Spatial prediction of groundwater potentiality using machine learning methods with Grey Wolf and Sparrow Search Algorithms

Liu, Rui; Li, Gulin; Wei, Liangshuai; Xu, Yuan; Gou, Xiaojuan; Luo, Shubin; Yang, Xin

doi:10.1016/j.jhydrol.2022.127977

Cited by 32 publications

(10 citation statements)

References 67 publications

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“…The water withdrawal inventory is an indispensable component of water resources management. Therefore, it presents a baseline step in the GWP mapping [14]. The study area is known for several types of groundwater withdrawal systems, and its inventorying was prepared after extensive field investigation and data collection.…”

Section: Database Generation 231 Water Withdrawal Points Inventoryingmentioning

confidence: 99%

“…Several scholars recommend integrating sampling data from areas without groundwater withdrawal in order to balance the input dataset ( [8,13,20] and references therein; therefore, the list of the water withdrawal systems (set to 1, i.e., a high GWP) inventoried was completed using the same number of locations with no water withdrawal systems (set to 0, i.e., a very low GWP), and it was randomly mapped using the function create random points in a GIS environment, resulting in a total of 884 points (Figure 1). At this step, a variation in the percentage of the data split (training/testing) has been noted among the most recent studies, in which the division of 70/30% was the most used partition [14,23,25] in addition to the partition 75/25% and 80/20%, which have been used by Namous et al [20] and Talukdar et al [9], respectively. Accordingly, the 884 points were randomly divided into training (70%) and validation (30%) data, which were used to develop and validate the groundwater potential prediction models.…”

Section: Database Generation 231 Water Withdrawal Points Inventoryingmentioning

confidence: 99%

“…Previous studies have shown that common indicators such as hydrological, geological, topographical, and climatic indicators were integrated according to their influence on the GWP using several mathematical and statistical approaches. Among the latter, the frequency ratio, weights-of-evidence, and logistic regression are the approaches usually used by scholars [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, numerous novel methods and algorithms related to artificial intelligence (AI) based on machine learning (ML) and deep learning (DL) have been developed, assessed, and approved in the field of GWP mapping determination; this has been conducted with respect to inventories of water withdrawal points and geological, hydrogeological, hydrological, topographic and climatic factors [11,17,[19][20][21]. On this matter, the following models were commonly used and applied in the sub-cited studies: random forest (RF), support vector machine (SVM), linear regression (LR), decision tree (DT), naive Bayes (NB), convolutional neural network (CNN), long short-term memory (LSTM) and artificial neural network (ANN) [6,9,14,[22][23][24][25]. Furthermore, a variety of methods have also been proposed to improve the efficiency and precision of the prediction models, such as optimization algorithms and ensemble models [23,26,27].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco

et al. 2023

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Water availability is a key factor in territorial sustainable development. Moreover, groundwater constitutes the survival element of human life and ecosystems in arid oasis areas. Therefore, groundwater potential (GWP) identification represents a crucial step for its management and sustainable development. This study aimed to map the GWP using ten algorithms, i.e., shallow models comprising: multilayer perceptron, k-nearest neighbor, decision tree, and support vector machine algorithms; hybrid models comprising: voting, random forest, adaptive boosting, gradient boosting (GraB), and extreme gradient boosting; and the deep learning neural network. The GWP inventory map was prepared using 884 binary data, with “1” indicating a high GWP and “0” indicating an extremely low GWP. Twenty-three GWP-influencing factors have been classified into numerical data using the frequency ration method. Afterwards, they were selected based on their importance and multi-collinearity tests. The predicted GWP maps show that, on average, only 11% of the total area was predicted as a very high GWP zone and 17% and 51% were estimated as low and very low GWP zones, respectively. The performance analyses demonstrate that the applied algorithms have satisfied the validation standards for both training and validation tests with an average area under curve of 0.89 for the receiver operating characteristic. Furthermore, the models’ prioritization has selected the GraB model as the outperforming algorithm for GWP mapping. This study provides decision support tools for sustainable development in an oasis area.

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Section: Database Generation 231 Water Withdrawal Points Inventoryingmentioning

confidence: 99%

Section: Database Generation 231 Water Withdrawal Points Inventoryingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco

et al. 2023

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“…R ∈ [0, 1] and ST ∈ [0.5, 1.0] signify the alarm value and safety thresholding value correspondingly. R < ST i.e., the producer implements the general searching mode without the influence of the predator; In R ≥ ST, the predator is found by the sparrow and each sparrow must fly towards the safest region [21].…”

Section: Parameter Tuning Using Sso Algorithmmentioning

confidence: 99%

Sparrow Search Optimization with Transfer Learning-Based Crowd Density Classification

Yamin¹,

Almutairi²,

Badghish³

et al. 2023

Computers, Materials &Amp; Continua

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Due to the rapid increase in urbanization and population, crowd gatherings are frequently observed in the form of concerts, political, and religious meetings. HAJJ is one of the well-known crowding events that takes place every year in Makkah, Saudi Arabia. Crowd density estimation and crowd monitoring are significant research areas in Artificial Intelligence (AI) applications. The current research study develops a new Sparrow Search Optimization with Deep Transfer Learning based Crowd Density Detection and Classification (SSODTL-CD2C) model. The presented SSODTL-CD2C technique majorly focuses on the identification and classification of crowd densities. To attain this, SSODTL-CD2C technique exploits Oppositional Salp Swarm Optimization Algorithm (OSSA) with EfficientNet model to derive the feature vectors. At the same time, Stacked Sparse Auto Encoder (SSAE) model is utilized for the classification of crowd densities. Finally, SSO algorithm is employed for optimal fine-tuning of the parameters involved in SSAE mechanism. The performance of the proposed SSODTL-CD2C technique was validated using a dataset with four different kinds of crowd densities. The obtained results demonstrated that the proposed SSODTL-CD2C methodology accomplished an excellent crowd classification performance with a maximum accuracy of 93.25%. So, the proposed method will be highly helpful in managing HAJJ and other crowded events.

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Evaluation of Groundwater Potential Zones Using GIS‐Based Machine Learning Ensemble Models in the Gidabo Watershed, Ethiopia

Mussa,

Lohani,

Eshete

2024

Global Challenges

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The main objective of this study is to map and evaluate groundwater potential zones (GWPZs) using advanced ensemble machine learning (ML) models, notably Random Forest (RF) and Support Vector Machine (SVM). GWPZs are identified by considering essential factors such as geology, drainage density, slope, land use/land cover (LULC), rainfall, soil, and lineament density. This is combined with datasets used for training and validating the RF and SVM models, which consisted of 75 potential sites (boreholes and springs), 22 non‐potential sites (bare lands and settlement areas), and 20 potential sites (water bodies). Each dataset is randomly partitioned into two sets: training (70%) and validation (30%). The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC‐ROC). The AUC of the RF model is 0.91, compared to 0.88 for the SVM model. Both models classified GWPZs effectively, but the RF model performed slightly better. The classified GWPZ map shows that high GWPZs are typically located within water bodies, natural springs, low‐lying regions, and forested areas. In contrast, low GWPZs are primarily found in shrubland and grassland areas. This study is vital for decision‐makers as it promotes sustainable groundwater use and ensures water security in the studied area.

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Spatial prediction of groundwater potentiality using machine learning methods with Grey Wolf and Sparrow Search Algorithms

Cited by 32 publications

References 67 publications

Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco

Spatial Prediction of Groundwater Withdrawal Potential Using Shallow, Hybrid, and Deep Learning Algorithms in the Toudgha Oasis, Southeast Morocco

Sparrow Search Optimization with Transfer Learning-Based Crowd Density Classification

Evaluation of Groundwater Potential Zones Using GIS‐Based Machine Learning Ensemble Models in the Gidabo Watershed, Ethiopia

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