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Special Issue "Application of AI and UAV Techniques in Urban Water Science"

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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: 15 June 2023 | Viewed by 5699

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Special Issue Editors

Prof. Dr. Zhaoli Wang

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Guest Editor

School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China
Interests: remote sensing of water science; hydrology; water resources; climate change; big data; AI; urban flood and control; sponge city
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yaolong Zhao

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Guest Editor

School of Geography, South China Normal University, Guangzhou 510631, China
Interests: remote sensing of water environment; urban waterlogging prevention; spatial analysis and policy simulation
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Prof. Dr. Weilin Liao

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Guest Editor

School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510641, China
Interests: application of remote sensing in water science; climate change; urban climate; AI

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAV) are two most popular techniques in many sectors including urban water science. With global climate change and human activities such as urbanization, urban climate and hydrology are changing along with raises of water quality and ecology problems. Accurate real time track and forecast of urban floods, droughts and water quality/ecology are of significance for sustainable development in urbanized areas. Compared to traditional methods/techniques, AI and UAV can provide real time and/or near real time information of urban water-related issues with higher accuracy in most cases, providing new tools for urban water management. In this special issue, we welcome papers focusing on AI and/or UAV with applications to urban water-related problems like floods and water quality as described above. Both general methodological contributions and case studies on AI and UAV covering different regions are welcome.

Prof. Dr. Zhaoli Wang
Prof. Dr. Yaolong Zhao
Prof. Dr. Weilin Liao
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 2200 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 (AI)
Unmanned Aerial Vehicles (UAV)
remote sensing
water quality
water ecology
flooding
drought
urban environment

Published Papers (5 papers)

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Research

Open AccessArticle

Retrieving Eutrophic Water in Highly Urbanized Area Coupling UAV Multispectral Data and Machine Learning Algorithms

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Water 2023, 15(2), 354; https://doi.org/10.3390/w15020354 - 14 Jan 2023

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Abstract

With the rapid development of urbanization and a population surge, the drawback of water pollution, especially eutrophication, poses a severe threat to ecosystem as well as human well-being. Timely monitoring the variations of water quality is a precedent to preventing the occurrence of eutrophication. Traditional monitoring methods (station monitoring or satellite remote sensing), however, fail to real-time obtain water quality in an accurate and economical way. In this study, an unmanned aerial vehicle (UAV) with a multispectral camera is used to acquire the refined remote sensing data of water bodies. Meanwhile, in situ measurement and sampling in-lab testing are carried out to obtain the observed values of four water quality parameters; subsequently, the comprehensive trophic level index (TLI) is calculated. Then three machine learning algorithms (i.e., Extreme Gradient Boosting (XGB), Random Forest (RF) and Artificial Neural Network (ANN)) are applied to construct the inversion model for water quality estimation. The measured values of water quality showed that the trophic status of the study area was mesotrophic or light eutrophic, which was consistent with the government’s water-control ambition. Among the four water quality parameters, TN had the highest correlation (r = 0.81, p = 0.001) with TLI, indicating that the variation in TLI was inextricably linked to TN. The performances of the three models were satisfactory, among which XGB was considered the optimal model with the best accuracy validation metrics (R² = 0.83, RMSE = 0.52). The spatial distribution map of water quality drawn by the XGB model was in good agreement with the actual situation, manifesting the spatial applicability of the XGB model inversion. The research helps guide effective monitoring and the development of timely warning for eutrophication. Full article

(This article belongs to the Special Issue Application of AI and UAV Techniques in Urban Water Science)

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Open AccessArticle

Application of Machine Learning Techniques for the Estimation of the Safety Factor in Slope Stability Analysis

Yaser Ahangari Nanehkaran

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Water 2022, 14(22), 3743; https://doi.org/10.3390/w14223743 - 18 Nov 2022

Cited by 4 | Viewed by 1089

Abstract

Slope stability is the most important stage in the stabilization process for different scale slopes, and it is dictated by the factor of safety (FS). The FS is a relationship between the geotechnical characteristics and the slope behavior under various loading conditions. Thus, the application of an accurate procedure to estimate the FS can lead to a fast and precise decision during the stabilization process. In this regard, using computational models that can be operated accurately is strongly needed. The performance of five different machine learning models to predict the slope safety factors was investigated in this study, which included multilayer perceptron (MLP), support vector machines (SVM), k-nearest neighbors (k-NN), decision tree (DT), and random forest (RF). The main objective of this article is to evaluate and optimize the various machine learning-based predictive models regarding FS calculations, which play a key role in conducting appropriate stabilization methods and stabilizing the slopes. As input to the predictive models, geo-engineering index parameters, such as slope height (H), total slope angle (β), dry density (γ_d), cohesion (c), and internal friction angle (φ), which were estimated for 70 slopes in the South Pars region (southwest of Iran), were considered to predict the FS properly. To prepare the training and testing data sets from the main database, the primary set was randomly divided and applied to all predictive models. The predicted FS results were obtained for testing (30% of the primary data set) and training (70% of the primary data set) for all MLP, SVM, k-NN, DT, and RF models. The models were verified by using a confusion matrix and errors table to conclude the accuracy evaluation indexes (i.e., accuracy, precision, recall, and f1-score), mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE). According to the results of this study, the MLP model had the highest evaluation with a precision of 0.938 and an accuracy of 0.90. In addition, the estimated error rate for the MLP model was MAE = 0.103367, MSE = 0.102566, and RMSE = 0.098470. Full article

(This article belongs to the Special Issue Application of AI and UAV Techniques in Urban Water Science)

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Open AccessArticle

Monitoring of Urban Black-Odor Water Using UAV Multispectral Data Based on Extreme Gradient Boosting

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Water 2022, 14(21), 3354; https://doi.org/10.3390/w14213354 - 22 Oct 2022

Cited by 1 | Viewed by 765

Abstract

During accelerated urbanization, the lack of attention to environmental protection and governance led to the formation of black-odor water. The existence of urban black-odor water not only affects the cityscape, but also threatens human health and damages urban ecosystems. The black-odor water bodies are small and hidden, so they require large-scale and high-resolution monitoring which offers a temporal and spatial variation of water quality frequently, and the unmanned aerial vehicle (UAV) with a multispectral instrument is up to the monitoring task. In this paper, the Nemerow comprehensive pollution index (NCPI) was introduced to assess the pollution degree of black-odor water in order to avoid inaccurate identification based on a single water parameter. Based on the UAV-borne multispectral data and NCPI of sampling points, regression models for inverting the parameter indicative of water quality were established using three artificial intelligence algorithms, namely extreme gradient boosting (XGBoost), random forest (RF), and support vector regression (SVR). The result shows that NCPI is qualified to evaluate the pollution level of black-odor water. The XGBoost regression (XGBR) model has the highest fitting accuracy on the training dataset (R² = 0.99) and test dataset (R² = 0.94), and it achieved the best retrieval effect on image inversion in the shortest time, which made it the best-fit model compared with the RF regression (RFR) model and the SVR model. According to inversion results based on the XGBR model, there was only a small size of mild black-odor water in the study area, which showed the achievement of water pollution treatment in Guangzhou. The research provides a theoretical framework and technical feasibility for the application of the combination of algorithms and UAV-borne multispectral images in the field of water quality inversion. Full article

(This article belongs to the Special Issue Application of AI and UAV Techniques in Urban Water Science)

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Open AccessFeature PaperArticle

Parameters Tuning of Fractional-Order Proportional Integral Derivative in Water Turbine Governing System Using an Effective SDO with Enhanced Fitness-Distance Balance and Adaptive Local Search

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Water 2022, 14(19), 3035; https://doi.org/10.3390/w14193035 - 27 Sep 2022

Viewed by 768

Abstract

Supply-demand-based optimization (SDO) is a swarm-based optimizer. However, it suffers from several drawbacks, such as lack of solution diversity and low convergence accuracy and search efficiency. To overcome them, an effective supply-demand-based optimization (ESDO) is proposed in this study. First, an enhanced fitness-distance balance (EFDB) and the Levy flight are introduced into the original version to avoid premature convergence and improve solution diversity; second, a mutation mechanism is integrated into the algorithm to improve search efficiency; finally, an adaptive local search strategy (ALS) is incorporated into the algorithm to enhance the convergence accuracy. The effect of the proposed method is verified based on the comparison of ESDO with several well-regarded algorithms using 23 benchmark functions. In addition, the ESDO algorithm is applied to tune the parameters of the fractional-order proportional integral derivative (FOPID) controller of the water turbine governor system. The comparative results reveal that ESDO is competitive and superior for solving real-world problems. Full article

(This article belongs to the Special Issue Application of AI and UAV Techniques in Urban Water Science)

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Open AccessArticle

Research on Water-Level Recognition Method Based on Image Processing and Convolutional Neural Networks

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Water 2022, 14(12), 1890; https://doi.org/10.3390/w14121890 - 12 Jun 2022

Cited by 2 | Viewed by 1232

Abstract

Water level dynamics in catchment-scale rivers is an important factor for surface water studies. Manual measurement is highly accurate but inefficient. Using automatic water level sensors has disadvantages such as high cost and difficult maintenance. In this study, a water level recognition method based on digital image processing technology and CNN is proposed. For achieving batch segmentation of source images, the coordinates of the water ruler region in the source image and characters’ region and the scale lines’ region on the ruler are obtained by using image processing algorithms such as grayscale processing, edge detection, and the tilt correction method based on Hough-transform and morphological operations. The CNN is then used to identify the value of digital characters. Finally, the water level value is calculated according to the mathematical relationship between the number of scale lines detected by pixel traversal in the binarized image and the value of digital characters. This method is used to identify the water levels of the water ruler images collected in the Hulu watershed of the Qilian Mountains in Northwest China. The results show that the accuracy compared with the actual measured water level reached 94.6% and improved nearly 24% compared to the template matching algorithm. With high accuracy, low cost, and easy deployment and maintenance, this method can be applied to water level monitoring in mountainous rivers, providing an effective tool for watershed hydrology research and water resources management. Full article

(This article belongs to the Special Issue Application of AI and UAV Techniques in Urban Water Science)

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