Using Artificial Intelligence in Water Research

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 (20 March 2024) | Viewed by 2367

Special Issue Editor

Aquatic Ecology Research Unit, Ghent University, 9000 Gent, Belgium
Interests: environmental modelling; machine learning; data science; climate change; greenhouse gas emissions; water management; decision support tools
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is revolutionizing the field of water management by providing powerful tools and techniques for more informed, efficient, and sustainable decision-making. This promising area of research and practice involves the utilization of AI technologies, such as machine learning, data analytics, and optimization algorithms, to address the intricate challenges associated with water resource management.

AI applications in water management encompass a wide array of tasks, including predictive modeling for water quality assessment and forecasting, optimizing water distribution systems, early warning systems for flood and drought management, water resource allocation and demand forecasting, infrastructure monitoring, and decision support systems for governance. These AI-driven solutions enable stakeholders, including government agencies, water utilities, and environmental organizations, to make data-driven decisions, enhance resource efficiency, and ensure the equitable distribution of water resources.

This Special Issue aims to explore the cutting-edge applications of artificial intelligence (AI) in the field of water management.  The primary purpose of this Special Issue is to provide a comprehensive platform for researchers, practitioners, and policymakers to present and discuss their latest research findings, case studies, and innovative solutions that leverage AI to enhance decision-making processes in water management. By doing so, the Special Issue aims to advance the state of knowledge in the field and promote the adoption of AI technologies for more effective and sustainable water resource management practices.

Dr. Long Ho
Guest Editor

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • machine learning
  • data analytics
  • optimization algorithms
  • water management
  • water resource allocation
  • water quality forecasting
  • flood and drought management
  • decision support systems
  • sustainable water governance
  • remote sensing
  • internet of things (IoT)
  • water infrastructure monitoring
  • hydroinformatics

Published Papers (2 papers)

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Research

14 pages, 2589 KiB  
Article
Applying Recurrent Neural Networks and Blocked Cross-Validation to Model Conventional Drinking Water Treatment Processes
by Aleksandar Jakovljevic, Laurent Charlin and Benoit Barbeau
Water 2024, 16(7), 1042; https://doi.org/10.3390/w16071042 - 04 Apr 2024
Viewed by 751
Abstract
The jar test is the current standard method for predicting the performance of a conventional drinking water treatment (DWT) process and optimizing the coagulant dose. This test is time-consuming and requires human intervention, meaning it is infeasible for making continuous process predictions. As [...] Read more.
The jar test is the current standard method for predicting the performance of a conventional drinking water treatment (DWT) process and optimizing the coagulant dose. This test is time-consuming and requires human intervention, meaning it is infeasible for making continuous process predictions. As a potential alternative, we developed a machine learning (ML) model from historical DWT plant data that can operate continuously using real-time sensor data without human intervention for predicting clarified water turbidity 15 min in advance. We evaluated three types of models: multilayer perceptron (MLP), the long short-term memory (LSTM) recurrent neural network (RNN), and the gated recurrent unit (GRU) RNN. We also employed two training methodologies: the commonly used holdout method and the theoretically correct blocked cross-validation (BCV) method. We found that the RNN with GRU was the best model type overall and achieved a mean absolute error on an independent production set of as low as 0.044 NTU. We further found that models trained using BCV typically achieve errors equal to or lower than their counterparts trained using holdout. These results suggest that RNNs trained using BCV are superior for the development of ML models for DWT processes compared to those reported in earlier literature. Full article
(This article belongs to the Special Issue Using Artificial Intelligence in Water Research)
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28 pages, 14239 KiB  
Article
Utilizing Hybrid Machine Learning and Soft Computing Techniques for Landslide Susceptibility Mapping in a Drainage Basin
by Yimin Mao, Yican Li, Fei Teng, Arkan K. S. Sabonchi, Mohammad Azarafza and Maosheng Zhang
Water 2024, 16(3), 380; https://doi.org/10.3390/w16030380 - 24 Jan 2024
Cited by 1 | Viewed by 1209
Abstract
The hydrological system of thebasin of Lake Urmia is complex, deriving its supply from a network comprising 13 perennial rivers, along withnumerous small springs and direct precipitation onto the lake’s surface. Among these contributors, approximately half of the inflow is attributed to the [...] Read more.
The hydrological system of thebasin of Lake Urmia is complex, deriving its supply from a network comprising 13 perennial rivers, along withnumerous small springs and direct precipitation onto the lake’s surface. Among these contributors, approximately half of the inflow is attributed to the Zarrineh River and the Simineh River. Remarkably, Lake Urmia lacks a natural outlet, with its water loss occurring solely through evaporation processes. This study employed a comprehensive methodology integrating ground surveys, remote sensing analyses, and meticulous documentation of historical landslides within the basin as primary information sources. Through this investigative approach, we preciselyidentified and geolocated a total of 512 historical landslide occurrences across the Urmia Lake drainage basin, leveraging GPS technology for precision. Thisarticle introduces a suite of hybrid machine learning predictive models, such as support-vector machine (SVM), random forest (RF), decision trees (DT), logistic regression (LR), fuzzy logic (FL), and the technique for order of preference by similarity to the ideal solution (TOPSIS). These models were strategically deployed to assess landslide susceptibility within the region. The outcomes of the landslide susceptibility assessment reveal that the main high susceptible zones for landslide occurrence are concentrated in the northwestern, northern, northeastern, and some southern and southeastern areas of the region. Moreover, when considering the implementation of predictions using different algorithms, it became evident that SVM exhibited superior performance regardingboth accuracy (0.89) and precision (0.89), followed by RF, with and accuracy of 0.83 and a precision of 0.83. However, it is noteworthy that TOPSIS yielded the lowest accuracy value among the algorithms assessed. Full article
(This article belongs to the Special Issue Using Artificial Intelligence in Water Research)
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