Proccedings of 10th International Conference "Environmental Engineering" 2017
DOI: 10.3846/enviro.2017.092
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Estimation of Groundwater Level Using Artificial Neural Networks: a Case Study of Hatay-Turkey

Abstract: Abstract. Groundwater, which is a strategic resource in Turkey, is used for drinking-use, agricultural irrigation and industrial purposes. Population increase and total water consumption are constantly increasing. In order to meet the need for water, over-shoots from underground water have caused significant falls in groundwater level. Estimation of water level is important for planning an efficient and sustainable groundwater management. In this study, groundwater level, monthly mean precipitation and tempera… Show more

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Cited by 10 publications
(7 citation statements)
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“…6). L t represents the lake level at time interval t. Input combinations show the last recorded daily delayed lake levels (L t- 5…”
Section: Resultsmentioning
confidence: 99%
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“…6). L t represents the lake level at time interval t. Input combinations show the last recorded daily delayed lake levels (L t- 5…”
Section: Resultsmentioning
confidence: 99%
“…In this paper, models ARMA (1, 1), ARMA (3, 3) and ARMA (5,5) have been applied to Miller Dam reservoir level data using MATLAB programming.…”
Section: Y T-2 ………+ φ T-p Y T-p +…+ + a T + θ 1 A T-1 + θ 2 A T-2 ……mentioning
confidence: 99%
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“…It is possible to introduce fuzzy systems that logical models which is consisted of "If-Then" rules and membership functions. For more information, researchers can access Jang [30].…”
Section: Nf Methodsmentioning
confidence: 99%
“…Water level estimation is essential for efficient groundwater management and achieving sustainable development goals. In [19] the authors estimated the water level using temperature and monthly mean precipitation using Multiple Linear Regression (MLR) and ANN. Similar work have been done in [20] to predict the groundwater level in the Reyhanli region of Turkey using VOLUME 11, 2023 Artificial Neural Networks (ANN) and M5Tree models.…”
Section: A Machine Learning For Water Level Predictionmentioning
confidence: 99%