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Novel Meta Heuristic Algorithms Based Advanced Machine Learning and Deep...
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Novel Meta Heuristic Algorithms Based Advanced Machine Learning and Deep Learning Methods in Water Resources

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 (10 July 2023) | Viewed by 10751

Special Issue Editors

Prof. Dr. Ozgur Kisi

grade E-Mail Website
Guest Editor
Department of Civil Engineering, Faculty of Natural Sciences and Engineering Ilia State University, 0162 Tbilisi, Georgia
Interests: developing novel algorithms and methods towards the innovative solution of hydrologic forecasting and modeling; suspended sediment modeling; forecasting, estimation, and spatial and temporal analysis of hydro-climatic variables such as precipitation, streamflow, suspended sediment, evaporation, evapotranspiration, groundwater, lake level and water quality parameters; hydro-informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Rana Muhammad Adnan

E-Mail Website
Guest Editor
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
Interests: sustainable development; hydrological modelling; artificial intelligence; time series analysis; rainfall–runoff relationship; wind energy; sediment load; evaporation; evapotranspiration; hydrometeorological droughts; groundwater; water quality parameters modeling; applications of novel metaheuristic approaches; trend analysis; watershed planning and management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the face of climate change and population growth in many parts of the world, we need appropriate tools that can assist in dealing with the difficulties introduced by the increasing complexity of water problems. Flooding and drought hazards cause numerous economic and life losses in the present changing climate and environment. It is, therefore, important to continue developing and improving our knowledge in the field of flood vulnerability assessment and hazard alleviation. Water resource management at the catchment level is a scientific discipline with great environmental importance. It is a multidisciplinary issue, which has prevailed from the cooperation of a wide range of scientists, such as engineers, Earth scientists, agronomists, environmentalists, biologists, and economists. The target is the optimal distribution of limited water resources and the preservation of acceptable levels of water quality, in such a way that all the users’ needs in domestic, agricultural, industrial, and ecological uses are satisfied with the least controversy and conflict. In order to achieve operational and efficient water management, we need to have reliable methodologies. This Special Issue will feature the latest advances and developments in operational hydrologic forecasts and water resource management. The focus is centered on advanced machine learning and deep learning methods for operational hydrologic forecasting for optimal water resource management. The computational power available today allows us to tackle simulation challenges in hydraulic and hydrological modeling at different scales that were impossible a few decades ago. However, even in the current situation, the time needed for these simulations is inadequate for many scientific and engineering applications, such as decision support systems, flood warning systems, design or optimization of hydraulic structures, calibration of model parameters, uncertainty quantification, real-time model-based control, etc.

To address these issues, the development of fast computing models to increase the simulation speed seems to be a promising strategy: It does not require a huge investment in new hardware and software, and the same tools can be used to solve very different problems. The main themes of this Special Issue include but are not limited to the following: 

  • Application of advanced machine learning models including deep learning methods for precise hydrologic forecasting (modeling rainfall, runoff, sediment, surface water and groundwater quality, lake level, water temperature, reservoir inflow, evaporation, evapotranspiration etc.);
  • Utilization of advanced machine learning models with ensemble models for solving water resource problems;
  • Spatial and temporal modeling of hydrological variable with aid of advanced computing models;
  • Coupling of data preprocessing techniques with machine learning methods to capture noise and nonlinear of hydrological variables;
  • Use and development of novel metaheuristic algorithms with machine learning methods to enhance their computing abilities.

Prof. Dr. Ozgur Kisi
Dr. Rana Muhammad Adnan
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

  • metaheuristic algorithms
  • data mining and deep learning
  • prediction
  • modeling
  • optimization
  • hybridization
  • soft computing
  • streamflow
  • rainfall–runoff
  • evaporation, evapotranspiration
  • water resource management
  • conservation and sustainability

Published Papers (5 papers)

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Research

19 pages, 5521 KiB  
Article
Inflow Prediction of Centralized Reservoir for the Operation of Pump Station in Urban Drainage Systems Using Improved Multilayer Perceptron Using Existing Optimizers Combined with Metaheuristic Optimization Algorithms
by Eui Hoon Lee
Water 2023, 15(8), 1543; https://doi.org/10.3390/w15081543 - 14 Apr 2023
Cited by 1 | Viewed by 1030
Abstract
Owing to the recent increase in abnormal climate, various structural measures including structural and non-structural approaches have been proposed for the prevention of potential water disasters. As a non-structural measure, fast and safe drainage is an essential preemptive operation of a drainage facility, [...] Read more.
Owing to the recent increase in abnormal climate, various structural measures including structural and non-structural approaches have been proposed for the prevention of potential water disasters. As a non-structural measure, fast and safe drainage is an essential preemptive operation of a drainage facility, including a centralized reservoir (CRs). To achieve such a preemptive operation, it is necessary to predict the inflow of the drainage facilities. Among the drainage facilities, CRs are located downstream of the drainage area, and their pump stations are operated according to the CR water level. The water level of a CR depends on the inflow, as does the preemptive operation of its pump station. In this study, as a nonstructural measure, the inflow prediction for the CR operation in an urban drainage system was proposed. For predicting the inflow of a CR, a new multilayer perceptron (MLP) using existing optimizers combined with a self-adaptive metaheuristic optimization algorithm, such as an improved harmony search, was proposed. Compared with the adaptive moment, which yields the best results among other existing optimizers, an MLP using an existing optimizer combined with an improved harmony search improves the mean square error and mean absolute error by 0.1767 and 0.0349, respectively. Full article
(This article belongs to the Special Issue Novel Meta Heuristic Algorithms Based Advanced Machine Learning and Deep Learning Methods in Water Resources)
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28 pages, 4906 KiB  
Article
Prediction of Sediment Yields Using a Data-Driven Radial M5 Tree Model
by Behrooz Keshtegar, Jamshid Piri, Waqas Ul Hussan, Kamran Ikram, Muhammad Yaseen, Ozgur Kisi, Rana Muhammad Adnan, Muhammad Adnan and Muhammad Waseem
Water 2023, 15(7), 1437; https://doi.org/10.3390/w15071437 - 06 Apr 2023
Cited by 6 | Viewed by 1645
Abstract
Reliable estimations of sediment yields are very important for investigations of river morphology and water resources management. Nowadays, soft computing methods are very helpful and famous regarding the accurate estimation of sediment loads. The present study checked the applicability of the radial M5 [...] Read more.
Reliable estimations of sediment yields are very important for investigations of river morphology and water resources management. Nowadays, soft computing methods are very helpful and famous regarding the accurate estimation of sediment loads. The present study checked the applicability of the radial M5 tree (RM5Tree) model to accurately estimate sediment yields using daily inputs of the snow cover fraction, air temperature, evapotranspiration and effective rainfall, in addition to the flow, in the Gilgit River, Upper Indus Basin (UIB) tributary, Pakistan. The results of the RM5Tree model were compared with support vector regression (SVR), artificial neural network (ANN), multivariate adaptive regression spline (MARS), M5Tree, sediment rating curve (SRC) and response surface method (RSM) models. The resulting accuracy of the models was assessed using Pearson’s correlation coefficient (R2), the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE). The prediction accuracy of the RM5Tree model during the testing period was superior to the ANN, MARS, SVR, M5Tree, RSM and SRC models with the R2, RMSE and MAPE being 0.72, 0.51 tons/day and 11.99%, respectively. The RM5Tree model predicted suspended sediment peaks better, with 84.10% relative accuracy, in comparison to the MARS, ANN, SVR, M5Tree, RSM and SRC models, with 80.62, 77.86, 81.90, 80.20, 74.58 and 62.49% relative accuracies, respectively. Full article
(This article belongs to the Special Issue Novel Meta Heuristic Algorithms Based Advanced Machine Learning and Deep Learning Methods in Water Resources)
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18 pages, 3861 KiB  
Article
Predicting Discharge Coefficient of Triangular Side Orifice Using LSSVM Optimized by Gravity Search Algorithm
by Payam Khosravinia, Mohammad Reza Nikpour, Ozgur Kisi and Rana Muhammad Adnan
Water 2023, 15(7), 1341; https://doi.org/10.3390/w15071341 - 29 Mar 2023
Cited by 3 | Viewed by 1357
Abstract
Side orifices are commonly installed in the side of a main channel to spill or divert some of the flow from the source channel to lateral channels. The aim of the present study is the accurate estimation of the discharge coefficient for flow [...] Read more.
Side orifices are commonly installed in the side of a main channel to spill or divert some of the flow from the source channel to lateral channels. The aim of the present study is the accurate estimation of the discharge coefficient for flow through triangular (Δ-shaped) side orifices by applying three data-driven models including support vector machine (SVM), least squares support vector machine (LSSVM) and least squares support vector machine improved by gravity search algorithm (LSSVM-GSA). The discharge coefficient was estimated by utilizing five dimensionless variables resulted from experimental data (570 runs). Five different scenarios were applied based on the input variables. The models were evaluated through several statistical indices and graphical charts. The results showed that all of the models could successfully estimate the discharge coefficient of Δ-shaped side orifices with adequate accuracy. However, the LSSVM-GSA produced the best performance for the input combination of all variables with the highest coefficients of determination (R2) and Nash–Sutcliffe efficiency (NSE), equal to 0.965 and 0.993, and the least root mean square error (RMSE) and mean absolute error (MAE), equal to 0.0099 and 0.0077, respectively. The LSSVM-GSA improved the RMSE of the SVM and LSSVM by 26% and 20% in estimating the discharge coefficient. Furthermore, the ratio of orifice crest height to orifice height (W/H) was identified as having the highest influence on the discharge coefficient of triangular side orifices among the various input variables. Full article
(This article belongs to the Special Issue Novel Meta Heuristic Algorithms Based Advanced Machine Learning and Deep Learning Methods in Water Resources)
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17 pages, 4057 KiB  
Article
Forecasting Long-Series Daily Reference Evapotranspiration Based on Best Subset Regression and Machine Learning in Egypt
by Ahmed Elbeltagi, Aman Srivastava, Abdullah Hassan Al-Saeedi, Ali Raza, Ismail Abd-Elaty and Mustafa El-Rawy
Water 2023, 15(6), 1149; https://doi.org/10.3390/w15061149 - 15 Mar 2023
Cited by 5 | Viewed by 1842
Abstract
The estimation of reference evapotranspiration (ETo), a crucial step in the hydrologic cycle, is essential for system design and management, including the balancing, planning, and scheduling of agricultural water supply and water resources. When climates vary from arid to semi-arid, and [...] Read more.
The estimation of reference evapotranspiration (ETo), a crucial step in the hydrologic cycle, is essential for system design and management, including the balancing, planning, and scheduling of agricultural water supply and water resources. When climates vary from arid to semi-arid, and there are problems with a lack of meteorological data and a lack of future information on ETo, as is the case in Egypt, it is more important to estimate ETo precisely. To address this, the current study aimed to model ETo for Egypt’s most important agricultural governorates (Al Buhayrah, Alexandria, Ismailiyah, and Minufiyah) using four machine learning (ML) algorithms: linear regression (LR), random subspace (RSS), additive regression (AR), and reduced error pruning tree (REPTree). The Climate Forecast System Reanalysis (CFSR) of the National Centers for Environmental Prediction (NCEP) was used to gather daily climate data variables from 1979 to 2014. The datasets were split into two sections: the training phase, i.e., 1979–2006, and the testing phase, i.e., 2007–2014. Maximum temperature (Tmax), minimum temperature (Tmin), and solar radiation (SR) were found to be the three input variables that had the most influence on the outcome of subset regression and sensitivity analysis. A comparative analysis of ML models revealed that REPTree outperformed competitors by achieving the best values for various performance matrices during the training and testing phases. The study’s novelty lies in the use of REPTree to estimate and predict ETo, as this algorithm has not been commonly used for this purpose. Given the sparse attempts to use this model for such research, the remarkable accuracy of the REPTree model in predicting ETo highlighted the rarity of this study. In order to combat the effects of aridity through better water resource management, the study also cautions Egypt’s authorities to concentrate their policymaking on climate adaptation. Full article
(This article belongs to the Special Issue Novel Meta Heuristic Algorithms Based Advanced Machine Learning and Deep Learning Methods in Water Resources)
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14 pages, 2426 KiB  
Article
A Water Quality Prediction Model Based on Multi-Task Deep Learning: A Case Study of the Yellow River, China
by Xijuan Wu, Qiang Zhang, Fei Wen and Ying Qi
Water 2022, 14(21), 3408; https://doi.org/10.3390/w14213408 - 27 Oct 2022
Cited by 8 | Viewed by 3579
Abstract
Water quality prediction is a fundamental and necessary task for the prevention and management of water environment pollution. Due to the fluidity of water, different sections of the same river have similar trends in their water quality. The present water quality prediction methods [...] Read more.
Water quality prediction is a fundamental and necessary task for the prevention and management of water environment pollution. Due to the fluidity of water, different sections of the same river have similar trends in their water quality. The present water quality prediction methods cannot exploit the correlation between the water quality of each section to deeply capture information because they do not take into account how similar the water quality is between sections. In order to address this issue, this paper constructs a water quality prediction model based on multi-task deep learning, taking the chemical oxygen demand (COD) of the water environment of the Lanzhou portion of the Yellow River as the research object. The multiple sections of correlation are trained and learned in this model at the same time, and the water quality information of each section is shared while retaining their respective heterogeneity, and the hybrid model CNN-LSTM is used for better mining from local to full time series features of water quality information. In comparison to the current single-section water quality prediction, experiments have shown that the model’s mean absolute error (MSE) and root mean square error (RMSE) of the predicted value of the model are decreased by 13.2% and 15.5%, respectively, and that it performs better in terms of time stability and generalization. Full article
(This article belongs to the Special Issue Novel Meta Heuristic Algorithms Based Advanced Machine Learning and Deep Learning Methods in Water Resources)
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