Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review

Environmental Science and Ecotechnology, Journal Year: 2023, Volume and Issue: 19, P. 100330 - 100330

Published: Oct. 19, 2023

The recent advancements made in the realms of Artificial Intelligence (AI) and Things (AIoT) have unveiled transformative prospects opportunities to enhance optimize environmental performance efficiency smart cities. These strides have, turn, impacted eco-cities, catalyzing ongoing improvements driving solutions address complex challenges. This aligns with visionary concept smarter an emerging paradigm urbanism characterized by seamless integration advanced technologies strategies. However, there remains a significant gap thoroughly understanding this new intricate spectrum its multifaceted underlying dimensions. To bridge gap, study provides comprehensive systematic review burgeoning landscape eco-cities their leading-edge AI AIoT for sustainability. ensure thoroughness, employs unified evidence synthesis framework integrating aggregative, configurative, narrative approaches. At core lie these subsequent research inquiries: What are foundational underpinnings how do they intricately interrelate, particularly paradigms, solutions, data-driven technologies? key drivers enablers propelling materialization eco-cities? primary that can be harnessed development In what ways contribute fostering sustainability practices, potential benefits offer challenges barriers arise implementation findings significantly deepen broaden our both sustainable urban as well formidable nature pose. Beyond theoretical enrichment, invaluable insights perspectives poised empower policymakers, practitioners, researchers advance eco-urbanism AI- AIoT-driven urbanism. Through insightful exploration contemporary identification successfully applied stakeholders gain necessary groundwork making well-informed decisions, implementing effective strategies, designing policies prioritize well-being.

Language: Английский

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Explainable Artificial Intelligence for Sustainable Urban Water Systems Engineering

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104349 - 104349

Published: Feb. 1, 2025

Language: Английский

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Control of climate and physiography on runoff response behavior through use of catchment classification and machine learning

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 899, P. 166422 - 166422

Published: Aug. 19, 2023

Language: Английский

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An evaluation of random forest based input variable selection methods for one month ahead streamflow forecasting

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 30, 2024

In the development of data-driven models for streamflow forecasting, choosing appropriate input variables is crucial. Although random forest (RF) has been successfully applied to forecasting variable selection (IVS), comparative analysis different forest-based IVS (RF-IVS) methods yet absent. Here, we investigate performance five RF-IVS in four (RF, support vector regression (SVR), Gaussian process (GP), and long short-term memory (LSTM)). A case study implemented contiguous United States one-month-ahead forecasting. Results indicate that enable acquire enhanced comparison widely used partial Pearson correlation conditional mutual information. Meanwhile, performance-based appear be superior test-based methods, tend select redundant variables. The RF with a forward strategy finally recommended connect GP model as promising combination having potential yield favorable performance.

Language: Английский

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Artificial Intelligence for Flood Risk Management: A Comprehensive State-of-the-Art Review and Future Directions

International Journal of Disaster Risk Reduction, Journal Year: 2024, Volume and Issue: 117, P. 105110 - 105110

Published: Dec. 19, 2024

Language: Английский

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The dynamics of lowland river sections of Danube and Tisza in the Carpathian basin

Frontiers in Earth Science, Journal Year: 2025, Volume and Issue: 13

Published: Feb. 12, 2025

The paper presents a detailed statistical analysis of data from 41 hydrometric stations along the Danube (section in Carpathian Basin) and its longest tributary, Tisza River. Most records cover 2–3 decades with an automated high temporal sampling frequency (15 min), few span 120 years daily or half-daily records. is not even exhibits strong irregularities. demonstrates that cubic spline fits down-sampling (where necessary) produce reliable, evenly sampled time series smoothly reconstruct water level river discharge data. Almost all indicate decadal decreasing trend for annual maximum values. timing (day year) maxima minima evaluated. While minimum values do show coherent tendencies, exhibit increasing trends but (earlier onset). Various possibilities explanations these observations are listed. empirical histograms changes can be well-fitted by piecewise-exponential functions containing four three sections, consistent understanding deterministic rather than stochastic processes, as well known hydrology. Such tests serve benchmarks modeling levels discharges. Extracted periods Lomb-Scargle algorithm (suitable unevenly series) long-time means expected seasonality. Resampled (1-hour frequency) were evaluated standard Fourier Welch procedures, revealing some secondary peaks spectra indicating quasi-periodic components signals. Further significance progress, attempts at explanations. Secondary may environmental changes, future investigation which could reveal important correlations.

Language: Английский

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Foundations of Smart Water and Artificial Intelligence Technologies

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 30

Published: Feb. 18, 2025

The increasing global demand for water, compounded by the challenges posed climate change, urbanisation, and population growth, necessitates adoption of innovative solutions water management. Smart Water technologies, which encompass integration advanced sensors, data analysis, automated systems, offer a promising approach to optimising use enhancing sustainability. While remain, benefits adopting these technologies are substantial, warranting further investment research. As intensify, role systems will become increasingly critical in ensuring sustainable management this vital resource. This chapter explores components, benefits, providing comprehensive overview their modern

Language: Английский

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An Integrated Approach Using Lars-Wg and Deep Learning for River Flow Prediction in Diverse Regions

Published: Jan. 1, 2025

Language: Английский

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Harnessing artificial intelligence to address diseases attributable to unsafe drinking water: challenges, potentials, and recommendations

Discover Water, Journal Year: 2025, Volume and Issue: 5(1)

Published: March 13, 2025

Language: Английский

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Artificial Intelligence in Climate-Resilient Water Management: A Systematic Review of Applications, Challenges, and Future Directions

Water Conservation Science and Engineering, Journal Year: 2025, Volume and Issue: 10(1)

Published: April 1, 2025

Language: Английский

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