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Water Resource Management in Artificial Intelligence and Big Data Analytics

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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: 20 August 2024 | Viewed by 4248

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Special Issue Editor

Prof. Dr. Haixing Liu

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Guest Editor

School of Civil & Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China
Interests: artificial intelligence and water engineering applications; optimal allocation of urban water resources; disaster prevention and mitigation of urban waterlogging; optimal design and safe operation of urban water distribution networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The water resources scarcity problem is still challenging due to anthropogenic activities and climate change. Massive water infrastructures, as evidenced by reservoir and inter-basin water transfer, have been built to tackle the temporal–spatial differences of water resources across the world. However, how to operate these complex systems still remains an issue when using the traditional approaches. The innovations of information communication technologies (ICT) have helped in this regard. Monitoring, data analytics, and artificial intelligence are the promising technologies in engineering planning, design, operation, and maintenance management. Innovative technologies could lower the capital and operational cost, secure infrastructure operation, and mitigate greenhouse gas emissions. Therefore, infrastructure sustainability and efficient utilization of water resources can be realized in this study branch.

This Special Issue will explore Big data, AI applications to water supply systems, water transfer systems, and reservoirs, etc, utilizing a cross-discipline approach to ICT and water engineering. This Special Issue will provide valuable information to the readership of Water.

Prof. Dr. Haixing Liu
Guest Editor

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

smart water
monitoring
water resources
hydrological process
urban water
flooding

Published Papers (3 papers)

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Research

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23 pages, 4916 KiB

Open AccessArticle

A Novel Daily Runoff Probability Density Prediction Model Based on Simplified Minimal Gated Memory–Non-Crossing Quantile Regression and Kernel Density Estimation

by Huaiyuan Liu, Sipeng Zhu and Li Mo

Water 2023, 15(22), 3947; https://0-doi-org.brum.beds.ac.uk/10.3390/w15223947 - 13 Nov 2023

Viewed by 890

Abstract

Reliable and accurate daily runoff predictions are critical to water resource management and planning. Probability density predictions of daily runoff can provide decision-makers with comprehensive information by quantifying the uncertainty of forecasting. Models based on quantile regression (QR) have been proven to achieve good probabilistic prediction performance, but the predicted quantiles may crossover with each other, seriously reducing the reliability of the prediction. This paper proposes non-crossing quantile regression (NCQR), which guarantees that the intervals between adjacent quantiles are greater than 0, which avoids the occurrence of quantile crossing. In order to apply NCQR to the prediction of nonlinear runoff series, this paper combines NCQR with recurrent neural network (RNN) models. In order to reduce the model training time and further improve the model accuracy, this paper simplifies the minimal gated memory (MGM) model and proposes a new RNN model, called the simplified minimal gated memory (SMGM) model. Kernel density estimation (KDE) is used to transform the discrete quantiles predicted using SMGM-NCQR into a continuous probability density function (PDF). This paper proposes a novel daily density prediction model that combines SMGM-NCQR and KDE. Three daily runoff datasets in the Yangtze River Basin in China are taken as examples and compared with the advanced models in current research in terms of five aspects: point prediction evaluation, interval prediction evaluation, probability density prediction evaluation, the degree of quantile crossing and training time. The experimental results show that the model can provide high-quality and highly reliable runoff probability density predictions. Full article

(This article belongs to the Special Issue Water Resource Management in Artificial Intelligence and Big Data Analytics)

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19 pages, 19962 KiB

Open AccessArticle

Forecasting Snowmelt Season Temperatures in the Mountainous Area of Northern Xinjiang of China

by Zulian Zhang, Weiyi Mao, Mingquan Wang, Wei Zhang, Chunrong Ji, Aidaituli Mushajiang and Dawei An

Water 2023, 15(19), 3337; https://0-doi-org.brum.beds.ac.uk/10.3390/w15193337 - 22 Sep 2023

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Abstract

The mountains in northern Xinjiang of China were studied during the snowmelt season. Multi-source fusions of live data of the Chinese Land Data Assimilation System (CLDAS, 0.05° × 0.05°, hourly data) were used as real data, and the Central Meteorological Observatory guidance forecast (SCMOC, 0.05° × 0.05°, forecasting the following 10 days in 3 h intervals) was used as forecast data, both of which were issued by the China Meteorological Administration. The dynamic linear regression and the average filter correction algorithms were selected to revise the original forecast products for SCMOC. Based on the conventional temperature forecast information, we designed four temperature-rise prediction algorithms for essential factors affecting snowmelt. The temperature-rise prediction algorithms included the daily maximum temperature algorithm, daily temperature-rise-range algorithm, snowmelt temperature algorithm, and daily snowmelt duration algorithm. Four temperature-rise prediction values were calculated for each prediction product. The root–mean-squared error algorithm and temperature prediction accuracy algorithm were used to compare and test each prediction algorithm value from the time sequence and spatial distribution. Comprehensive tests showed that the forecast product revised by the average filter algorithm was superior to the revised dynamic linear regression algorithm as well as the original forecast product. Through these algorithms, the more suitable temperature-rise forecast value for each grid point in the study area could be obtained at different prediction times. The comprehensive and accurate temperature forecast value in the mountainous snowmelt season could provide an accurate theoretical basis for the effective prediction of runoff in snowmelt areas and the prevention of snowmelt flooding. Full article

(This article belongs to the Special Issue Water Resource Management in Artificial Intelligence and Big Data Analytics)

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Review

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26 pages, 1561 KiB

Open AccessReview

Building a Smart Water City: IoT Smart Water Technologies, Applications, and Future Directions

by Nwakego Joy Okoli and Boniface Kabaso

Water 2024, 16(4), 557; https://0-doi-org.brum.beds.ac.uk/10.3390/w16040557 - 12 Feb 2024

Viewed by 2167

Abstract

Water is an essential service for the sustainable development and economic competitiveness of any country. The global water demand has increased substantially due to economic development, climate change, and rising population. The Internet of Things (IoT) and Information and Communication Technologies (ICT) can help conserve available water resources. Smart cities apply IoT to boost the performance and efficiency of urban facilities. Smart cities are towns created to use IoT and ICT (innovative technologies) such as smart water applications. Several studies on smart water technology have been conducted, but there is a need to review current research that leverages the IoT as a communication technology to design effective smart water applications. This review paper is aimed at presenting evidence on the current design of smart water applications. The study also covers publication statistics to increase collaboration between stakeholders. Findings show that various technologies such as microcontrollers, embedded programming languages, sensors, communication modules, and protocols are used by researchers to accomplish their aim of designing IoT-based smart water solutions. None of the publications employed the 5G mobile networks as a communication module for their smart water application development. Findings further show that the integration of 3D printing and solar energy into IoT-based smart water applications is revolutionary and can increase the sustainability of the systems. Future directions required to ensure that developed smart water applications are widely adopted to help conserve and manage water resources are suggested. Full article

(This article belongs to the Special Issue Water Resource Management in Artificial Intelligence and Big Data Analytics)

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