Estimation of Groundwater Level Using Artificial Neural Networks: a Case Study of Hatay-Turkey

Demirci, Mustafa; Üneş, Fatih; Kaya, Yunus Ziya; Mamak, Mustafa; Taşar, Bestami; Ispir, Eyup

doi:10.3846/enviro.2017.092

Cited by 10 publications

(7 citation statements)

References 10 publications

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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%

“…The artificial intelligence approach is a technique widely used in several engineering problems such as estimating suspended sediment, evaporation, groundwater level and nearshore bar volumes [1][2][3][4][5]. Zaji and Bonakdari [6] applied genetic algorithm artificial neural network and genetic programming methods to estimate lake level.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Estimating Dam Reservoir Level Fluctuations Using Data-Driven Techniques

Üneş¹,

Demirci²,

Taşar³

et al. 2019

Pol. J. Environ. Stud.

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Water-resource investigations should embrace and deal with effective reservoir water storage, which can be employed for water supply, irrigation, providing flood and drought control, hydropower and energy security of local generation. Dam reservoirs are used to provide storage for water and are important elements in planning and managing water resources. If the dam reservoir level is estimated correctly, the most suitable dam operation performance can be obtained. Reservoir level parameters are inflow to reservoir, water storage in reservoir, water release from reservoir, evaporation, soil moisture and infiltration. They represent uncertainties and must be considered in water resource operation. Reservoir water level is also important in the analysis and design of several water resource projects such as dam construction, irrigation needs and flood control. During a flood, the opening of the dam weir passage should be sufficient to ensure that reservoir

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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%

See 1 more Smart Citation

Estimating Dam Reservoir Level Fluctuations Using Data-Driven Techniques

Üneş¹,

Demirci²,

Taşar³

et al. 2019

Pol. J. Environ. Stud.

Self Cite

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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%

Prediction of the Rainfall – Runoff Relationship Using Neuro-Fuzzy and Support Vector Machines

Taşar¹,

Üneş²,

Varçin³

2019

Air and Water Components of the Environment Conference

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Rainfall-Runoff relationship analyzes are essential for the protection of flood rooting, management of water resources and design of water structures. In this study, Neuro-Fuzzy (NF) and Support Vector Machines (SVM) methods are applied for Rainfall-Runoff prediction. Daily hydrological and seasonal data taken from Muskegon basin in USA were used for present study. 1397 daily data of rainfall, temperature and runoff from the study area were analyzed by NF and SVM methods. The results show that the SVM method lead to low errors and high determinations in the Rainfall-Runoff modeling. Models results are compared with daily observed data. SVM method can be used as an alternative to classical methods in Rainfall-Runoff prediction.

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“…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%

Predictive Modeling of Water Table Depth, Drilling Duration, and Soil Layer Classification Using Adaptive Ensemble Learning for Cost-Effective Percussion Water Borehole Drilling

et al. 2023

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Water drilling machines are used to drill boreholes in the ground to extract groundwater. The resources required for water drilling vary from region to region due to underground water table depth and ground soil layer. Water drilling on a hard underground soil layer requires different resources than a soft underground. The proposed study facilitates the drilling industry by selecting the region with a soft land layer and increasing the penetration rate. Furthermore, the number of days and water table depth prediction allows the drilling industry to estimate the depth of the water table and time resources to reach the water table at different locations. The classification techniques classify the region based on the soil land layer. Regression techniques are used for predicting water table depth and number of days. The experiments are performed on a borehole log dataset provided by a research organization. This study used Support Vector Machine, TabNet, and Deep Tabular models to predict the land soil layer and compare the results with our proposed Ensemble Weighted Voting Soil Layer Classifier (EWV-SLC). The performance of the classification model is evaluated using accuracy, Precision, Recall, and F1 Score. The experimental finding shows that the EWV-SLC model performs better in accuracy and F1 score than other machine learning techniques. The performance of the regression model is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE),Root Mean Square Error (RMSE), and Mean Absolute Percentage Error(MAPE). In a days and water table depth prediction phase Support, Vector Regressor, Deep Neural Network, and TabNet Regressor are used, and compare the results with our proposed Ensemble Number of Days (E-NOD) and Ensemble Water Table depth (E-WTD) Regressor model. E-NOD and E-WTD models achieved less MAE, RMSE, and MSE than other machine learning methods. INDEX TERMS Applied machine learning, ensemble learning, voting classifier, deep tabular model. The associate editor coordinating the review of this manuscript and approving it for publication was Ikramullah Lali.

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Estimation of Groundwater Level Using Artificial Neural Networks: a Case Study of Hatay-Turkey

Cited by 10 publications

References 10 publications

Estimating Dam Reservoir Level Fluctuations Using Data-Driven Techniques

Estimating Dam Reservoir Level Fluctuations Using Data-Driven Techniques

Prediction of the Rainfall – Runoff Relationship Using Neuro-Fuzzy and Support Vector Machines

Predictive Modeling of Water Table Depth, Drilling Duration, and Soil Layer Classification Using Adaptive Ensemble Learning for Cost-Effective Percussion Water Borehole Drilling

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