Forecasting the upwelling phenomenon using an artificial neural network

Chafai Bouzegag et al.

Abstract


In this paper, we investigate the upwelling phenomenon using data of 97 monitoring stations in Ouargla and El Oued valleys located in the Low Septentrional Sahara south of Algeria. This research paper constitutes a contribution to the morphological, hydrological, hydrogeological study of the water table in order to understand the processes of upwelling groundwater. By using ArcGIS as a mapping tool, we worked on real UTM coordinates in X and Y for real data overlay drawn maps in clear and usable way of this phenomenon. On the other hand, we propose a new method based on neural network to model the level flctuation of the groundwater as well as to predict the evolution of the water table level. The obtained model allows us to warm this harmful phenomenon and plan sustainable solutions to protect the environment. The finding shows that the obtained model provides more significant accuracy rate and it drives more robustness in very challenging situation such as the heterogeneity of the data and sudden climate change comparing to the related research.


Keywords


upwelling phenomenon, monitoring stations, modeling, ArcGIS software, neural network

Full Text:

PDF

References


ANRH (National Agency of Water Resources in Algeria), 2000. Fourth hydrogeological measure compagne « Notes relatives à la remontée des eaux dans la cuvette d’Ouargla ». 11 pp.

Apaydin, A., 2009. Response of groundwater to climate variation: Fluctuations of groundwater level and well yields in the Halacli aquifer (Cankiri, Turkey). Environmental Monitoring and Assessment, 165: 653–663.

ASCE, 2000. Artifiial neural networks in hydrology – I: Preliminary concepts. Journal of Hydrologic Engineering ASCE, 5(2): 115–123.

Benguergoura, L.S., Remini, B, 2016. Contamination of groundwater by sewage and degradation of cultures under water stress. Third International Conference on Computational and Experimental Science and Engineering (ICCESEN-2016), 1(2): 1–6.

Bouzegag, C., Bouzid-Lagha, S., 2015. Cartographic and statistical analysis of the phenomenon of rising water table in southeast Algeria. GCGW, Athens.

Capot-Rey, R., 1952. Les limites du Sahara Français. Inst. Rech. Sah., Alger, VIII: 23–47.

Castany, G., 1982. Principes et méthodes de l’hydrogéologie. Dunod.

Chaudhari S., 2008. Upwelling detection in AVHRR Sea surface temperature (SST) images using neural network framework. IGARSS IV p. 926.

Chehma, A., 2011. Le sahara en Algérie, situation et défi. CMEP TASSILI (N° 09 MDU 754) Laboratoire de Bio ressources sahariennes. Préservation et valorisation. Université Kasdi Merbah-Ouargla.

Conte, S., Dunsmore, H., Shen, V., 1986. Software Engineering Metrics and Models. Benjamin/Cummings, Menlo Park.

Djelal, N., Saadia, N., 2013. Robot Perception Based on Different Computational Intelligence Techniques. Springer.

Elish, M.O., 2012. A comparative study of fault density prediction in aspect-oriented systems using MLP, RBF, KNN, RT, DENFIS and SVR models. Artifiial Intellgence Review, 42: 695–703. DOI 10.1007/s10462-012-9348-9.

ENAGEO, 1990. Etude hydrogéologique de la nappe phréatique de la cuvette de Ouargla. Rapport, avril.

Estienne, P. et Godard, A., 1970. Cahiers de géographie du Québec. Climatologie. Paris, Armand Colin, Collection U.

Haykin, S., 2005. Neural Networks and Learning Machines, 2nd ed., Pearson, Prentice Hall.

Kriebel S., Brauer, W., Eiflr, W., 1998. Coastal upwelling prediction with a mixture of neural networks. IEEE Transactions on Geoscience and Remote Sensing, 36(4): 1508–1518.

Mohanty, S., Jha, M.K., Kumar, A., Sudheer, K.P., 2010. Artifiial neural network modeling for groundwater level forecasting in a river island of eastern India. Water Resource Management, 24: 1845–1865.

Mousseau, S., Debeauvais, T., 2012. Ville de Sevran plan climat énergie territorial étude de la vulnérabilité du territoire au changement climatique. Document fial.

Nayak, P.C., Rao, Y.R.S., Sudheer, K.P., 2006. Groundwater level forecasting in a shallow aquifer using artifiial neural network approach. Water Resource Management, 20: 77–90.

Nesson, C., 1978. L’évolution des ressources hydrauliques dans les oasis du Bas Sahara algérien. Recherche sur l'Algérie (ed. CNRS), pp. 7–100, Paris.

Remini, B., Kechad, R., 2011. Impact of the water table razing on the degradation of el Oued palm plantation (Algeria). Mechanisms and solutions. Geographia Technica, 1: 48–56.

Renguang, Z., 2017. Machine learning of mineralization-related geochemical anomalies: A review of potential methods. Natural Resources Research, 26: 457–464.

Zouini, D., 2000. Le déséquilibre d’une nappe aquifère et ses conséquences à Souf (Sahara septentrional algérien).




DOI: http://dx.doi.org/10.17951/pjss.2020.53.2.245-259
Date of publication: 2020-12-26 01:25:46
Date of submission: 2019-12-11 00:27:29


Statistics


Total abstract view - 1071
Downloads (from 2020-06-17) - PDF - 761

Indicators



Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 CHAFAI BOUZEGAG

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.