Special Issue "Using Artificial Intelligence for Smart Water Management"

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 (7 October 2022) | Viewed by 6677

Special Issue Editors

Prof. Dr. Jianjun Ni
E-Mail Website
Guest Editor
College of IOT Engineering, Hohai University, Changzhou, China
Interests: artificial intelligence; smart water; internet of things; machine learning; automation, robotics
Prof. Dr. Zhenxiang Xing
E-Mail Website
Guest Editor
School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin, China
Interests: hydrological simulation and water resources analysis

Special Issue Information

Dear Colleagues,

With the rapid development of social economy and the wide application of information technology, the construction of intelligent water resources has become an effective carrier for many countries to improve the scientific management of water resources. The basic idea of smart water management is to improve the effectiveness and efficiency of flood monitoring, water environment monitoring and management, water resources management and allocation, water supply and drainage pipe network monitoring and other related aspects, using artificial intelligence technology and other new information technologies. Enhancing the ability of informationization and intelligence is one of the key directions of water management now and in the future. This Special Issue is focused on providing state-of-the-art understanding and applications of artificial intelligence theory and new information technologies for water management.

Prof. Dr. Jianjun Ni
Prof. Dr. Zhenxiang Xing
Guest Editors

Manuscript Submission Information

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Keywords

  • intelligent models about water management
  • automated monitoring technology
  • big data on water management
  • network technology for water management
  • flood forecasting
  • water environment monitoring
  • monitoring of water supply and drainage network
  • deep learning technology of smart water

Published Papers (6 papers)

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Research

Article
Comparison of Two Convergence Criterion in the Optimization Process Using a Recursive Method in a Multi-Reservoir System
Water 2022, 14(19), 2952; https://doi.org/10.3390/w14192952 - 21 Sep 2022
Viewed by 530
Abstract
Stochastic dynamic programming (SDP) is an optimization technique used in the operation of reservoirs for many years. However, being an iterative method requiring considerable computational time, it is important to establish adequate convergence criterion for its most effective use. Based on two previous [...] Read more.
Stochastic dynamic programming (SDP) is an optimization technique used in the operation of reservoirs for many years. However, being an iterative method requiring considerable computational time, it is important to establish adequate convergence criterion for its most effective use. Based on two previous studies for the optimization of operations in one of the most important multi-reservoir systems in Mexico, this work uses SDP, centred on the interest in the convergence criterion used in the optimization process. In the first trial, following the recommendations in the literature consulted, the difference in the absolute value of two consecutive iterations was taken and compared against a set tolerance value and a discount factor. In the second trial, it was decided to take the squared difference of the two consecutive iterations. In each of the trials, the computational time taken to obtain the optimal operating policy was quantified, along with whether the optimal operating policy was obtained by meeting the convergence criterion or by reaching the maximum number of iterations. With each optimization policy, the operation of the system under study was simulated and four variables were taken as evaluators of the system behaviour. The results showed few differences in the two operation policies but notable differences in the computation time used in the optimization process, as well as in the fulfilment of the convergence criterion. Full article
(This article belongs to the Special Issue Using Artificial Intelligence for Smart Water Management)
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Article
Development of a Revised Multi-Layer Perceptron Model for Dam Inflow Prediction
Water 2022, 14(12), 1878; https://doi.org/10.3390/w14121878 - 10 Jun 2022
Cited by 2 | Viewed by 822
Abstract
It is necessary to predict dam inflow in advance for flood prevention and stable dam operations. Although predictive models using deep learning are increasingly studied, these existing studies have merely applied the models or adapted the model structure. In this study, data preprocessing [...] Read more.
It is necessary to predict dam inflow in advance for flood prevention and stable dam operations. Although predictive models using deep learning are increasingly studied, these existing studies have merely applied the models or adapted the model structure. In this study, data preprocessing and machine learning algorithms were improved to increase the accuracy of the predictive model. Data preprocessing was divided into two types: The learning method, which distinguishes between peak and off seasons, and the data normalization method. To search for a global solution, the model algorithm was improved by adding a random search algorithm to the gradient descent of the Multi-Layer Perceptron (MLP) method. This revised model was applied to the Soyang Dam Basin in South Korea, and deep learning-based discharge prediction was performed using historical data from 2004 to 2021. Data preprocessing improved the accuracy by up to 61.5%, and the revised model improved the accuracy by up to 40.3%. With the improved algorithm, the accuracy of dam inflow predictions increased to 89.4%. Based on these results, stable dam operation is possible through more accurate inflow predictions. Full article
(This article belongs to the Special Issue Using Artificial Intelligence for Smart Water Management)
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Article
An Improved Transfer Learning Model for Cyanobacterial Bloom Concentration Prediction
Water 2022, 14(8), 1300; https://doi.org/10.3390/w14081300 - 16 Apr 2022
Viewed by 836
Abstract
The outbreak of cyanobacterial blooms is a serious water environmental problem, and the harm it brings to aquatic ecosystems and water supply systems cannot be underestimated. It is very important to establish an accurate prediction model of cyanobacterial bloom concentration, which is a [...] Read more.
The outbreak of cyanobacterial blooms is a serious water environmental problem, and the harm it brings to aquatic ecosystems and water supply systems cannot be underestimated. It is very important to establish an accurate prediction model of cyanobacterial bloom concentration, which is a challenging issue. Machine learning techniques can improve the prediction accuracy, but a large amount of historical monitoring data is needed to train these models. For some waters with an inconvenient geographical location or frequent sensor failures, there are not enough historical data to train the model. To deal with this problem, a fused model based on a transfer learning method is proposed in this paper. In this study, the data of water environment with a large amount of historical monitoring data are taken as the source domain in order to learn the knowledge of cyanobacterial bloom growth characteristics and train the prediction model. The data of the water environment with a small amount of historical monitoring data are taken as the target domain in order to load the model trained in the source domain. Then, the training set of the target domain is used to participate in the inter-layer fine-tuning training of the model to obtain the transfer learning model. At last, the transfer learning model is fused with a convolutional neural network to obtain the prediction model. Various experiments are conducted for a 2 h prediction on the test set of the target domain. The results show that the proposed model can significantly improve the prediction accuracy of cyanobacterial blooms for the water environment with a low data volume. Full article
(This article belongs to the Special Issue Using Artificial Intelligence for Smart Water Management)
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Article
Flood Season Staging and Adjustment of Limited Water Level for a Multi-Purpose Reservoir
Water 2022, 14(5), 775; https://doi.org/10.3390/w14050775 - 28 Feb 2022
Cited by 1 | Viewed by 1070
Abstract
A reasonable flood season delineation can effectively implement staged reservoir scheduling and improve water resource efficiency. Therefore, this study is aimed at analyzing the flood period segmentation and optimizing the staged flood limit water levels (FLWLs) for a multi-purpose reservoir, the Longtan Reservoir, [...] Read more.
A reasonable flood season delineation can effectively implement staged reservoir scheduling and improve water resource efficiency. Therefore, this study is aimed at analyzing the flood period segmentation and optimizing the staged flood limit water levels (FLWLs) for a multi-purpose reservoir, the Longtan Reservoir, China. The rainfall seasonality index (SIP) and the runoff seasonality index (SIR) are used to evaluate the feasibility and rationality of the flood period staging. The fractal method is then used to segment the flood season. Finally, the design flood is carried out to optimize the staged FLWLs. The results show that the SI is an effective indicator for judging the feasibility and verifying the rationality of flood segmentation. The flood period can be segmented into the pre-flood season (12 April–29 May), the main flood season (30 May–3 September), and the post-flood season (4 September–9 November). The FLWLs in the main flood and the post-flood season can be raised by 2.05 m and 3.45 m, and the effective reservoir capacity is increased by 5.810 billion m3 and 6.337 billion m3, according to the results of the flood season division. Full article
(This article belongs to the Special Issue Using Artificial Intelligence for Smart Water Management)
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Article
Study on the Optimal Operation of a Hydropower Plant Group Based on the Stochastic Dynamic Programming with Consideration for Runoff Uncertainty
Water 2022, 14(2), 220; https://doi.org/10.3390/w14020220 - 12 Jan 2022
Cited by 1 | Viewed by 648
Abstract
Hydropower plant operation reorganizes the temporal and spatial distribution of water resources to promote the comprehensive utilization of water resources in the basin. However, a lot of uncertainties were brought to light concerning cascade hydropower plant operation with the introduction of the stochastic [...] Read more.
Hydropower plant operation reorganizes the temporal and spatial distribution of water resources to promote the comprehensive utilization of water resources in the basin. However, a lot of uncertainties were brought to light concerning cascade hydropower plant operation with the introduction of the stochastic process of incoming runoff. Therefore, it is of guiding significance for the practice operation to investigate the stochastic operation of cascade hydropower plants while considering runoff uncertainty. The runoff simulation model was constructed by taking the cascade hydropower plants in the lower reaches of the Lancang River as the research object, and combining their data with the copula joint function and Gibbs method, and a Markov chain was adopted to construct the transfer matrix of runoff between adjacent months. With consideration for the uncertainty of inflow runoff, the stochastic optimal operation model of cascade hydropower plants was constructed and solved by the SDP algorithm. The results showed that 71.12% of the simulated monthly inflow of 5000 groups in the Nuozhadu hydropower plant drop into the reasonable range. Due to the insufficiency of measured runoff, there were too many 0 values in the derived transfer probability, but after the simulated runoff series were introduced, the results significantly improved. Taking the transfer probability matrix of simulated runoff as the input of the stochastic optimal operation model of the cascade hydropower plants, the operation diagram containing the future-period incoming water information was obtained, which could directly provide a reference for the optimal operation of the Nuozhadu hydropower plant. In addition, taking the incoming runoff process in a normal year as the standard, the annual mean power generation based on stochastic dynamic programming was similar to that based on dynamic programming (respectively 305.97 × 108 kWh and 306.91 × 108 kWh), which proved that the operation diagram constructed in this study was reasonable. Full article
(This article belongs to the Special Issue Using Artificial Intelligence for Smart Water Management)
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Article
Underwater Biological Detection Algorithm Based on Improved Faster-RCNN
Water 2021, 13(17), 2420; https://doi.org/10.3390/w13172420 - 03 Sep 2021
Cited by 3 | Viewed by 1574
Abstract
Underwater organisms are an important part of the underwater ecological environment. More and more attention has been paid to the perception of underwater ecological environment by intelligent means, such as machine vision. However, many objective reasons affect the accuracy of underwater biological detection, [...] Read more.
Underwater organisms are an important part of the underwater ecological environment. More and more attention has been paid to the perception of underwater ecological environment by intelligent means, such as machine vision. However, many objective reasons affect the accuracy of underwater biological detection, such as the low-quality image, different sizes or shapes, and overlapping or occlusion of underwater organisms. Therefore, this paper proposes an underwater biological detection algorithm based on improved Faster-RCNN. Firstly, the ResNet is used as the backbone feature extraction network of Faster-RCNN. Then, BiFPN (Bidirectional Feature Pyramid Network) is used to build a ResNet–BiFPN structure which can improve the capability of feature extraction and multi-scale feature fusion. Additionally, EIoU (Effective IoU) is used to replace IoU to reduce the proportion of redundant bounding boxes in the training data. Moreover, K-means++ clustering is used to generate more suitable anchor boxes to improve detection accuracy. Finally, the experimental results show that the detection accuracy of underwater biological detection algorithm based on improved Faster-RCNN on URPC2018 dataset is improved to 88.94%, which is 8.26% higher than Faster-RCNN. The results fully prove the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue Using Artificial Intelligence for Smart Water Management)
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