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Projection of Groundwater Levels, Spring Flows, Lake Dynamics, Extreme Drought Events, and Floods under Future Climates Using Artificial Intelligence Models

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water and Climate Change".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 21480

Special Issue Editors

Dr. Hakan Başağaoğlu

E-Mail Website
Guest Editor
Edwards Aquifer Authority, San Antonio, TX 78215, USA
Interests: hydrological drought projections; artificial intelligence; machine learning; statistical downscaling; karstic aquifers; groundwater flow modeling
Dr. Debaditya Chakraborty

E-Mail Website
Guest Editor
Department of Construction Science, University of Texas at San Antonio, San Antonio, TX 78207, USA
Interests: artificial intelligence; machine learning; data science; statistical downscaling; projections of hydrological processes
Dr. Marcio Giacomoni

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA
Interests: sustainability of water resources and natural environments; drought management and water conservation; flood projections; water resources systems analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hydrological sciences and engineering applications have successfully incorporated a wide array of data-driven modeling and analysis approaches, which expand our technical capacity so that we may better understand critical water-related challenges facing hydroclimatological systems around the world.

For the upcoming Special Issue of Water, we invite original technical and review papers on the artificial intelligence-based prediction of local-, regional-, and global-scale hydrologic and hydroclimatic features and processes (e.g., groundwater levels, spring flows, soil moisture, lake and streamflow dynamics, and water quality) and the projection of extreme hydrological events (e.g., droughts or floods) under a changing climate over the next several decades (2021–2100).

We are also interested in innovative research studies employing the statistical downscaling of projected climate variables from global circulation models to a local or regional scale and the use of these projected downscaled climate data with artificial intelligence models for projections of hydroclimatic events. We expect that the findings will establish grounds to investigate and recommend potential mitigation and adaptation measures for future climate change-resilient water management strategies.

The subject areas include, but are not limited to, the impacts of changes in the climate, enviromental factors, and human activities on:

  • Groundwater levels, spring flows, and water quality measures crucial for sustainable consumptive water uses and environmental flows;
  • Lake and stream flow dynamics;
  • Evapotranspiration, soil moisture, and crop yields;
  • The intensity, frequency, and duration of drought and flood events.

Dr. Hakan Başağaoğlu
Dr. Debaditya Chakraborty
Dr. Marcio Giacomoni
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. 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

  • artificial intelligence
  • future projections
  • climate change
  • hydrological droughts
  • soil moisture
  • groundwater
  • floods
  • lake and surface water dynamics
  • sustainability
  • statistical downscaling

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Published Papers (4 papers)

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Research

Jump to: Review

29 pages, 3563 KiB  
Article
Development of Monthly Reference Evapotranspiration Machine Learning Models and Mapping of Pakistan—A Comparative Study
by Jizhang Wang, Ali Raza, Yongguang Hu, Noman Ali Buttar, Muhammad Shoaib, Kouadri Saber, Pingping Li, Ahmed Elbeltagi and Ram L. Ray
Water 2022, 14(10), 1666; https://doi.org/10.3390/w14101666 - 23 May 2022
Cited by 13 | Viewed by 2927
Abstract
Accurate estimation of reference evapotranspiration (ETo) plays a vital role in irrigation and water resource planning. The Penman–Monteith method recommended by the Food and Agriculture Organization (FAO PM56) is widely used and considered a standard to calculate ETo. However, [...] Read more.
Accurate estimation of reference evapotranspiration (ETo) plays a vital role in irrigation and water resource planning. The Penman–Monteith method recommended by the Food and Agriculture Organization (FAO PM56) is widely used and considered a standard to calculate ETo. However, FAO PM56 cannot be used with limited meteorological variables, so it is compulsory to choose an alternative model for ETo estimation, which requires fewer variables. This study built ten machine learning (ML) models based on multi-function, neural network, and tree-based structure against the FAO PM56 method. For this purpose, time series temperature data on a monthly scale are only used to train ML models. The developed ML models were applied to estimate ETo at different test stations and the obtained results were compared with the FAO PM56 method to verify and validate their performance in ETo estimation for the selected stations. In addition, multiple statistical indicators, including root-mean-square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and correlation coefficient (r) were calculated to compare the performance of each ML model on ETo estimation. Among the applied ML models, the ETo tree boost (TB) ML model outperformed the other ML models in estimating ETo in diverse climatic conditions based on statistical indicators (R2, NSE, r, RMSE, and MAE). Moreover, the observed R2, NSE, and r were the highest for the TB ML model, while RMSE and MAE were found to be the lowest at the study sites compared to other applied ML models. Lastly, ETo point data yielded from the TB ML model was used in an interpolation process to create monthly and annual ETo maps. Based on the ETo maps, this study suggests mainly a focus on areas with high ETo values and proper irrigation scheduling of crops to ensure water sustainability. Full article
(This article belongs to the Special Issue Projection of Groundwater Levels, Spring Flows, Lake Dynamics, Extreme Drought Events, and Floods under Future Climates Using Artificial Intelligence Models)
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22 pages, 2250 KiB  
Article
Improving Drought Modeling Using Hybrid Random Vector Functional Link Methods
by Rana Muhammad Adnan, Reham R. Mostafa, Abu Reza Md. Towfiqul Islam, Alireza Docheshmeh Gorgij, Alban Kuriqi and Ozgur Kisi
Water 2021, 13(23), 3379; https://doi.org/10.3390/w13233379 - 1 Dec 2021
Cited by 47 | Viewed by 3862
Abstract
Drought modeling is essential in water resources planning and management in mitigating its effects, especially in arid regions. Climate change highly influences the frequency and intensity of droughts. In this study, new hybrid methods, the random vector functional link (RVFL) integrated with particle [...] Read more.
Drought modeling is essential in water resources planning and management in mitigating its effects, especially in arid regions. Climate change highly influences the frequency and intensity of droughts. In this study, new hybrid methods, the random vector functional link (RVFL) integrated with particle swarm optimization (PSO), the genetic algorithm (GA), the grey wolf optimization (GWO), the social spider optimization (SSO), the salp swarm algorithm (SSA) and the hunger games search algorithm (HGS) were used to forecast droughts based on the standard precipitation index (SPI). Monthly precipitation data from three stations in Bangladesh were used in the applications. The accuracy of the methods was compared by forecasting four SPI indices, SPI3, SPI6, SPI9, and SPI12, using the root mean square errors (RMSE), the mean absolute error (MAE), the Nash–Sutcliffe efficiency (NSE), and the determination coefficient (R2). The HGS algorithm provided a better performance than the alternative algorithms, and it considerably improved the accuracy of the RVFL method in drought forecasting; the improvement in RMSE for the SPI3, SP6, SPI9, and SPI12 was by 6.14%, 11.89%, 14.14%, 24.5% in station 1, by 6.02%, 17.42%, 13.49%, 24.86% in station 2 and by 7.55%, 26.45%, 15.27%, 13.21% in station 3, respectively. The outcomes of the study recommend the use of a HGS-based RVFL in drought modeling. Full article
(This article belongs to the Special Issue Projection of Groundwater Levels, Spring Flows, Lake Dynamics, Extreme Drought Events, and Floods under Future Climates Using Artificial Intelligence Models)
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35 pages, 7498 KiB  
Article
Groundwater Level Prediction Using a Multiple Objective Genetic Algorithm-Grey Relational Analysis Based Weighted Ensemble of ANFIS Models
by Dilip Kumar Roy, Sujit Kumar Biswas, Mohamed A. Mattar, Ahmed A. El-Shafei, Khandakar Faisal Ibn Murad, Kowshik Kumar Saha, Bithin Datta and Ahmed Z. Dewidar
Water 2021, 13(21), 3130; https://doi.org/10.3390/w13213130 - 6 Nov 2021
Cited by 14 | Viewed by 3827
Abstract
Predicting groundwater levels is critical for ensuring sustainable use of an aquifer’s limited groundwater reserves and developing a useful groundwater abstraction management strategy. The purpose of this study was to assess the predictive accuracy and estimation capability of various models based on the [...] Read more.
Predicting groundwater levels is critical for ensuring sustainable use of an aquifer’s limited groundwater reserves and developing a useful groundwater abstraction management strategy. The purpose of this study was to assess the predictive accuracy and estimation capability of various models based on the Adaptive Neuro Fuzzy Inference System (ANFIS). These models included Differential Evolution-ANFIS (DE-ANFIS), Particle Swarm Optimization-ANFIS (PSO-ANFIS), and traditional Hybrid Algorithm tuned ANFIS (HA-ANFIS) for the one- and multi-week forward forecast of groundwater levels at three observation wells. Model-independent partial autocorrelation functions followed by frequentist lasso regression-based feature selection approaches were used to recognize appropriate input variables for the prediction models. The performances of the ANFIS models were evaluated using various statistical performance evaluation indexes. The results revealed that the optimized ANFIS models performed equally well in predicting one-week-ahead groundwater levels at the observation wells when a set of various performance evaluation indexes were used. For improving prediction accuracy, a weighted-average ensemble of ANFIS models was proposed, in which weights for the individual ANFIS models were calculated using a Multiple Objective Genetic Algorithm (MOGA). The MOGA accounts for a set of benefits (higher values indicate better model performance) and cost (smaller values indicate better model performance) performance indexes calculated on the test dataset. Grey relational analysis was used to select the best solution from a set of feasible solutions produced by a MOGA. A MOGA-based individual model ranking revealed the superiority of DE-ANFIS (weight = 0.827), HA-ANFIS (weight = 0.524), and HA-ANFIS (weight = 0.697) at observation wells GT8194046, GT8194048, and GT8194049, respectively. Shannon’s entropy-based decision theory was utilized to rank the ensemble and individual ANFIS models using a set of performance indexes. The ranking result indicated that the ensemble model outperformed all individual models at all observation wells (ranking value = 0.987, 0.985, and 0.995 at observation wells GT8194046, GT8194048, and GT8194049, respectively). The worst performers were PSO-ANFIS (ranking value = 0.845), PSO-ANFIS (ranking value = 0.819), and DE-ANFIS (ranking value = 0.900) at observation wells GT8194046, GT8194048, and GT8194049, respectively. The generalization capability of the proposed ensemble modelling approach was evaluated for forecasting 2-, 4-, 6-, and 8-weeks ahead groundwater levels using data from GT8194046. The evaluation results confirmed the useability of the ensemble modelling for forecasting groundwater levels at higher forecasting horizons. The study demonstrated that the ensemble approach may be successfully used to predict multi-week-ahead groundwater levels, utilizing previous lagged groundwater levels as inputs. Full article
(This article belongs to the Special Issue Projection of Groundwater Levels, Spring Flows, Lake Dynamics, Extreme Drought Events, and Floods under Future Climates Using Artificial Intelligence Models)
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Review

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36 pages, 993 KiB  
Review
A Review on Interpretable and Explainable Artificial Intelligence in Hydroclimatic Applications
by Hakan Başağaoğlu, Debaditya Chakraborty, Cesar Do Lago, Lilianna Gutierrez, Mehmet Arif Şahinli, Marcio Giacomoni, Chad Furl, Ali Mirchi, Daniel Moriasi and Sema Sevinç Şengör
Water 2022, 14(8), 1230; https://doi.org/10.3390/w14081230 - 11 Apr 2022
Cited by 33 | Viewed by 9217
Abstract
This review focuses on the use of Interpretable Artificial Intelligence (IAI) and eXplainable Artificial Intelligence (XAI) models for data imputations and numerical or categorical hydroclimatic predictions from nonlinearly combined multidimensional predictors. The AI models considered in this paper involve Extreme Gradient Boosting, Light [...] Read more.
This review focuses on the use of Interpretable Artificial Intelligence (IAI) and eXplainable Artificial Intelligence (XAI) models for data imputations and numerical or categorical hydroclimatic predictions from nonlinearly combined multidimensional predictors. The AI models considered in this paper involve Extreme Gradient Boosting, Light Gradient Boosting, Categorical Boosting, Extremely Randomized Trees, and Random Forest. These AI models can transform into XAI models when they are coupled with the explanatory methods such as the Shapley additive explanations and local interpretable model-agnostic explanations. The review highlights that the IAI models are capable of unveiling the rationale behind the predictions while XAI models are capable of discovering new knowledge and justifying AI-based results, which are critical for enhanced accountability of AI-driven predictions. The review also elaborates the importance of domain knowledge and interventional IAI modeling, potential advantages and disadvantages of hybrid IAI and non-IAI predictive modeling, unequivocal importance of balanced data in categorical decisions, and the choice and performance of IAI versus physics-based modeling. The review concludes with a proposed XAI framework to enhance the interpretability and explainability of AI models for hydroclimatic applications. Full article
(This article belongs to the Special Issue Projection of Groundwater Levels, Spring Flows, Lake Dynamics, Extreme Drought Events, and Floods under Future Climates Using Artificial Intelligence Models)
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