Identification of groundwater potential zones using remote sensing, GIS, machine learning and electrical resistivity tomography techniques in Guelma basin, northeastern Algeria

Braham, Massinissa; Boufekane, Abdelmadjid; Bourenane, Hamid; Amara, Baya Nait; Bensalem, Rabah; Oubaiche, El Hadi; Bouhadad, Youcef

doi:10.1080/10106049.2022.2063408

Cited by 8 publications

(4 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mine DC method cannot accurately delineate anomalies, and its inversion methods require further research. Further research is also needed on well-ground and interhole resistivity imaging techniques, as well as 3D resistivity imaging and other emerging technologies [47,48].…”

Section: Direct Current Methodsmentioning

confidence: 99%

Methods for the Geophysical Exploration and Sustainable Utilisation of Coalbed Methane Resources in Abandoned Mines of Shanxi, China

Liu,

Li,

2024

Sustainability

View full text Add to dashboard Cite

Underground coal mining results in large goafs and numerous abandoned mines that contain substantial amounts of coalbed methane. If this methane is not used and controlled, it will escape into the atmosphere through geological fractures and can result in serious greenhouse gas effects and environmental damage. Exploring and developing the coalbed methane resources of abandoned mines can not only improve coal mine safety and protect the ecological environment but also reuse waste and mitigate energy shortages. Geophysical methods have made some progress in detecting abandoned coal mines, but there are still some challenges and difficulties. The resolution of seismic exploration may not be enough to accurately describe the details of coal seams and CBM rich areas, and the effect of resistivity method in deep CBM exploration is limited. In addition, the geological structure of abandoned coal mines is usually more complex, such as faults, folds, etc., which makes the application of exploration methods more difficult and increases the difficulty of data interpretation. Therefore, it is necessary to develop and perfect exploration technology continuously including the application of geophysical big data, deep learning, and artificial intelligence inversion to realize the accurate detection and evaluation of CBM resources in abandoned coal mines.

show abstract

Section: Direct Current Methodsmentioning

confidence: 99%

Methods for the Geophysical Exploration and Sustainable Utilisation of Coalbed Methane Resources in Abandoned Mines of Shanxi, China

Liu,

Li,

2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…From a geographical standpoint, ML-GPM studies have been carried out mainly in Asia, while there are very few in the African continent (Braham et al, 2022;Gómez-Escalonilla et al, 2021, 2022Martínez-Santos and Renard, 2020;Namous et al, 2021). Moreover, ours is one of the first studies in the literature addressing groundwater potential based on a multiclass approach.…”

Section: Introductionmentioning

confidence: 98%

“…The recent literature showcases several examples of ML algorithms in groundwater potential mapping studies. These include support vector machines (Al-Fugara et al, 2022;Panahi et al, 2020), decision trees (Al-Abadi et al, 2021;Arabameri et al, 2021;Braham et al, 2022), artificial neural networks (Chen et al, 2021;Nguyen et al, 2020;Hakim et al, 2022), and ensemble methods like boosting, random forests and extra trees classification, among others (Bai et al, 2022;Choudhary et al, 2022;Gómez-Escalonilla et al, 2021, 2022Martinsen et al, 2022;Sachdeva and Kumar, 2021).…”

Section: Introductionmentioning

confidence: 99%

Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers

Gómez-Escalonilla

Diancoumba²,

Traoré³

et al. 2022

Journal of Hydrology: Regional Studies

View full text Add to dashboard Cite

“…To identify groundwater potential zones, some studies have combined the GIS and RS with the multicriteria decision-making (MCDM) models (Saaty 2008;Aneesh and Deka 2015;Singh et al 2018;Maize et al, 2020). A few of them are the fuzzy logic (Mallick et al 2019), certainty factor (CF) (Razandi et al 2015), weighted overlay index (Prasad et al 2008), probabilistic models such as frequency ratio (FR) (Thapa et al 2017;Das and Pardeshi 2018;Braham et al 2022), multi-influencing factor (MIF) (Acharya et al 2019); machine learning (Saha et al 2022), and analytical hierarchy process (AHP) (Agarwal et al 2013;Ajay Kumar et al 2020;Arshad et al 2022;Aissaoui et al 2023). It is found that the use of the AHP model via GIS is an efficient and effective technique for the spatial data management, and also utilized to identify groundwater potential zones.…”

Section: Introductionmentioning

confidence: 99%

Geospatial techniques for deciphering groundwater potential zones of reservoir-induced seismicity region in western India using analytical hierarchy process

Ajaykumar,

Mondal

2023

Preprint

View full text Add to dashboard Cite

This study aims to demarcate groundwater potential zones using an integrated approach of remote sensing, geographical information system, and analytical hierarchy process in a reservoir-induced seismicity (RIS) region of western India. Multiple important factors affecting groundwater such as geology, geomorphology, soils, land use and land cover, slope, lineaments density, drainage density, rainfall, normalized vegetation index, and topography wetness index were considered for deciphering the groundwater potential zones. Spatially distributed thematic layers of all these factors were generated using remotely sensed data and ground-based field data in GIS platform. The assigned weights of all these layers and their attributes were then normalized by using analytical hierarchy process technique. The deciphered groundwater potential zones of this RIS area were categorized as very good (15.68%), good (27.34%), moderate (29.25%), poor (19.54%), and very poor (8.19%). These assessed groundwater potentialities were positively correlated with the specific yields of actual field data with a correlation coefficient of R = 0.90, and was found reasonable. It was also observed that the very good to good potential zones were in the upstreams. Most of the very good groundwater potential zones (~ 16.79%) were found in the northern part, namely Koyna region (which was more the seismically active) than the Warna region (~ 14.57%) located in the southern part. It indirectly indicated that the groundwater potentially also induced the seismicity of earthquakes along with both Koyna and Warna reservoir waters. The deciphered groundwater potential zones in this RIS area will aid in better study of the earthquake seismicity in future.

show abstract

Identification of groundwater potential zones using remote sensing, GIS, machine learning and electrical resistivity tomography techniques in Guelma basin, northeastern Algeria

Cited by 8 publications

References 43 publications

Methods for the Geophysical Exploration and Sustainable Utilisation of Coalbed Methane Resources in Abandoned Mines of Shanxi, China

Methods for the Geophysical Exploration and Sustainable Utilisation of Coalbed Methane Resources in Abandoned Mines of Shanxi, China

Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers

Geospatial techniques for deciphering groundwater potential zones of reservoir-induced seismicity region in western India using analytical hierarchy process

Contact Info

Product

Resources

About