Artificial Intelligence Technologies Revolutionizing Wastewater Treatment: Current Trends and Future Prospective

Water, Journal Year: 2024, Volume and Issue: 16(2), P. 314 - 314

Published: Jan. 17, 2024

Integration of the Internet Things (IoT) into fields wastewater treatment and water quality prediction has potential to revolutionize traditional approaches address urgent challenges, considering global demand for clean sustainable systems. This comprehensive article explores transformative applications smart IoT technologies, including artificial intelligence (AI) machine learning (ML) models, in these areas. A successful example is implementation an IoT-based automated monitoring system that utilizes cloud computing ML methods effectively above-mentioned issues. The been employed optimize, simulate, automate various aspects, such as managing natural systems, water-treatment processes, wastewater-treatment applications, water-related agricultural practices like hydroponics aquaponics. review presents a collection significant water-based which have combined with IoT, neural networks, or undergone critical peer-reviewed assessment. These encompass chlorination, adsorption, membrane filtration, indices, modeling parameters, river levels, automating/monitoring effluent aquaculture Additionally, this provides overview discusses future along examples how their algorithms utilized evaluate treated diverse aquatic environments.

Language: Английский

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Scenario-Based Green Infrastructure Installations for Building Urban Stormwater Resilience—A Case Study of Fengxi New City, China

Sustainability, Journal Year: 2024, Volume and Issue: 16(10), P. 3990 - 3990

Published: May 10, 2024

Global climate change has precipitated a surge in urban flooding challenges, prompting the imperative role of green infrastructure (GI) as linchpin sponge city construction to enhance sustainability and resilience. But evaluation stormwater resilience faces challenges due lack comprehensive framework taking intrinsic features system into account insufficient coverage alternative scenarios’ performance under multiple rainfall return periods. This study, focusing on Fengxi New City, China, evaluates suitability GI (i.e., roofs, rain gardens, permeable pavements) constructs management model (SWMM) for hydrological simulation. study also establishes uses quantitative methods unify performances scenarios different Our analytical findings elucidate that is predominantly concentrated northern western areas area, with smallest suitable area observed pavements. Divergent GIs exhibit disparate performances, gardens emerging particularly efficacious. Importantly, combination yields synergistic enhancement resilience, underscoring strategic advantage adopting diverse integrated approach implementation. facilitates deeper understanding assists informed planning decisions cities.

Language: Английский

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A Novel Deep Learning Approach for Real-Time Critical Assessment in Smart Urban Infrastructure Systems

Electronics, Journal Year: 2024, Volume and Issue: 13(16), P. 3286 - 3286

Published: Aug. 19, 2024

The swift advancement of communication and information technologies has transformed urban infrastructures into smart cities. Traditional assessment methods face challenges in capturing the complex interdependencies temporal dynamics inherent these systems, risking resilience. This study aims to enhance criticality geographic zones within cities by introducing a novel deep learning architecture. Utilizing Convolutional Neural Networks (CNNs) for spatial feature extraction Long Short-Term Memory (LSTM) networks dependency modeling, proposed framework processes inputs such as total electricity use, flooding levels, population, poverty rates, energy consumption. CNN component constructs hierarchical maps through successive convolution pooling operations, while LSTM captures sequence-based patterns. Fully connected layers integrate features generate final predictions. Implemented Python using TensorFlow Keras on an Intel Core i7 system with 32 GB RAM NVIDIA GTX 1080 Ti GPU, model demonstrated superior performance. It achieved mean absolute error 0.042, root square 0.067, R-squared value 0.935, outperforming existing methodologies real-time adaptability resource efficiency.

Language: Английский

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Resilience Assessment in Urban Water Infrastructure: A Critical Review of Approaches, Strategies and Applications

Published: June 19, 2023

Resilient urban water infrastructure (UWI) is essential to maintaining public health and safety in areas preventing consistent disruptions. However, UWI vulnerable a wide range of shocks stresses due the complex nature interdependency its components. The primary objective this study evaluate advances resilience assessment comprising supply, stormwater, wastewater systems. This involves examining bibliometric analysis, developed frameworks understand concepts for society, strategies improving resilience, indicators. findings indicate that has primarily been conducted countries, highlighting macroeconomic importance UWI. Three major were identified analysing UWI: system design, development concepts, implementation green infrastructure. It was also found while commonly defined based on technical approaches, more thorough understanding can be obtained through holistic approach. While such as upgrade, decentralisation, digitalisation, nature-based solutions enhance UWI, they may insufficient achieve all To address issue proper comparison different options, comprehensive qualified indicators metrics should extensively examined future.

Language: Английский

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Mobile Augmented Reality Application to Evaluate the River Flooding Impact in Coimbra

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(21), P. 10017 - 10017

Published: Nov. 2, 2024

The downtown area of the city Coimbra, Portugal, is at low altitude and has historically suffered floods that have caused serious economic losses. present research proposes a mobile augmented reality (MAR) application aimed visualising effect possible scenarios flooding in an higher risk city. A realistic 3D model was created, using data extracted with BLosm processed through Blender, followed by its integration into Unity Vuforia for AR visualisation. methodology encompasses extraction simplification models, mapping real-world coordinates Unity, analysing several datasets, obtaining regression implementing workflow to manage interactions between various objects. MAR enables users visualise potential flood impacts on buildings, utilising colour-coded indicators represent different levels water contact. system’s efficacy evaluated simulating use-case scenarios, demonstrating application’s capability provide real-time, interactive assessments. results underline integrating machine learning enhancing urban management prevention.

Language: Английский

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