Artificial Intelligence in Hydrology: Advancements in Soil, Water Resource Management, and Sustainable Development DOI Open Access
Seyed Mostafa Biazar, Golmar Golmohammadi,

Rohit R. Nedhunuri

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(5), P. 2250 - 2250

Published: March 5, 2025

Hydrology relates to many complex challenges due climate variability, limited resources, and especially, increased demands on sustainable management of water soil. Conventional approaches often cannot respond the integrated complexity continuous change inherent in system; hence, researchers have explored advanced data-driven solutions. This review paper revisits how artificial intelligence (AI) is dramatically changing most important facets hydrological research, including soil land surface modeling, streamflow, groundwater forecasting, quality assessment, remote sensing applications resources. In AI techniques could further enhance accuracy texture analysis, moisture estimation, erosion prediction for better management. Advanced models also be used as a tool forecast streamflow levels, therefore providing valuable lead times flood preparedness resource planning transboundary basins. quality, AI-driven methods improve contamination risk enable detection anomalies, track pollutants assist treatment processes regulatory practices. combined with open new perspectives monitoring resources at spatial scale, from forecasting storage variations. paper’s synthesis emphasizes AI’s immense potential hydrology; it covers latest advances future prospects field ensure

Language: Английский

A review of hybrid deep learning applications for streamflow forecasting DOI
Kin‐Wang Ng, Yuk Feng Huang, Chai Hoon Koo

et al.

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 625, P. 130141 - 130141

Published: Sept. 12, 2023

Language: Английский

Citations

81
Advances in machine learning and IoT for water quality monitoring: A comprehensive review DOI Creative Commons
Ismail Essamlali, Hasna Nhaila, Mohamed El Khaïli

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(6), P. e27920 - e27920

Published: March 1, 2024

Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality ensure usability. The advent of the. Internet Things (IoT) has brought about revolutionary shift by enabling real-time data collection from diverse sources, thereby facilitating efficient monitoring water (WQ). By employing Machine learning (ML) techniques, this gathered can be analyzed make accurate predictions regarding quality. These predictive insights play crucial role decision-making processes aimed at safeguarding quality, such identifying areas need immediate attention and implementing preventive measures avert contamination. This paper aims provide comprehensive review current state art monitoring, with specific focus on employment IoT wireless technologies ML techniques. study examines utilization range technologies, including Low-Power Wide Area Networks (LpWAN), Wi-Fi, Zigbee, Radio Frequency Identification (RFID), cellular networks, Bluetooth, context Furthermore, it explores application both supervised unsupervised algorithms for analyzing interpreting collected data. In addition discussing art, survey also addresses challenges open research questions involved integrating (WQM).

Language: Английский

Citations

33
Navigating the molecular landscape of environmental science and heavy metal removal: A simulation-based approach DOI Creative Commons

Iman Salahshoori,

Marcos A.L. Nobre, Amirhosein Yazdanbakhsh

et al.

Journal of Molecular Liquids, Journal Year: 2024, Volume and Issue: 410, P. 125592 - 125592

Published: July 20, 2024

Heavy metals pose a significant threat to ecosystems and human health because of their toxic properties ability bioaccumulate in living organisms. Traditional removal methods often fall short terms cost, energy efficiency, minimizing secondary pollutant generation, especially complex environmental settings. In contrast, molecular simulation offer promising solution by providing in-depth insights into atomic interactions between heavy potential adsorbents. This review highlights the for removing types pollutants science, specifically metals. These powerful tool predicting designing materials processes remediation. We focus on specific like lead, Cadmium, mercury, utilizing cutting-edge techniques such as Molecular Dynamics (MD), Monte Carlo (MC) simulations, Quantum Chemical Calculations (QCC), Artificial Intelligence (AI). By leveraging these methods, we aim develop highly efficient selective unravelling underlying mechanisms, pave way developing more technologies. comprehensive addresses critical gap scientific literature, valuable researchers protection health. modelling hold promise revolutionizing prediction metals, ultimately contributing sustainable solutions cleaner healthier future.

Language: Английский

Citations

18
A data-driven model for water quality prediction in Tai Lake, China, using secondary modal decomposition with multidimensional external features DOI Creative Commons
Rui Tan, Zhaocai Wang, Tunhua Wu

et al.

Journal of Hydrology Regional Studies, Journal Year: 2023, Volume and Issue: 47, P. 101435 - 101435

Published: May 30, 2023

Tai Lake, the third largest freshwater lake in China, with a history of serious ecological pollution incidents. Lake water quality prediction techniques are essential to ensure an early emergency response capability for sustainable management. Herein, effective data-driven ensemble model was developed predicting dissolved oxygen (DO) based on meteorological factors, indicators and spatial information. First, variation mode decomposition (VMD) used decompose data into multiple modal components classify them feature terms self terms. The were combined relevant external features multivariate by convolutional neural network (CNN) bi-directional long short-term memory (BiLSTM) attention mechanism (AT), as well using whale optimization algorithm (WOA) optimize hyperparameters. form secondary model. Finally, groupings linearly summed obtain outcome. proposed has highest accuracy best effect 0.5 days period. This research also establishes stepwise temperature regulation mechanism, where output target DO content value is achieved changing magnitude combining it this model, thereby strengthening protection resources management fishery production.

Language: Английский

Citations

29
The role of industry 4.0 in advancing sustainability development: A focus review in the United Arab Emirates DOI Creative Commons
Abrar Alhammadi, Imad Alsyouf, Concetta Semeraro

et al.

Cleaner Engineering and Technology, Journal Year: 2023, Volume and Issue: 18, P. 100708 - 100708

Published: Dec. 7, 2023

In recent years, the widespread adoption of Industry 4.0 technologies has significantly enhanced sustainability performance and addressed environmental concerns for companies. These advanced are crucial in driving reshaping organizational processes. This study investigates relationship between Sustainable Development Goals (SDGs), specifically focusing on United Arab Emirates (UAE). The research is considered significant novel because its potential to address challenges, foster sustainable economic growth, guide policy decisions, promote innovation, bridge knowledge gaps field. A systematic literature review was conducted utilizing Scopus database achieve these objectives. process involved defining relevant selection criteria such as SDG1 SDG17 Big Data Internet Things (IoT), reviewing 138 articles, employing appropriate analysis methods, including bibliometric concatenate function. Various visualizations were used present outcomes effectively. categorized SDGs into three levels: 17 Goals, 169 Targets, 241 Indicators, 6 Design Principles, 9 Technologies, 37 Enablers. By analyzing publications, identified most least utilized UAE context. It discovered that addressing UAE's food security aligns with SDG 14: Life Below Water, given country's abundant natural resources. highlights key areas concern emphasizes interconnection technologies, offering valuable insights future investigations. Moreover, findings benefit researchers interested exploring specific applications within scope study. aids understanding leveraging role SDGs.

Language: Английский

Citations

27
Progress and opportunities in advancing near‐term forecasting of freshwater quality DOI Creative Commons
Mary E. Lofton, Dexter W. Howard, R. Quinn Thomas

et al.

Global Change Biology, Journal Year: 2023, Volume and Issue: 29(7), P. 1691 - 1714

Published: Jan. 9, 2023

Abstract Near‐term freshwater forecasts, defined as sub‐daily to decadal future predictions of a variable with quantified uncertainty, are urgently needed improve water quality management ecosystems exhibit greater variability due global change. Shifting baselines in land use and climate change prevent managers from relying on historical averages for predicting conditions, necessitating near‐term forecasts mitigate risks human health safety (e.g., flash floods, harmful algal blooms) ecosystem services water‐related recreation tourism). To assess the current state forecasting identify opportunities progress, we synthesized papers published past 5 years. We found that is currently dominated by quantity fewer number early stages development (i.e., non‐operational) despite their potential important preemptive decision support tools. contend more critically poised make substantial advances based examples recent progress methodology, workflows, end‐user engagement. For example, systems can predict temperature, dissolved oxygen, bloom/toxin events days ahead reasonable accuracy. Continued will be greatly accelerated adapting tools approaches machine learning modeling methods). In addition, effective operational require substantive engagement end users throughout forecast process, funding, training opportunities. Looking ahead, provides hopeful face increased risk change, encourage scientific community incorporate research management.

Language: Английский

Citations

26
Integration of deep learning and improved multi-objective algorithm to optimize reservoir operation for balancing human and downstream ecological needs DOI

Rujian Qiu,

Dong Wang, Vijay P. Singh

et al.

Water Research, Journal Year: 2024, Volume and Issue: 253, P. 121314 - 121314

Published: Feb. 14, 2024

Language: Английский

Citations

12
In-depth simulation of rainfall–runoff relationships using machine learning methods DOI Creative Commons
Mehdi Fuladipanah,

Alireza Shahhosseini,

Namal Rathnayake

et al.

Water Practice & Technology, Journal Year: 2024, Volume and Issue: 19(6), P. 2442 - 2459

Published: June 1, 2024

ABSTRACT Measurement inaccuracies and the absence of precise parameters value in conceptual analytical models pose challenges simulating rainfall–runoff modeling (RRM). Accurate prediction water resources, especially scarcity conditions, plays a distinctive pivotal role decision-making within resource management. The significance machine learning (MLMs) has become pronounced addressing these issues. In this context, forthcoming research endeavors to model RRM utilizing four MLMs: Support Vector Machine, Gene Expression Programming (GEP), Multilayer Perceptron, Multivariate Adaptive Regression Splines (MARS). simulation was conducted Malwathu Oya watershed, employing dataset comprising 4,765 daily observations spanning from July 18, 2005, September 30, 2018, gathered rainfall stations, Kappachichiya hydrometric station. Of all input combinations, incorporating Qt−1, Qt−2, R̄t identified as optimal configuration among considered alternatives. models' performance assessed through root mean square error (RMSE), average (MAE), coefficient determination (R2), developed discrepancy ratio (DDR). GEP emerged superior choice, with corresponding index values (RMSE, MAE, R2, DDRmax) (43.028, 9.991, 0.909, 0.736) during training process (40.561, 10.565, 0.832, 1.038) testing process.

Language: Английский

Citations

11
River discharge prediction based multivariate climatological variables using hybridized long short-term memory with nature inspired algorithm DOI
Sandeep Samantaray, Abinash Sahoo, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132453 - 132453

Published: Dec. 1, 2024

Language: Английский

Citations

9
A Hybrid Improved Dual-Channel and Dual-Attention Mechanism Model for Water Quality Prediction in Nearshore Aquaculture DOI Open Access
Wenjing Liu, Ji Wang,

Zhenhua Li

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(2), P. 331 - 331

Published: Jan. 15, 2025

The aquatic environment in aquaculture serves as the foundation for survival and growth of animals, while a high-quality water is necessary condition promoting efficient healthy development. To effectively guide early warnings regulation quality aquaculture, this study proposes predictive model based on dual-channel dual-attention mechanism, namely, DAM-ResNet-LSTM model. This encompasses two parallel feature extraction channels: residual network (ResNet) long short-term memory (LSTM), with mechanisms integrated into each channel to enhance model’s representation capabilities. Then, proposed trained, validated, tested using meteorological parameter data collected by an offshore farm environmental monitoring system. results demonstrate that structure mechanism can significantly improve performance prediction accuracy pH, dissolved oxygen (DO), salinity (SAL) (with Nash coefficients 0.9361, 0.9396, 0.9342, respectively) higher than chemical demand (COD), ammonia nitrogen (NH3-N), nitrite (NO2−), active phosphate (AP) 0.8578, 0.8542, 0.8372, 0.8294, respectively). Compared single-channel DA-ResNet (ResNet mechanism), predicting DO, SAL, COD, NH3-N, NO2−, AP increase 12.76%, 12.58%, 11.68%, 18.350%, 19.32%, 16%, 14.99%, respectively. DA-LSTM (LSTM corresponding increases are 9.15%, 9.93%, 9.11%, 10.91%, 10.11%, 10.39%, 10.2%, ResNet-LSTM LSTM parallel) without attention improvements 1.91%, 2.4%, 0.74%, 3.41%, 2.71%, 3.55%, 4.13%, fulfills practical requirements accurate forecasting nearshore aquaculture.

Language: Английский

Citations

1