scholarly journals Regional Mapping of Groundwater Potential in Ar Rub Al Khali, Arabian Peninsula Using the Classification and Regression Trees Model

2021 ◽  
Vol 13 (12) ◽  
pp. 2300
Author(s):  
Samy Elmahdy ◽  
Tarig Ali ◽  
Mohamed Mohamed
Keyword(s):  
Machine Learning ◽  
Regional Scale ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Groundwater Potential ◽  
Machine Learning Algorithms ◽  
Conditioning Factors ◽  
Potential Mapping ◽  
Classification And Regression ◽  
Groundwater Potential Mapping

Mapping of groundwater potential in remote arid and semi-arid regions underneath sand sheets over a very regional scale is a challenge and requires an accurate classifier. The Classification and Regression Trees (CART) model is a robust machine learning classifier used in groundwater potential mapping over a very regional scale. Ten essential groundwater conditioning factors (GWCFs) were constructed using remote sensing data. The spatial relationship between these conditioning factors and the observed groundwater wells locations was optimized and identified by using the chi-square method. A total of 185 groundwater well locations were randomly divided into 129 (70%) for training the model and 56 (30%) for validation. The model was applied for groundwater potential mapping by using optimal parameters values for additive trees were 186, the value for the learning rate was 0.1, and the maximum size of the tree was five. The validation result demonstrated that the area under the curve (AUC) of the CART was 0.920, which represents a predictive accuracy of 92%. The resulting map demonstrated that the depressions of Mondafan, Khujaymah and Wajid Mutaridah depression and the southern gulf salt basin (SGSB) near Saudi Arabia, Oman and the United Arab Emirates (UAE) borders reserve fresh fossil groundwater as indicated from the observed lakes and recovered paleolakes. The proposed model and the new maps are effective at enhancing the mapping of groundwater potential over a very regional scale obtained using machine learning algorithms, which are used rarely in the literature and can be applied to the Sahara and the Kalahari Desert.

scholarly journals Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1909 ◽  
Author(s):  
Kalantar ◽  
Al-Najjar ◽  
Pradhan ◽  
Saeidi ◽  
Halin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Factor Analysis ◽  
Discriminant Analysis ◽  
Groundwater Potential ◽  
Environmental Modeling ◽  
Operating Characteristics ◽  
Topographic Wetness Index ◽  
Conditioning Factors ◽  
Potential Mapping ◽  
Groundwater Potential Mapping

Assessment of the most appropriate groundwater conditioning factors (GCFs) is essential when performing analyses for groundwater potential mapping. For this reason, in this work, we look at three statistical factor analysis methods—Variance Inflation Factor (VIF), Chi-Square Factor Optimization, and Gini Importance—to measure the significance of GCFs. From a total of 15 frequently used GCFs, 11 most effective ones (i.e., altitude, slope angle, plan curvature, profile curvature, topographic wetness index, distance from river, distance from fault, river density, fault density, land use, and lithology) were finally selected. In addition, 917 spring locations were identified and used to train and test three machine learning algorithms, namely Mixture Discriminant Analysis (MDA), Linear Discriminant Analysis (LDA) and Random Forest (RF). The resultant trained models were then applied for groundwater potential prediction and mapping in the Haraz basin of Mazandaran province, Iran. MDA has been successfully applied for soil erosion and landslide mapping, but has not yet been fully explored for groundwater potential mapping (GPM). Although other discriminant methods, such as LDA, exist, MDA is worth exploring due to its capability to model multivariate nonlinear relationships between variables; it also undertakes a mixture of unobserved subclasses with regularization of non-linear decision boundaries, which could potentially provide more accurate classification. For the validation, areas under Receiver Operating Characteristics (ROC) curves (AUC) were calculated for the three algorithms. RF performed better with AUC value of 84.4%, while MDA and LDA yielded 75.2% and 74.9%, respectively. Although MDA performance is lower than RF, the result is satisfactory, because it is within the acceptable standard of environmental modeling. The outcome of factor analysis and groundwater maps emphasizes on optimization of multicolinearity factors for faster spatial modeling and provides valuable information for government agencies and private sectors to effectively manage groundwater in the region.

scholarly journals Preprocessing approaches in machine learning-based groundwater potential mapping: an application to the Koulikoro and Bamako regions, Mali

2021 ◽  
Author(s):  
Víctor Gómez-Escalonilla ◽  
Pedro Martínez-Santos ◽  
Miguel Martín-Loeches
Keyword(s):  
Machine Learning ◽  
Groundwater Potential ◽  
Gradient Boosting ◽  
Groundwater Prospect ◽  
Absolute Value ◽  
Explanatory Variables ◽  
Learning Classifiers ◽  
Potential Mapping ◽  
Learning Metrics ◽  
Groundwater Potential Mapping

Abstract. Groundwater is crucial for domestic supplies in the Sahel, where the strategic importance of aquifers can only be expected to increase in the coming years due to climate change. Groundwater potential mapping is gaining recognition as a valuable tool to underpin water management practices in the region, and hence, to improve water access. This paper presents a machine learning method to map groundwater potential and illustrates it through an application to two regions of Mali. A set of explanatory variables for the presence of groundwater is developed first. Several scaling methods (standardization, normalization, maximum absolute value and min-max scaling) are used to avoid the pitfalls associated with the reclassification of explanatory variables. A number of supervised learning classifiers is then trained and tested on a large borehole database (n = 3,345) in order to find meaningful correlations between the presence or absence of groundwater and the explanatory variables. This process identifies noisy, collinear and counterproductive variables and excludes them from the input dataset. Tree-based algorithms, including the AdaBoost, Gradient Boosting, Random Forest, Decision Tree and Extra Trees classifiers were found to outperform other algorithms on a consistent basis (accuracy > 0.85), whereas maximum absolute value and standardization proved the most efficient methods to scale explanatory variables. Borehole flow rate data is used to calibrate the results beyond standard machine learning metrics, thus adding robustness to the predictions. The southern part of the study area was identified as the better groundwater prospect, which is consistent with the geological and climatic setting. From a methodological standpoint, the outcomes lead to three major conclusions: (1) because there is no aprioristic way to know which algorithm will work better on a given dataset, we advocate the use of a large number of machine learning classifiers, out of which the best are subsequently picked for ensembling; (2) standard machine learning metrics may be of limited value when appraising map outcomes, and should be complemented with hydrogeological indicators whenever possible; and (3) the scaling of the variables helps to minimize bias arising from expert judgement and maintains robust predictive capabilities.

scholarly journals Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

2020 ◽  
Vol 17 (7) ◽  
pp. 2473 ◽  
Author(s):  
Phong Tung Nguyen ◽  
Duong Hai Ha ◽  
Abolfazl Jaafari ◽  
Huu Duy Nguyen ◽  
Tran Van Phong ◽  
...  
Keyword(s):  
Water Resource Management ◽  
Groundwater Potential ◽  
Ann Model ◽  
Yield Data ◽  
Ensemble Technique ◽  
Conditioning Factors ◽  
Model Studies ◽  
Potential Mapping ◽  
Artificial Neural ◽  
Groundwater Potential Mapping

The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

scholarly journals Machine learning approaches to classify melon landraces based on phenotypic traits

Genetika ◽  
2020 ◽  
Vol 52 (3) ◽  
pp. 1021-1029
Author(s):  
Rad Naroui ◽  
Gholamali Keykha ◽  
Jahangir Abbaskoohpayegani ◽  
Ramin Rafezi
Keyword(s):  
Machine Learning ◽  
Regression Trees ◽  
Classification And Regression Trees ◽  
Partial Least Square ◽  
Support Vector ◽  
Phenotypic Traits ◽  
Learning Approaches ◽  
Operating Characteristics ◽  
Kappa Value ◽  
Classification And Regression

Phenotyping of native cultivars is becoming more essential, as they are an important for breeders as a genetic source for breeding. The variability of morphological properties plays critical role in melon breeding. In this paper various machine learning approaches were implemented to identify melon accession classes. A field experiment was conducted in Zahak Agriculture station to differentiate 144 melon accessions based on 14 traits. For this, Partial Least Square Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN) and Classification And Regression Trees (CART) were compared. The most commonly used performance values comprise overall accuracy, kappa value, Receiver Operating Characteristics (ROC) and Area Under Curve (AUC) were performed to identify accuracy of the models. The results showed the best performance for CART than others. The AUC and kappa value were 0.85 and 0.80 and fruit weight was the most important trait that affecting diversity in melon accessions. Regarding to these results Classification And Regression Trees (CART) is reliable for identification of melon accessions classes.

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