdisciplinary Journal of Nonlinear Science, 32(9):093110.
- Dash, N.B. Panda S.N. Remesan R. Sahoo, N. (2010). Hybrid Neural Modeling for Groundwater Level Prediction. Neural Comput. Appl. 19:1251–1263.
- Gong, Y., Wang, Z., Xu, G., & Zhang, Z. (2018). A Comparative Study Of Groundwater Level Forecasting Using Data-Driven Models Based on Ensemble Empirical Mode Decomposition. Water, 10(6):730.
- Karahan, H. & Ayvaz, M.T. (2008). Simultaneous Parameter İdentification of A Heterogeneous Aquifer System Using Artificial Neural Networks. Hydrogeology Journal, 16:817-827.
- Katipoğlu, O.M. (2022a). Evaluation of the Performance of Data-Driven Approaches For Filling Monthly Precipitation Gaps in A Semi-Arid Climate Conditions. Acta Geophysica, 1-21.
- Katipoğlu, O.M. (2022b). Monthly Stream Flows Estimation in the Karasu River of Euphrates Basin with Artificial Neural Networks Approach. Mühendislik Bilimleri ve Tasarım Dergisi, 10(3):917-928.
- Lotfi E. and Akbarzadeh T.M.R. (2013, July) Emotional Brain-inspired Adaptive Fuzzy Decayed Learning for Online Prediction Problems. In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-7). IEEE.
- Lotfi, E, Khosravi, A. Akbarzadeh, T.M.R. and Nahavandi, S. (2014, October) Wind Power Forecasting Using Emotional Neural Networks. In 2014 IEEE International conference on systems, man, and cybernetics (SMC) (pp. 311-316). IEEE.
- Malik, A. and Bhagwat, A. (2021). Modelling Groundwater Level Fluctuations in Urban Areas Using Artificial Neural Network. Groundwater for Sustainable Development, 12, 100484.
- Mohanty, S. Jha, M.K. Kumar, A. and Sudheer, K.P. (2010). Artificial Neural Network Modeling For Groundwater Level Forecasting İn A River İsland Of Eastern India. Water resources management, 24, 1845-1865.
- Morén, J, and Balkenius, C. (2000). A Computational Model of Emotional Learning in the Amygdala. From animals to animats, 6, 115-124.
- Pandey, K. Kumar, S. Malik, A. and Kuriqi, A. (2020). Artificial Neural Network Optimized With A Genetic Algorithm For Seasonal Groundwater Table Depth Prediction İn Uttar Pradesh, India. Sustainability, 12(21):8932.
- Phelps, E.A. and LeDoux, J.E. (2005). Contributions of The Amygdala to Emotion Processing: From Animal Models to Human Behavior. Neuron, 48(2):175-187.
- Roshni, T. Jha M.K. and Drisya, J. (2020). Neural Network Modeling For Groundwater-Level Forecasting İn Coastal Aquifers. Neural Computing and Applications, 32, 12737-12754.
- Sahoo, S. and Jha, M.K. (2013). Groundwater-Level Prediction Using Multiple Linear Regression and Artificial Neural Network Techniques: A Comparative Assessment. Hydrogeology Journal, 21(8), 1865.
- Sarıgöl, M.Ağıralioğlu, N. & Bayata, H.F. (2016). Akarsu Taşımacılığının Dünyadaki Durumu ve Karasu Nehri'nde Taşımacılık Potansiyelinin İncelenmesi. Uluslararası Erzincan Sempozyumu, 137.
- Supreetha, B.S., Prabhakar Nayak, K. Narayan Shenoy, K. (2015). Groundwater Level Prediction Using Hybrid Artificial Neural Network with Genetic Algorithm. Int. J. Earth Sci. Eng. 8:2609–2615.
- Touchette, P.E. MacDonald, R.F. and Langer, S.N. (1985). A Scatter Plot for Identifying Stimulus Control of Problem Behavior. Journal of Applied Behavior Analysis,18(4):343-351.
- Tung, T.M. and Yaseen, Z.M. (2020). A Survey on River Water Quality Modelling Using Artificial Intelligence Models: 2000–2020. Journal of Hydrology, 585, 124670.
- Yaseen, Z.M. El-Shafie, A. Afan, H.A. Hameed, M. Mohtar, W.H.M.W. and Hussain, A. (2016). RBFNN Versus FFNN For Daily River Flow Forecasting At Johor River, Malaysia. Neural Computing and Applications, 27:1533-1542.
- Yongbo, L.I. Shubin S.I. Zhiliang, L.I.U. and Xihui, L. (2019). Review of Local Mean Decomposition and Its Application in Fault Diagnosis of Rotating Machinery. Journal of Systems Engineering and Electronics, 30(4):799-814.