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Remote Sensing
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Applying Remote Sensing, Geospatial Information Systems, and Machine Learning Algorithms...
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Applying Remote Sensing, Geospatial Information Systems, and Machine Learning Algorithms to Manage Groundwater Resources

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (1 July 2023) | Viewed by 13033

Special Issue Editors

Dr. Seyed Amir Naghibi

E-Mail Website
Guest Editor
Division of Water Resources Engineering, Centre for Middle Eastern Studies, Lund University, Finngatan 16, 22362 Lund, Sweden
Interests: remote sensing; machine learning; geospatial information systems; hydrogeology; programming
Prof. Dr. Biswajeet Pradhan

grade E-Mail Website
Guest Editor
Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: radar image processing remote sensing and GIS applications GIS for engineers forecasting disaster hazard; stochastic analysis and modelling; natural hazards; environmental engineering modelling; geospatial information systems; photogrammetry and remote sensing; unmanned aerial vehicles (UAVs).
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Groundwater resources have been extremely over-used during the past several decades, which has resulted in water table drop, land subsidence, shortage of water resources, and subsequently various socio-economic challenges. Considering the high dependency of governments and stakeholders on groundwater resources, it is highly recommended to use remote sensing (RS) data integrated with geospatial information systems (GIS)-based novel machine learning algorithms and programming languages to provide useful information and tools for water sector managers. The purpose of this Special Issue is to publish papers including original research and review papers on surface and groundwater interactions, groundwater potential, groundwater quality, artificial recharge, and land subsidence by implementing remote-sensing-derived data and advanced machine learning algorithms using spatial tools.

Topics of interest in this Special Issue include but are not limited to:

  • Groundwater potential assessment using RS data and machine learning algorithms;
  • Artificial recharge site selection using new approaches;
  • Spatial modelling of groundwater quality;
  • Spatial modelling of land subsidence;
  • Groundwater sustainable management using GIS and RS.

Dr. Seyed Amir Naghibi
Prof. Biswajeet Pradhan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing
  • machine learning
  • geospatial information systems
  • groundwater potential
  • groundwater quality
  • land subsidence

Published Papers (3 papers)

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Research

21 pages, 72718 KiB  
Article
Using UAV Photogrammetry and Automated Sensors to Assess Aquifer Recharge from a Coastal Wetland
by Santiago García-López, Mercedes Vélez-Nicolás, Javier Martínez-López, Angel Sánchez-Bellón, María Jesús Pacheco-Orellana, Verónica Ruiz-Ortiz, Juan José Muñoz-Pérez and Luis Barbero
Remote Sens. 2022, 14(24), 6185; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14246185 - 07 Dec 2022
Cited by 1 | Viewed by 1690
Abstract
Novel data-acquisition technologies have revolutionized the study of natural systems, allowing the massive collection of information in situ and remotely. Merging these technologies improves the understanding of complex hydrological interactions, such as those of wetland–aquifer systems, and facilitates their conservation and management. This [...] Read more.
Novel data-acquisition technologies have revolutionized the study of natural systems, allowing the massive collection of information in situ and remotely. Merging these technologies improves the understanding of complex hydrological interactions, such as those of wetland–aquifer systems, and facilitates their conservation and management. This paper presents the combination of UAV technology with water level dataloggers for the study of a coastal temporary wetland linked to an underlying sandy aquifer and influenced by the tidal regime. Wetland morphology was defined using UAV imagery and SfM algorithms during the dry period. The DTM (6.9 cm resolution) was used to generate a flood model, which was subsequently validated with an orthophoto from a wet period. This information was combined with water stage records at 10-min intervals from a network of dataloggers to infer the water balance of the wetland and the transfers to the aquifer. Inflows into the pond were around 6200 m3 (40% direct precipitation over the pond, 60% surface runoff). Outputs equalled the inputs (41% direct evaporation from water surface, 59% transfers into the aquifer). The proposed methodology has demonstrated its suitability to unravel complex wetland–aquifer interactions and to provide reliable estimations of the elements of the water balance. Full article
(This article belongs to the Special Issue Applying Remote Sensing, Geospatial Information Systems, and Machine Learning Algorithms to Manage Groundwater Resources)
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19 pages, 5307 KiB  
Article
Machine Learning Algorithms for Modeling and Mapping of Groundwater Pollution Risk: A Study to Reach Water Security and Sustainable Development (Sdg) Goals in a Mediterranean Aquifer System
by Safae Ijlil, Ali Essahlaoui, Meriame Mohajane, Narjisse Essahlaoui, El Mostafa Mili and Anton Van Rompaey
Remote Sens. 2022, 14(10), 2379; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102379 - 15 May 2022
Cited by 18 | Viewed by 4575
Abstract
Groundwater pollution poses a severe threat and issue to the environment and humanity overall. That is why mitigative strategies are urgently needed. Today, studies mapping groundwater risk pollution assessment are being developed. In this study, five new hybrid/ensemble machine learning (ML) models are [...] Read more.
Groundwater pollution poses a severe threat and issue to the environment and humanity overall. That is why mitigative strategies are urgently needed. Today, studies mapping groundwater risk pollution assessment are being developed. In this study, five new hybrid/ensemble machine learning (ML) models are developed, named DRASTIC-Random Forest (RF), DRASTIC-Support Vector Machine (SVM), DRASTIC-Multilayer Perceptron (MLP), DRASTIC-RF-SVM, and DRASTIC-RF-MLP, for groundwater pollution assessment in the Saiss basin, in Morocco. The performances of these models are evaluated using the Receiver Operating Characteristic curve (ROC curve), precision, and accuracy. Based on the results of the ROC curve method, it is indicated that the use of hybrid/ensemble machine learning (ML) models improves the performance of the individual machine learning (ML) algorithms. In effect, the AUC value of the original DRASTIC is 0.51. Furthermore, both hybrid/ensemble models, DRASTIC-RF-MLP (AUC = 0.953) and DRASTIC-RF-SVM, (AUC = 0.901) achieve the best accuracy among the other models, followed by DRASTIC-RF (AUC = 0.852), DRASTIC-SVM (AUC = 0.802), and DRASTIC-MLP (AUC = 0.763). The results delineate areas vulnerable to pollution, which require urgent actions and strategies to improve the environmental and social qualities for the local population. Full article
(This article belongs to the Special Issue Applying Remote Sensing, Geospatial Information Systems, and Machine Learning Algorithms to Manage Groundwater Resources)
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25 pages, 27498 KiB  
Article
Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data
by Ehsan Kamali Maskooni, Seyed Amir Naghibi, Hossein Hashemi and Ronny Berndtsson
Remote Sens. 2020, 12(17), 2742; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12172742 - 24 Aug 2020
Cited by 47 | Viewed by 5331
Abstract
Groundwater (GW) is being uncontrollably exploited in various parts of the world resulting from huge needs for water supply as an outcome of population growth and industrialization. Bearing in mind the importance of GW potential assessment in reaching sustainability, this study seeks to [...] Read more.
Groundwater (GW) is being uncontrollably exploited in various parts of the world resulting from huge needs for water supply as an outcome of population growth and industrialization. Bearing in mind the importance of GW potential assessment in reaching sustainability, this study seeks to use remote sensing (RS)-derived driving factors as an input of the advanced machine learning algorithms (MLAs), comprising deep boosting and logistic model trees to evaluate their efficiency. To do so, their results are compared with three benchmark MLAs such as boosted regression trees, k-nearest neighbors, and random forest. For this purpose, we firstly assembled different topographical, hydrological, RS-based, and lithological driving factors such as altitude, slope degree, aspect, slope length, plan curvature, profile curvature, relative slope position, distance from rivers, river density, topographic wetness index, land use/land cover (LULC), normalized difference vegetation index (NDVI), distance from lineament, lineament density, and lithology. The GW spring indicator was divided into two classes for training (434 springs) and validation (186 springs) with a proportion of 70:30. The training dataset of the springs accompanied by the driving factors were incorporated into the MLAs and the outputs were validated by different indices such as accuracy, kappa, receiver operating characteristics (ROC) curve, specificity, and sensitivity. Based upon the area under the ROC curve, the logistic model tree (87.813%) generated similar performance to deep boosting (87.807%), followed by boosted regression trees (87.397%), random forest (86.466%), and k-nearest neighbors (76.708%) MLAs. The findings confirm the great performance of the logistic model tree and deep boosting algorithms in modelling GW potential. Thus, their application can be suggested for other areas to obtain an insight about GW-related barriers toward sustainability. Further, the outcome based on the logistic model tree algorithm depicts the high impact of the RS-based factor, such as NDVI with 100 relative influence, as well as high influence of the distance from river, altitude, and RSP variables with 46.07, 43.47, and 37.20 relative influence, respectively, on GW potential. Full article
(This article belongs to the Special Issue Applying Remote Sensing, Geospatial Information Systems, and Machine Learning Algorithms to Manage Groundwater Resources)
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