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Application of Artificial Intelligence Models for Prediction of Groundwater Level
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Application of Artificial Intelligence Models for Prediction of Groundwater Level

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: closed (25 January 2024) | Viewed by 6823

Special Issue Editors

Dr. Meysam Vadiati

E-Mail Website
Guest Editor
Hubert H. Humphrey Fellowship Program, Global Affairs, University of California, Davis, 10 College Park, Davis, CA 95616, USA
Interests: hydrogeology; machine learning; artificial intelligence; groundwater; clustering
Special Issues, Collections and Topics in MDPI journals
Dr. Saeideh Samani

E-Mail Website
Guest Editor Assistant
Water Research Institute, Ministry of Energy, Tehran, Iran
Interests: artificial intelligence; machine learning; predictive modeling; water resource management; hydrogeology; time series analysis; sustainable groundwater use

Special Issue Information

Dear Colleagues,

This Special Issue explores the application of artificial intelligence (AI) models in predicting groundwater levels (GWL) and its implications for water resource management and sustainability. The special issue will encompass a wide range of topics, including but not limited to:

  • Development and optimization of AI models for GWL prediction.
  • Integrating environmental variables, such as rainfall, temperature, and land use data, into AI models for improved accuracy.
  • Time series analysis techniques for capturing temporal patterns and trends in GWL data.
  • Incorporation of remote sensing data, such as satellite imagery and LiDAR data, in AI models for enhanced predictions.
  • Comparison and evaluation of different AI algorithms and techniques for GWL
  • Assessing the uncertainties and limitations associated with AI models in groundwater prediction.

This Special Issue will provide a platform to present cutting-edge research and advancements in the application of AI models in GWL prediction, fostering knowledge exchange and collaboration among researchers, hydrogeologists, and water resource managers. By integrating AI techniques with environmental data, time series analysis, and remote sensing, this special issue aims to push the boundaries of current knowledge and provide insights into the potential of AI models for accurate GWL prediction.

Dr. Meysam Vadiati
Guest Editor

Dr. Saeideh Samani
Guest Editor Assistant

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. Water 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 2600 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

  • machine learning
  • data analysis
  • hydrogeology
  • time series analysis
  • model optimization
  • uncertainty analysis
  • sustainable water management

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Published Papers (3 papers)

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Research

30 pages, 45867 KiB  
Article
Quantitative Assessment of Future Environmental Changes in Hydrological Risk Components: Integration of Remote Sensing, Machine Learning, and Hydraulic Modeling
by Farinaz Gholami, Yue Li, Junlong Zhang and Alireza Nemati
Water 2024, 16(23), 3354; https://doi.org/10.3390/w16233354 - 22 Nov 2024
Abstract
Floods are one of the most devastating natural hazards that have intensified due to land use land cover (LULC) changes in recent years. Flood risk assessment is a crucial task for disaster management in flood-prone areas. In this study, we proposed a flood [...] Read more.
Floods are one of the most devastating natural hazards that have intensified due to land use land cover (LULC) changes in recent years. Flood risk assessment is a crucial task for disaster management in flood-prone areas. In this study, we proposed a flood risk assessment framework that combines flood vulnerability, hazard, and damages under long-term LULC changes in the Tajan watershed, northern Iran. The research analyzed historical land use change trends and predicted changes up to 2040 by employing a Geographic Information System (GIS), remote sensing, and land change modeling. The flood vulnerability map was generated using the Random Forest model, incorporating historical data from 332 flooded locations and 12 geophysical and anthropogenic flood factors under LULC change scenarios. The potential flood damage costs in residential and agricultural areas, considering long-term LULC changes, were calculated using the HEC-RAS hydraulic model and a global damage function. The results revealed that unplanned urban growth, agricultural expansion, and deforestation near the river downstream amplify flood risk in 2040. High and very high flood vulnerability areas would increase by 43% in 2040 due to human activities and LULC changes. Estimated annual flood damage for agriculture and built-up areas was projected to surge from USD 162 million to USD 376 million and USD 91 million to USD 220 million, respectively, considering 2021 and 2040 land use change scenarios in the flood-prone region. This research highlights the importance of land use planning in mitigating flood-associated risks, both in the studied area and other flood-prone regions. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence Models for Prediction of Groundwater Level)
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14 pages, 2726 KiB  
Article
Synthetic Time Series Data in Groundwater Analytics: Challenges, Insights, and Applications
by Sarva T. Pulla, Hakan Yasarer and Lance D. Yarbrough
Water 2024, 16(7), 949; https://doi.org/10.3390/w16070949 - 25 Mar 2024
Cited by 1 | Viewed by 1337
Abstract
This study presents ‘Synthetic Wells’, a method for generating synthetic groundwater level time series data using machine learning (ML) aimed at improving groundwater management in contexts where real data are scarce. Utilizing data from the National Water Information System of the US Geological [...] Read more.
This study presents ‘Synthetic Wells’, a method for generating synthetic groundwater level time series data using machine learning (ML) aimed at improving groundwater management in contexts where real data are scarce. Utilizing data from the National Water Information System of the US Geological Survey, this research employs the Synthetic Data Vault (SDV) framework’s Probabilistic AutoRegressive (PAR) synthesizer model to simulate real-world groundwater fluctuations. The synthetic data generated for approximately 100 wells align closely with the real data, achieving a quality score of 70.94%, indicating a reasonable replication of groundwater dynamics. A Streamlit-based web application was also developed, enabling users to generate custom synthetic datasets. A case study in Mississippi, USA, demonstrated the utility of synthetic data in enhancing the accuracy of time series forecasting models. This unique approach represents an innovative first-of-its-kind tool in the realm of groundwater research, providing new avenues for data-driven decision-making and management in hydrological studies. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence Models for Prediction of Groundwater Level)
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21 pages, 5354 KiB  
Article
Groundwater Level Prediction with Machine Learning to Support Sustainable Irrigation in Water Scarcity Regions
by Wanru Li, Mekuanent Muluneh Finsa, Kathryn Blackmond Laskey, Paul Houser and Rupert Douglas-Bate
Water 2023, 15(19), 3473; https://doi.org/10.3390/w15193473 - 1 Oct 2023
Cited by 17 | Viewed by 4671
Abstract
Predicting groundwater levels is challenging, especially in regions of water scarcity where data availability is often limited. However, these regions have substantial water needs and require cost-effective groundwater utilization strategies. This study uses artificial intelligence to predict groundwater levels to provide guidance for [...] Read more.
Predicting groundwater levels is challenging, especially in regions of water scarcity where data availability is often limited. However, these regions have substantial water needs and require cost-effective groundwater utilization strategies. This study uses artificial intelligence to predict groundwater levels to provide guidance for drilling shallow boreholes for subsistence irrigation. The Bilate watershed, located 80 km north of Arba Minch in southern Ethiopia and covering just over 5250 km2, was selected as the study area. Bilate is typical of areas in Africa with high demand for water and limited availability of well data. Using a non-time series database of 75 boreholes, machine learning models, including multiple linear regression, multivariate adaptive regression splines, artificial neural networks, random forest regression, and gradient boosting regression (GBR), were constructed to predict the depth to the water table. The study considered 20 independent variables, including elevation, soil type, and seasonal data (spanning three seasons) for precipitation, specific humidity, wind speed, land surface temperature during day and night, and Normalized Difference Vegetation Index (NDVI). GBR performed the best of the approaches, with an average 0.77 R-squared value and a 19 m median absolute error on testing data. Finally, a map of predicted water levels in the Bilate watershed was created based on the best model, with water levels ranging from 1.6 to 245.9 m. With the limited set of borehole data, the results show a clear signal that can provide guidance for borehole drilling decisions for sustainable irrigation with additional implications for drinking water. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence Models for Prediction of Groundwater Level)
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