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Looking into the Future of Smart Water Management through Artificial Intelligence

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 (28 February 2023) | Viewed by 8323

Special Issue Editors

Dr. Padam Jee Omar

E-Mail Website
Guest Editor
Department of Civil Engineering, Indian Institute of Technology (Banaras Hindu University, Varanasi), Uttar Pradesh 221005, India
Interests: water; remote sensing; GIS; groundwater modeling; water resource engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Ganesh Ji Omar

E-Mail Website
Guest Editor
Department of Physics, Faculty of Science, National University of Singapore, #11-01, T Lab Building, 5 A Engineering Drive 1, Singapore 117411, Singapore
Interests: condensed matter physics; oxide electronics; spintronics
Special Issues, Collections and Topics in MDPI journals
Dr. Pankaj Kumar Gupta

E-Mail Website1 Website2
Guest Editor
Wetland Hydrology Research Laboratory, Faculty of Environment, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Interests: soil and groundwater pollution; fate and transport of napls; multiphase flow; remediation, restoration, and management of polluted sites; soil microbiome; wetland hydrology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human habitation has always depended on a reliable supply of safe drinking water. We are always seeking solutions that will assure reliable access to clean drinking water as the world population grows and the security of our freshwater supplies deteriorates. Artificial Intelligence (AI) in cleantech has a lot of potential for water quality management. This special issue is to publish contributions on AI methodologies for Smart Water Management Systems.

The AI-based Smart Water Management System explains how to employ cutting-edge Information and communication technologies to practice the tapping water-saving habit at home. The AI system will monitor and manage water flow from the above tank to multiple service areas such as the cooking area, washroom, clothes washing, and gardening in the residence. Water is polluted with effluent or other corrosive pollutants in many regions, seeking to make treatment of wastewater a critical component for a sustainable future. AI systems can predict the configuration of pollutants from water and improve the water recovery plant's quality. The groundwater quality can be monitored regularly, and data on the purity can be obtained using computer vision and big data. Water managers and government entities can utilize AI to create a smart water system that can provide efficient water facilities and adapt to changing conditions. These technologies will be cost-effective and long-lasting, allowing for optimizing all water management solutions and predicting possible damage. The provision of detectors in residence, connected to devices or smartphones and machines via a Wi-Fi system, allows massive data collection. Which can be used for both advantage of the individual consumer and, more importantly, for the benefit of the technical director, enabling responsible consumption administration, plant and channel upkeep, and operating parameters in various time bands.

Water resource management is also critical for maintaining biodiversity, characterized by a high level of complexity, as evidenced by many species of plants and animals and the environmental factors that our region records. Natural and man-made risks, on the other hand, affect ecosystems at various scales and complexities, creating modifications and shifts in their stability and lowering their effectiveness and adaptability. This must be examined to assist the next generation economic system and government entities in resolving the water demand crisis. One of the important goals of water management either now or in the future is to improve information and intelligence capabilities. This Special Issue seeks contributions that provide cutting-edge knowledge and uses of artificial intelligence and other computing infrastructure for smart water management.

Dr. Padam Jee Omar
Dr. Ganesh Ji Omar
Dr. Pankaj Kumar Gupta
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

  • AI
  • ML
  • big data analytics
  • water quality
  • smart water management

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Published Papers (3 papers)

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Research

11 pages, 2874 KiB  
Article
IWQP4Net: An Efficient Convolution Neural Network for Irrigation Water Quality Prediction
by Ibrahim Al-Shourbaji and Salahaldeen Duraibi
Water 2023, 15(9), 1657; https://doi.org/10.3390/w15091657 - 24 Apr 2023
Cited by 6 | Viewed by 1909
Abstract
With the increasing worldwide population and the requirement for efficient approaches to farm care and irrigation, the demand for water is constantly rising, and water resources are becoming scarce. This has led to the development of smart water management systems that aim to [...] Read more.
With the increasing worldwide population and the requirement for efficient approaches to farm care and irrigation, the demand for water is constantly rising, and water resources are becoming scarce. This has led to the development of smart water management systems that aim to improve the efficiency of water management. This paper pioneers an effective Irrigation Water Quality Prediction (IWQP) model using a convolution neural architecture that can be trained on any general computing device. The developed IWQP4Net is assessed using several evaluation measurements and compared to the Logistic Regression (LR), Support Vector regression (SVR), and k-Nearest Neighbor (kNN) models. The results show that the developed IWQP4Net achieved a promising outcome and better performance than the other comparative models. Full article
(This article belongs to the Special Issue Looking into the Future of Smart Water Management through Artificial Intelligence)
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15 pages, 2382 KiB  
Article
A Hydrological Data Prediction Model Based on LSTM with Attention Mechanism
by Zhihui Dai, Ming Zhang, Nadia Nedjah, Dong Xu and Feng Ye
Water 2023, 15(4), 670; https://doi.org/10.3390/w15040670 - 8 Feb 2023
Cited by 14 | Viewed by 3564
Abstract
With the rapid development of IoT, big data and artificial intelligence, the research and application of data-driven hydrological models are increasing. However, when conducting time series analysis, many prediction models are often directly based on the following assumptions: hydrologic time series are normal, [...] Read more.
With the rapid development of IoT, big data and artificial intelligence, the research and application of data-driven hydrological models are increasing. However, when conducting time series analysis, many prediction models are often directly based on the following assumptions: hydrologic time series are normal, homogeneous, smooth and non-trending, which are not always all true. To address the related issues, a solution for short-term hydrological forecasting is proposed. Firstly, a feature test is conducted to verify whether the hydrological time series are normal, homogeneous, smooth and non-trending; secondly, a sequence-to-sequence (seq2seq)-based short-term water level prediction model (LSTM-seq2seq) is proposed to improve the accuracy of hydrological prediction. The model uses a long short-term memory neural network (LSTM) as an encoding layer to encode the historical flow sequence into a context vector, and another LSTM as a decoding layer to decode the context vector in order to predict the target runoff, by superimposing on the attention mechanism, aiming at improving the prediction accuracy. Using the experimental data regarding the water level of the Chu River, the model is compared to other models based on the analysis of normality, smoothness, homogeneity and trending of different water level data. The results show that the prediction accuracy of the proposed model is greater than that of the data set without these characteristics for the data set with normality, smoothness, homogeneity and trend. Flow data at Runcheng, Wuzhi, Baima Temple, Longmen Town, Dongwan, Lu’s and Tongguan are used as input data sets to train and evaluate the model. Metrics RMSE and NSE are used to evaluate the prediction accuracy and convergence speed of the model. The results show that the prediction accuracy of LSTM-seq2seq and LSTM-BP models is higher than other models. Furthermore, the convergence process of the LSTM-seq2seq model is the fastest among the compared models. Full article
(This article belongs to the Special Issue Looking into the Future of Smart Water Management through Artificial Intelligence)
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15 pages, 5701 KiB  
Article
Experimental Study on Microchannel with Addition of Microinserts Aiming Heat Transfer Performance Improvement
by Shailesh Ranjan Kumar and Satyendra Singh
Water 2022, 14(20), 3291; https://doi.org/10.3390/w14203291 - 18 Oct 2022
Cited by 2 | Viewed by 1913
Abstract
Microchannel technology rapidly established itself as a practicable solution to the problem of the removal of extremely concentrated heat generation in present-day cooling fields. By implementing a better design structure, altering the working fluids and flow conditions, using various materials for fabrication, etc., [...] Read more.
Microchannel technology rapidly established itself as a practicable solution to the problem of the removal of extremely concentrated heat generation in present-day cooling fields. By implementing a better design structure, altering the working fluids and flow conditions, using various materials for fabrication, etc., it is possible to increase the heat transfer performance of microchannels. Two parameters that affect how well a microchannel transfers heat were only recently coupled, and the complicated coupling of the parameter that affects how well a microchannel sink transfers heat is still not well understood. The newest industrial developments, such as micro-electro-mechanical systems, high performance computing systems, high heat density generating future devices, such as 5G/6G devices, fuel cell power plants, etc., all present thermal challenges that require the use of microchannel technology. In this paper, single-phase flow in microchannels of various sizes, with or without microinserts, is described in terms of its thermal-fluid flow properties, including fluid flow characteristics and heat transfer characteristics considering the compound effects of variations of channel size and addition of microinserts. The trials were carried out using distilled water that had thermo-physical characteristics that varied with temperature. A microchannel with microinserts was developed for managing the high heat generation density equipment. The fluid flow and heat transfer characteristics are explored and analyzed for Reynolds numbers ranges from 125 to 4992, for 1 mm channel size, and from 250 to 9985, for 2 mm channel size. The cooling performance criteria are pressure drop characteristics, heat transfer characteristics, and overall performance, whereas the testing parameters were chosen for the variations in channel size and the addition of microinserts. The influence of inserting microinserts on microchannels is discussed. Results suggest that by inserting microinserts, the performance of the heat transfer of microchannels is significantly improved and, also, fluid flow resistance is increased. The criteria of the thermal performance factor are employed to assess the overall performance of the microchannel. Significant intensification of heat transfer is observed with indication that the addition of microinserts to microchannels and reduction in channel sizes exhibited improved overall performance. Full article
(This article belongs to the Special Issue Looking into the Future of Smart Water Management through Artificial Intelligence)
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