Numerical modeling of groundwater to assess the impact of proposed railway construction on groundwater regime

Shenga, Zinaw Dingetu; Baroková, Dana; Šoltész, Andrej

doi:10.1556/606.2018.13.3.18

Cited by 3 publications

(3 citation statements)

References 7 publications

(8 reference statements)

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“…Overall, there is insufficient geodatabase related to groundwater, notably in fractured and karstic bedrock aquifers [8]. The knowledge gaps in term of geodatabase make the development of numerical models difficult and consequently understand the aquifer functioning in mountainous areas [9]. In order to resolve this issue, various statistical models and machine learning algorithms have been employed for groundwater potential modelling and mapping using inventories of springs for dependent variables (i.e., binary logistic regression (LR) [10], certainty factor (CF) [11], weights-of-evidence (WE) [12], artificial neural networks (ANNs), random forest (RF), support vector machines (SVMs), naïve Bayes (NB) and decision tree (DT) [13]).…”

Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Groundwater Potentiality in Large Semi-Arid and Karstic Mountainous Region Using Machine Learning Models

Namous

Hssaisoune

Pradhan

et al. 2021

Water

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The drinking and irrigation water scarcity is a major global issue, particularly in arid and semi-arid zones. In rural areas, groundwater could be used as an alternative and additional water supply source in order to reduce human suffering in terms of water scarcity. In this context, the purpose of the present study is to facilitate groundwater potentiality mapping via spatial-modelling techniques, individual and ensemble machine-learning models. Random forest (RF), logistic regression (LR), decision tree (DT) and artificial neural networks (ANNs) are the main algorithms used in this study. The preparation of groundwater potentiality maps was assembled into 11 ensembles of models. Overall, about 374 groundwater springs was identified and inventoried in the mountain area. The spring inventory data was randomly divided into training (75%) and testing (25%) datasets. Twenty-four groundwater influencing factors (GIFs) were selected based on a multicollinearity test and the information gain calculation. The results of the groundwater potentiality mapping were validated using statistical measures and the receiver operating characteristic curve (ROC) method. Finally, a ranking of the 15 models was achieved with the prioritization rank method using the compound factor (CF) method. The ensembles of models are the most stable and suitable for groundwater potentiality mapping in mountainous aquifers compared to individual models based on success and prediction rate. The most efficient model using the area under the curve validation method is the RF-LR-DT-ANN ensemble of models. Moreover, the results of the prioritization rank indicate that the best models are the RF-DT and RF-LR-DT ensembles of models.

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Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Groundwater Potentiality in Large Semi-Arid and Karstic Mountainous Region Using Machine Learning Models

Namous

Hssaisoune

Pradhan

et al. 2021

Water

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“…Because of these challenges, numerical models cannot be created in mountainous areas to understand how groundwater moves in bedrock (Shenga et al, 2018b). Various types of machine-learning and data-mining models have been employed for groundwater potential modelling, however, and they include binary logistic regression (Ozdemir, 2011b), weights of evidence (Ozdemir, 2011a;Pourtaghi and Pourghasemi, 2014;, frequency ratio (Oh et al, 2011;Manap et al, 2014), artificial neural networks (Lee et al, 2012(Lee et al, , 2018, random forest (Naghibi et al, 2016;Rahmati et al, 2016;Zabihi et al, 2016;Naghibi et al, 2017b;Golkarian et al, 2018), support vector machine (Naghibi et al, 2017b), boosted regression trees (Mousavi et al, 2017;Kordestani et al, 2019), generalized linear and additive models (Falah et al, 2017), classification and regression trees (Naghibi et al, 2016;, multivariate adaptive regression spline (Zabihi et al, 2016;Golkarian et al, 2018), evidential belief function (Nampak et al, 2014;Pourghasemi and Beheshtirad, 2015), maximum entropy (Rahmati et al, 2016), decision trees (Lee and Lee, 2015;Naghibi et al, 2019), and logistic model tree (Rahmati et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers

Moghaddam

Rahmati

Panahi

et al. 2020

CATENA

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Machine learning models have attracted much research attention for groundwater potential mapping. However, the accuracy of models for groundwater potential mapping is significantly influenced by sample size and this is still a challenge. This study evaluates the influence of sample size on the accuracy of different individual and hybrid models, adaptive neuro-fuzzy inference system (ANFIS), ANFISimperial competitive algorithm (ANFIS-ICA), alternating decision tree (ADT), and random forest (RF) to model groundwater potential, considering the number of springs from 177 to 714. A well-documented inventory of springs, as a natural representative of groundwater potential, was used to designate four sample data sets: 100% (D1), 75% (D2), 50% (D3), and 25% (D4) of the entire springs inventory. Each data set was randomly split into two groups of 30% (for training) and 70% (for validation). Fifteen diverse geo-environmental factors were employed as independent variables. The area under the operating 2 receiver characteristic curve (AUROC) and the true skill statistic (TSS) as two cutoff-independent and cutoff-dependent performance metrics were used to assess the performance of models. Results showed that the sample size influenced the performance of four machine learning algorithms, but RF had a lower sensitivity to the reduction of sample size. In addition, validation results revealed that RF (AUROC=90.74-96.32%, TSS=0.79-0.85) had the best performance based on all four sample data sets, followed by ANFIS-ICA (AUROC=81.23-91.55%, TSS=0.74-0.81), ADT (AUROC=79.29-88.46%, TSS=0.59-0.74), and ANFIS (AUROC=73.11-88.43%, TSS=0.59-0.74). Further, the relative slope position, lithology, and distance from faults were the main spring-affecting factors contributing to groundwater potential modelling. This study can provide useful guidelines and valuable reference for selecting machine learning models when a complete spring inventory in a watershed is unavailable.

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“…Numerical models are used to solve flow problems in open channels [10], groundwater flow [11] and also sediment transport [12]. The paper describes the evaluation of flow in the intake of a low pressure SHPP.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the impact of intake structure on the flow at low pressure SHPP

Bytčanková

Rumann

Dušička

2021

Pollack

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The structural parts of intake structures directly affect the flow velocity distribution in the turbine intake of small hydropower plants, where inhomogeneous flow leads to uneven load of the turbine units causing operational problems. A 2D numerical flow modeling was used for investigations of the flow in an intake structure of a low-head small hydropower plant. The effects of shape changes of the intake structure on the flow velocity distribution in the turbine intakes were investigated and assessed proving significant effect of the shapes of the intake structure on the flow homogeneity in turbine intakes.

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Numerical modeling of groundwater to assess the impact of proposed railway construction on groundwater regime

Cited by 3 publications

References 7 publications

Spatial Prediction of Groundwater Potentiality in Large Semi-Arid and Karstic Mountainous Region Using Machine Learning Models

Spatial Prediction of Groundwater Potentiality in Large Semi-Arid and Karstic Mountainous Region Using Machine Learning Models

The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers

Analyzing the impact of intake structure on the flow at low pressure SHPP

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