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Application of AI and UAV Techniques in Urban Water Science
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Application of AI and UAV Techniques in Urban Water Science

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 (15 June 2023) | Viewed by 22085

Special Issue Editors

Prof. Dr. Zhaoli Wang

E-Mail Website1 Website2 Website3
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

E-Mail Website1 Website2
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
Special Issues, Collections and Topics in MDPI journals
Dr. Weilin Liao

E-Mail Website
Guest Editor
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China
Interests: urban climate; climate change; remote sensing and GIS applications
Special Issues, Collections and Topics in MDPI journals

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

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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 (AI)
  • Unmanned Aerial Vehicles (UAV)
  • remote sensing
  • water quality
  • water ecology
  • flooding
  • drought
  • urban environment

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Published Papers (6 papers)

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Research

19 pages, 6862 KiB  
Article
Water Quality Sampling and Multi-Parameter Monitoring System Based on Multi-Rotor UAV Implementation
by Rihong Zhang, Zhenhao Wang, Xiaomin Li, Zipeng She and Baoe Wang
Water 2023, 15(11), 2129; https://doi.org/10.3390/w15112129 - 3 Jun 2023
Cited by 5 | Viewed by 3772
Abstract
Water quality sampling and monitoring are fundamental to water environmental protection. The purpose of this study was to develop a water quality sampling and multi-parameter monitoring system mounted on a multi-rotor unmanned aerial vehicle (UAV). The system consisted of the UAV, water sampling [...] Read more.
Water quality sampling and monitoring are fundamental to water environmental protection. The purpose of this study was to develop a water quality sampling and multi-parameter monitoring system mounted on a multi-rotor unmanned aerial vehicle (UAV). The system consisted of the UAV, water sampling and multi-parameter detection device, and path planning algorithm. The water sampling device was composed of a rotating drum, a direct current (DC) reduction motor, water suction hose, high-pressure isolation pump, sampling bottles, and microcontroller. The multi-parameter detection device consisted of sensors for potential of hydrogen (pH), turbidity, total dissolved solids (TDS), and a microcontroller. The flight path of the UAV was optimized using the proposed layered hybrid improved particle swarm optimization (LHIPSO) and rapidly-exploring random trees (RRT) obstacle avoidance path planning algorithm, in order to improve the sampling efficiency. Simulation experiments were conducted that compared the LHIPSO algorithm with the particle swarm optimization (PSO) algorithm and the dynamic adjustment (DAPSO) algorithm. The simulation results showed that the LHIPSO algorithm had improved global optimization capability and stability compared to the other algorithms, validating the effectiveness of the proposed algorithm. Field experiments were conducted at an aquaculture fish farm, and the device achieved real-time monitoring of three water quality parameters (pH, TDS, turbidity) at depths of 1 m and 2 m. A rapid analysis of three parameters (ammonia nitrogen, nitrite, dissolved oxygen) was performed in the laboratory on the collected water samples, and validated the feasibility of this study. Full article
(This article belongs to the Special Issue Application of AI and UAV Techniques in Urban Water Science)
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17 pages, 25363 KiB  
Article
Retrieving Eutrophic Water in Highly Urbanized Area Coupling UAV Multispectral Data and Machine Learning Algorithms
by Di Wu, Jie Jiang, Fangyi Wang, Yunru Luo, Xiangdong Lei, Chengguang Lai, Xushu Wu and Menghua Xu
Water 2023, 15(2), 354; https://doi.org/10.3390/w15020354 - 14 Jan 2023
Cited by 5 | Viewed by 2492
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 [...] Read more.
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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14 pages, 3130 KiB  
Article
Application of Machine Learning Techniques for the Estimation of the Safety Factor in Slope Stability Analysis
by Yaser Ahangari Nanehkaran, Tolga Pusatli, Jin Chengyong, Junde Chen, Ahmed Cemiloglu, Mohammad Azarafza and Reza Derakhshani
Water 2022, 14(22), 3743; https://doi.org/10.3390/w14223743 - 18 Nov 2022
Cited by 47 | Viewed by 5060
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, [...] Read more.
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 (γd), 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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21 pages, 4214 KiB  
Article
Monitoring of Urban Black-Odor Water Using UAV Multispectral Data Based on Extreme Gradient Boosting
by Fangyi Wang, Haiying Hu, Yunru Luo, Xiangdong Lei, Di Wu and Jie Jiang
Water 2022, 14(21), 3354; https://doi.org/10.3390/w14213354 - 22 Oct 2022
Cited by 9 | Viewed by 2303
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 [...] Read more.
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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23 pages, 4957 KiB  
Article
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
by Weiguo Zhao, Hongfei Zhang, Zhenxing Zhang, Kaidi Zhang and Liying Wang
Water 2022, 14(19), 3035; https://doi.org/10.3390/w14193035 - 27 Sep 2022
Cited by 3 | Viewed by 1879
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 [...] Read more.
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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26 pages, 10796 KiB  
Article
Research on Water-Level Recognition Method Based on Image Processing and Convolutional Neural Networks
by Gang Dou, Rensheng Chen, Chuntan Han, Zhangwen Liu and Junfeng Liu
Water 2022, 14(12), 1890; https://doi.org/10.3390/w14121890 - 12 Jun 2022
Cited by 17 | Viewed by 4903
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 [...] Read more.
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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