Published: Jan. 1, 2024
Language: Английский
World Journal of Advanced Research and Reviews, Journal Year: 2024, Volume and Issue: 21(1), P. 1373 - 1382
Published: Jan. 19, 2024
Integrating the Internet of Things (IoT) and Artificial Intelligence (AI) in smart water management revolutionizes sustainable resource utilization. This comprehensive review explores these technologies' benefits, challenges, regulatory implications, future trends. Smart enhances operational efficiency, predictive maintenance, conservation while addressing data security infrastructure investment challenges. Regulatory frameworks play a pivotal role shaping responsible deployment AI IoT, ensuring privacy ethical use. Future trends include advanced sensors, decentralized systems, quantum computing, blockchain for enhanced security. The alignment with Sustainable Development Goals (SDGs) underscores transformative potential achieving universal access to clean water, climate resilience, inclusive, development. As we embrace technologies, collaboration, public awareness, considerations will guide evolution intelligent equitable systems.
Language: Английский
Citations
30
Remote Sensing, Journal Year: 2025, Volume and Issue: 17(6), P. 1089 - 1089
Published: March 20, 2025
Wetlands in the Yellow River Watershed of Inner Mongolia face significant reductions under future climate and land use scenarios, threatening vital ecosystem services water security. This study employs high-resolution projections from NASA’s Global Daily Downscaled Projections (GDDP) Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC AR6), combined with a machine learning Cellular Automata–Markov (CA–Markov) framework to forecast cover transitions 2040. Statistically downscaled temperature precipitation data for two Shared Socioeconomic Pathways (SSP2-4.5 SSP5-8.5) are integrated satellite-based (Landsat, Sentinel-1) 2007 2023, achieving high classification accuracy (over 85% overall, Kappa > 0.8). A Maximum Entropy (MaxEnt) analysis indicates that rising temperatures, increased variability, urban–agricultural expansion will exacerbate hydrological stress, driving substantial wetland contraction. Although certain areas may retain or slightly expand their wetlands, dominant trend underscores urgency spatially targeted conservation. By synthesizing data, multi-temporal transitions, ecological modeling, this provides insights adaptive resource planning management ecologically sensitive regions.
Language: Английский
Citations
3
Water, Journal Year: 2023, Volume and Issue: 15(22), P. 3965 - 3965
Published: Nov. 15, 2023
Water is our most precious resource, and its responsible management utilization are paramount in the face of ever-growing environmental challenges [...]
Language: Английский
Citations
21
Heliyon, Journal Year: 2024, Volume and Issue: 10(12), P. e33218 - e33218
Published: June 1, 2024
This study employs a comparative analysis method to examine variations in food waste (FW) generation between developed and developing nations, focusing on income levels, population growth rates, community engagement management. Quantitative data from Taiwan, Malaysia, Bangladesh are comprehensively analyzed using regression descriptive statistics. Results indicate that with its stringent regulatory frameworks advanced recycling technologies, generates significantly less FW per capita compared Malaysia Bangladesh. shows moderate levels of reduction efforts, supported by varying degrees participation, whereas faces challenges both enforcement technological adoption. The proposes an integrative management model emphasizing compliance participation metrics, technology diffusion indices effectively address challenges. These findings underscore the importance tailored strategies aligned economic demographic contexts nations. Policymakers practitioners can leverage these insights establish targeted goals enhance initiatives. research highlights urgency integrated approaches mitigate environmental public health risks associated mismanagement, advocating for evidence-based policies robust quantitative analysis.
Language: Английский
Citations
7
SSRN Electronic Journal, Journal Year: 2024, Volume and Issue: unknown
Published: Jan. 1, 2024
This research delves into the utilization of advanced artificial intelligence (AI), specifically ChatGPT or Bard, to improve strategies for monitoring and controlling water air pollution. Given escalating concerns surrounding environmental degradation its repercussions on public health, there is a pressing demand innovative pollution management techniques. investigation centers harnessing capabilities ChatGPT, an language model, address real-time data analysis, decision-making, engagement challenges within realm quality. Incorporating cutting-edge methods in monitoring, such as sensor networks, satellite imagery, IoT devices, this aims obtain comprehensive understanding dynamics. Nevertheless, substantial volume presents processing extracting meaningful insights. employed intelligent tool proficient comprehending natural queries delivering insightful analyses. integration streamlines interpretation intricate sets, enabling swift decision-making control authorities. Moreover, assumes pivotal role by serving user-friendly interface disseminating information levels, regulatory measures, preventive actions. Through interactive conversations, it enhances communication between agencies general public, cultivating awareness encouraging participation initiatives. paper underscores significance collaborative human-AI approach tackling multifaceted The also ethical considerations associated with AI-driven emphasizing importance responsible AI implementation. As technologies progress, proposed framework contribute ongoing discourse sustainable involvement. By synergizing state-of-the-art techniques, seeks offer efficacious solution advancing contemporary landscape.
Language: Английский
Citations
6
Sustainability, Journal Year: 2024, Volume and Issue: 16(7), P. 2995 - 2995
Published: April 3, 2024
Water, indispensable for life and central to ecosystems, human activities, climate dynamics, requires rapid accurate monitoring. This is vital sustaining enhancing welfare, effectively managing land, water, biodiversity on both the local global level. In rapidly evolving domain of remote sensing deep learning, this study focuses water body extraction classification through use recent learning models visual foundation (VFMs). Specifically, Segment Anything Model (SAM) Contrastive Language-Image Pre-training (CLIP) have shown promise in semantic segmentation, dataset creation, change detection, instance segmentation tasks. A novel two-step approach involving segmenting images via Automatic Mask Generator method SAM zero-shot segments using CLIP proposed, its effectiveness tested problems. The proposed methodology was applied imagery acquired from LANDSAT 8 OLI very high-resolution aerial imagery. Results revealed that accurately delineated bodies across complex environmental conditions, achieving a mean intersection over union (IoU) 94.41% an F1 score 96.97% satellite Similarly, dataset, achieved IoU 90.83% exceeding 94.56%. high accuracy selecting predominantly classified as highlights model intricate image analysis.
Language: Английский
Citations
4
Published: Jan. 1, 2025
Language: Английский
Citations
0
Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 381, P. 125156 - 125156
Published: April 5, 2025
Language: Английский
Citations
0
Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 73, P. 107664 - 107664
Published: April 10, 2025
Language: Английский
Citations
0
Lecture notes in networks and systems, Journal Year: 2025, Volume and Issue: unknown, P. 401 - 411
Published: Jan. 1, 2025
Language: Английский
Citations
0