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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 March 2024) | Viewed by 13762

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Special Issue Editors

Dr. Quoc Bao Pham

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Guest Editor

Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska Street 60, 41-200 Sosnowiec, Poland
Interests: hydrology; groundwater; machine learning; water resources management; climate change; GIS; remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Sk Ajim Ali

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Guest Editor

Department of Geography, Faculty of Science, Aligarh Muslim University, Aligarh 202002, India
Interests: geographic information system (GIS); remote sensing (RS); machine learning; disaster management; natural hazards; multiple-criteria decision making (MCDM); water quality, waste management
Special Issues, Collections and Topics in MDPI journals

Dr. Sani Isah Abba

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Guest Editor

Interdisciplinary Research Centre for Membrane and Water Security, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Interests: machine learning; artificial intelligence; hydrology; climate change; environmental monitoring; water resources; groundwater

Dr. Rana Muhammad Adnan Ikram

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Guest Editor

College of Architecture and Urban Planning, Guangzhou University, Guangzhou, China
Interests: sustainable environment development; water resources management; hydrological modeling; artificial intelligence; time series analysis ;rainfall–runoff
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

(1) Introduction, including scientific background and highlighting the importance of this research area

The journal Water (ISSN: 2073-4441, IF 3.530) is currently running a Special Issue entitled “Artificial Intelligence and Machine/Deep Learning for Hydro-Meteorological Forecasting”. Dr. Quoc Bao Pham, Dr. Sk Ajim Ali, Dr. Sani Isah Abba and Dr. Rana Muhammad Adnan Ikram are serving as Guest Editors for this Special Issue. We think you could make an exceptional contribution based on your expertise in this particular field.

Hydro-meteorological extremes have a broad spatial scope and a time sequence, and can be impacted by a variety of climatological and geographic factors. Making effective forecasts requires an understanding of the pertinent spatial and temporal information. The competences of artificial intelligence (AI) methods can be applied in such instances due to their enhanced abilities in learning complex relationships.

Recently, modelling and prediction of hydrological processes, climate change, and earth systems have benefited greatly from the use of AI technologies. Among them, machine learning (ML) and deep learning (DL) techniques are primarily cited as being necessary to improve model performance in terms of accuracy, robustness, efficiency, and computation cost. AI has the potential to lighten meteorologists' workloads, while increasing the precision of weather predictions. Scientists will have a better chance of warning people in danger, since AI technology can interpret data under harsh weather conditions quickly and accurately.

Considering these advantages of AI and machine/deep learning, the main objective of this Special Issue is to provide a scientific forum for advancing the successful application of artificial intelligence and machine/deep learning models toward hydro-meteorological forecasting and monitoring in various climate-related hazard-prone regions of the earth, as well as to foster informed discussions among scientists and stakeholders on this pressing issue.

(2) Aim of the Special Issue and how the subject relates to the journal scope

Scientists can benefit from the use of artificial intelligence systems, machine learning, neural networks, and deep learning while performing complex tasks such as weather forecasting. These technologies are very versatile and have been demonstrated to be more accurate than conventional methods at predicting weather patterns. Systems can be given a large amount of information, and after analysing the data they receive, they can learn to recognise natural phenomena, such as hurricanes, storms, snowfalls, and much more.

Thus, this Special Issue aims to provide an outlet for high-quality peer-reviewed publications that implement state-of-the-art models and techniques that incorporate AI- and ML-based methods to map, evaluate, and model hydro-meteorological forecasting, its monitoring, and their implications together, with the framing of newer hypotheses that can further our understanding of operative processes.

(3) Suggested themes and article types for submissions

The Special Issue may include (without being limited to) the following themes:

Artificial intelligence in Hydro-meteorology;
Data-driven approach for hydro-meteorological modelling;
Machine and deep learning in micro-climate assessment;
AI and machine learning for weather predictions;
Spatio-temporal hydrological extremes through AI and machine learning;
Machine learning for weather and climate;
Monitoring hydrological hazards through remote sensing, GIS and machine learning;
Artificial intelligence for disaster risk reduction;
Potential of deep learning in multi-hazard assessment.

Given your competence in this area, we invite you to contribute a paper on the aforementioned subjects or any relevant issues.

Dr. Quoc Bao Pham
Dr. Sk Ajim Ali
Dr. Sani Isah Abba
Dr. Rana Muhammad Adnan Ikram
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
machine learning
deep learning
climate change forecasting
hydro-meteorological analysis
early warning system
multi-hazard

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Published Papers (5 papers)

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Research

30 pages, 10753 KiB

Open AccessArticle

Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate

by Ali Raza, Romana Fahmeed, Neyha Rubab Syed, Okan Mert Katipoğlu, Muhammad Zubair, Fahad Alshehri and Ahmed Elbeltagi

Water 2023, 15(21), 3822; https://doi.org/10.3390/w15213822 - 1 Nov 2023

Cited by 6 | Viewed by 2039

Abstract

The Food and Agriculture Organization recommends that the Penman–Monteith Method contains Equation 56 (PMF) as a widely accepted standard for reference evapotranspiration (ET_o) calculation. Despite this, the PMF cannot be employed when meteorological variables are constrained; therefore, alternative models for ET_o estimation requiring fewer variables must be chosen, which means that they perform at least as well as, if not better than, the PMF in terms of accuracy and efficiency. This study evaluated five machine learning (ML) algorithms to estimate ET_o and compared their results with the standardized PMF. For this purpose, ML models were trained using monthly time series climatic data. The created ML models underwent testing to determine ET_o under varying meteorological input combinations. The results of ML models were compared to assess their accuracy and validate their performance using several statistical indicators, errors (root-mean-square (RMSE), mean absolute error (MAE)), model efficiency (NSE), and determination coefficient (R²). The process of evaluating ML models involved the utilization of radar charts, Smith graphs, heatmaps, and bullet charts. Based on our findings, satisfactory results have been obtained using RBFFNN based on M12 input combinations (mean temperature (T_mean), mean relative humidity (RH_mean), sunshine hours (Sh)) for ET_o estimation. The RBFFNN model exhibited the most precise estimation as RMSE obtained values of 0.30 and 0.22 during the training and testing phases, respectively. In addition, during training and testing, the MAE values for this model were recorded as 0.15 and 0.17, respectively. The highest R² and NSE values were noted as 0.98 and 0.99 for the RBFNN during performance analysis, respectively. The scatter plots and spatial variations of the RBFNN and PMF in the studied region indicated that the RBFNN had the highest efficacy (R², NSE) and lowest errors (RMSE, MAE) as compared with the other four ML models. Overall, our study highlights the potential of ML models for ET_o estimation in the arid region (Jacobabad), providing vital insights for improving water resource management, helping climate change research, and optimizing irrigation scheduling for optimal agricultural water usage in the region. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine/Deep Learning for Hydro-Meteorological Forecasting)

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24 pages, 5565 KiB

Open AccessArticle

Modeling of Monthly Rainfall–Runoff Using Various Machine Learning Techniques in Wadi Ouahrane Basin, Algeria

by Mahdi Valikhan Anaraki, Mohammed Achite, Saeed Farzin, Nehal Elshaboury, Nadhir Al-Ansari and Ismail Elkhrachy

Water 2023, 15(20), 3576; https://doi.org/10.3390/w15203576 - 12 Oct 2023

Cited by 11 | Viewed by 2270

Abstract

Rainfall–runoff modeling has been the core of hydrological research studies for decades. To comprehend this phenomenon, many machine learning algorithms have been widely used. Nevertheless, a thorough comparison of machine learning algorithms and the effect of pre-processing on their performance is still lacking in the literature. Therefore, the major objective of this research is to simulate rainfall runoff using nine standalone and hybrid machine learning models. The conventional models include artificial neural networks, least squares support vector machines (LSSVMs), K-nearest neighbor (KNN), M5 model trees, random forests, multiple adaptive regression splines, and multivariate nonlinear regression. In contrast, the hybrid models comprise LSSVM and KNN coupled with a gorilla troop optimizer (GTO). Moreover, the present study introduces a new combination of the feature selection method, principal component analysis (PCA), and empirical mode decomposition (EMD). Mean absolute error (MAE), root mean squared error (RMSE), relative RMSE (RRMSE), person correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), and Kling Gupta efficiency (KGE) metrics are used for assessing the performance of the developed models. The proposed models are applied to rainfall and runoff data collected in the Wadi Ouahrane basin, Algeria. According to the results, the KNN–GTO model exhibits the best performance (MAE = 0.1640, RMSE = 0.4741, RRMSE = 0.2979, R = 0.9607, NSE = 0.9088, and KGE = 0.7141). These statistical criteria outperform other developed models by 80%, 70%, 72%, 77%, 112%, and 136%, respectively. The LSSVM model provides the worst results without pre-processing the data. Moreover, the findings indicate that using feature selection, PCA, and EMD significantly improves the accuracy of rainfall–runoff modeling. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine/Deep Learning for Hydro-Meteorological Forecasting)

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19 pages, 6765 KiB

Open AccessFeature PaperArticle

Ranking Sub-Watersheds for Flood Hazard Mapping: A Multi-Criteria Decision-Making Approach

by Nguyet-Minh Nguyen, Reza Bahramloo, Jalal Sadeghian, Mehdi Sepehri, Hadi Nazaripouya, Vuong Nguyen Dinh, Afshin Ghahramani, Ali Talebi, Ismail Elkhrachy, Chaitanya B. Pande and Sarita Gajbhiye Meshram

Water 2023, 15(11), 2128; https://doi.org/10.3390/w15112128 - 3 Jun 2023

Cited by 9 | Viewed by 2091

Abstract

The aim of this paper is to assess the extent to which the Sad-Kalan watershed in Iran participates in floods and rank the Sad-Kalan sub-watersheds in terms of flooding potential by utilizing multi-criteria decision-making approaches. We employed the entropy of a drainage network, stream power index (SPI), slope, topographic control index (TCI), and compactness coefficient (Cc) in this investigation. After forming a decision matrix with 25 possibilities (sub-watersheds) and 5 evaluation indices, we used four MCDM approaches, including the analytic hierarchy process (AHP), best–worst method (BWM), interval rough numbers AHP (IRNAHP), picture fuzzy with AHP (PF-AHP), and picture fuzzy with linear assignment model (PF-LAM, hereafter PICALAM) algorithms, to rank the sub-watersheds. The study results demonstrated that PICALAM exhibited superior performance compared to the other methods due to its consideration of both local and global weights for each criterion. Additionally, among the methods used (AHP, BWM, and IRNAHP) that showed similar performances in ranking the sub-watersheds, the BWM method proved to be more time-efficient in the ranking process. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine/Deep Learning for Hydro-Meteorological Forecasting)

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17 pages, 4309 KiB

Open AccessArticle

Runoff Prediction of Irrigated Paddy Areas in Southern China Based on EEMD-LSTM Model

by Shaozhe Huang, Lei Yu, Wenbing Luo, Hongzhong Pan, Yalong Li, Zhike Zou, Wenjuan Wang and Jialong Chen

Water 2023, 15(9), 1704; https://doi.org/10.3390/w15091704 - 27 Apr 2023

Cited by 7 | Viewed by 1524

Abstract

To overcome the difficulty that existing hydrological models cannot accurately simulate hydrological processes with limited information in irrigated paddy areas in southern China, this paper presents a prediction model combining the Ensemble Empirical Mode Decomposition (EEMD) method and the Long Short-Term Memory (LSTM) network. Meteorological factors were set as the multivariate input to the model. Rainfall, regarded as the main variable affecting runoff, was decomposed and reconstructed into a combination of new series with stronger regularity by using the EEMD and K-means algorithm. The LSTM was used to explore the data laws and then to simulate and predict the runoff of the irrigated paddy areas. The Yangshudang (YSD) watershed of the Zhanghe Irrigation System (ZIS) in Hubei Province, China was taken as the study area. Compared with the other models, the results show that the EEMD-LSTM multivariate model had better simulation performance, with an

N S E

above 0.85. Among them, the

R^{2}

N S E

R M S E

and

R A E

of the EEMD-LSTM

^{(3)}

model were the best, and they were 0.85, 0.86, 1.106 and 0.35, respectively. The prediction accuracy of peak flows was better than other models, as well as the performance of runoff prediction in rainfall and nonrainfall events, while improving the

N S E

by 0.05, 0.24 and 0.24, respectively, compared with the EEMD-LSTM

^{(1)}

model. Overall, the EEMD-LSTM multivariations model is suited for simulating and predicting the daily-scale rainfall–runoff process of irrigated paddy areas in southern China. It can provide technical support and help decision making for efficient utilization and management of water resources. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine/Deep Learning for Hydro-Meteorological Forecasting)

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20 pages, 6102 KiB

Open AccessArticle

Performance of Machine Learning Techniques for Meteorological Drought Forecasting in the Wadi Mina Basin, Algeria

by Mohammed Achite, Nehal Elshaboury, Muhammad Jehanzaib, Dinesh Kumar Vishwakarma, Quoc Bao Pham, Duong Tran Anh, Eslam Mohammed Abdelkader and Ahmed Elbeltagi

Water 2023, 15(4), 765; https://doi.org/10.3390/w15040765 - 15 Feb 2023

Cited by 24 | Viewed by 4050

Abstract

Water resources, land and soil degradation, desertification, agricultural productivity, and food security are all adversely influenced by drought. The prediction of meteorological droughts using the standardized precipitation index (SPI) is crucial for water resource management. The modeling results for SPI at 3, 6, 9, and 12 months are based on five types of machine learning: support vector machine (SVM), additive regression, bagging, random subspace, and random forest. After training, testing, and cross-validation at five folds on sub-basin 1, the results concluded that SVM is the most effective model for predicting SPI for different months (3, 6, 9, and 12). Then, SVM, as the best model, was applied on sub-basin 2 for predicting SPI at different timescales and it achieved satisfactory outcomes. Its performance was validated on sub-basin 2 and satisfactory results were achieved. The suggested model performed better than the other models for estimating drought at sub-basins during the testing phase. The suggested model could be used to predict meteorological drought on several timescales, choose remedial measures for research basin, and assist in the management of sustainable water resources. Full article

(This article belongs to the Special Issue Artificial Intelligence and Machine/Deep Learning for Hydro-Meteorological Forecasting)

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