Water Resources Management, Год журнала: 2025, Номер unknown
Опубликована: Март 11, 2025
Язык: Английский
Carbon letters, Год журнала: 2023, Номер 34(1), С. 265 - 289
Опубликована: Дек. 16, 2023
Язык: Английский
Процитировано
58
World Journal of Advanced Research and Reviews, Год журнала: 2024, Номер 21(1), С. 1373 - 1382
Опубликована: Янв. 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.
Язык: Английский
Процитировано
30
Results in Engineering, Год журнала: 2024, Номер 22, С. 102283 - 102283
Опубликована: Май 21, 2024
It is widely acknowledged that traditional agricultural practices must effectively address the increasing global demand for food while facing water scarcity and climate change challenges. The imperative environmentally sustainable approaches has never been more urgent. In response, IoT-based Smart Agriculture emerged as a promising solution. can significantly bolster development by integrating renewable energy sources, particularly in arid regions with abundant sunlight. Real-time control systems utilizing big data acquisition processing are pivotal this advancement. This study introduces cloud-based smart irrigation system to connect numerous small-scale farms centralize pertinent data. optimizes usage through comprehensive collection, storage, analysis. Leveraging insights from facilitate informed decision-making regarding management, thereby fostering conservation efforts, regions. Additionally, research explores weather prediction services enhance control, during intermittent rainy periods, within real-world testbed powered solar energy. incorporates sophisticated management system. showcases Farm prototype leveraging Internet of Things, embedded systems, low-cost Wireless Sensor Networks, NI CompactRIO controller, Cloud Computing. Encouragingly, results demonstrate tangible improvements conservation. Furthermore, deployment methodology outlined provides clear roadmap be readily adapted similar endeavors. © 2023 Elsevier Inc. All rights reserved.
Язык: Английский
Процитировано
30
Desalination, Год журнала: 2024, Номер 574, С. 117285 - 117285
Опубликована: Янв. 4, 2024
Язык: Английский
Процитировано
29
Engineering Science & Technology Journal, Год журнала: 2024, Номер 5(2), С. 517 - 530
Опубликована: Фев. 25, 2024
This comprehensive review explores the landscape of Urban Water Management in United States, focusing on sustainable practices aimed at addressing challenges posed by rapid urbanization and climate change. With urban areas facing increasing water stress, this study aims to identify, analyze, evaluate a range implemented across country. The encompasses diverse aspects Management, including efficiency measures, green infrastructure initiatives, change resilience strategies, pollution mitigation efforts. In examining investigates technological innovations policy frameworks that have contributed optimizing use settings. Additionally, role is explored, emphasizing its benefits applications through case studies successful implementations, shedding light how nature-based solutions can enhance sustainability. delves into critical dimension systems, analyzing impacts resources exploring adaptation strategies. Infrastructure improvements integrated planning approaches are examined as essential components building resilient systems. Addressing mitigation, focuses stormwater management wastewater treatment. Best regulatory measures scrutinized understand effectively managing treating mitigate protect quality. Furthermore, highlights significance holistic approach contexts. Stakeholder engagement cross-sectoral coordination emphasized integral elements implementing Through projects, extracts valuable lessons insights for future implementations. opportunities current providing nuanced understanding barriers identifying emerging opportunities. synthesizes key findings, implications, recommendations advancing States. generated contribute ongoing dialogue effective strategies face evolving environmental dynamics. Keywords: Sustainable Practices, Efficiency, Climate Change, Resilience, Pollution Mitigation, Infrastructure.
Язык: Английский
Процитировано
24
Results in Engineering, Год журнала: 2024, Номер 23, С. 102831 - 102831
Опубликована: Сен. 1, 2024
Water quality assessment and prediction play crucial roles in ensuring the sustainability safety of freshwater resources. This study aims to enhance water by integrating advanced machine learning models with XAI techniques. Traditional methods, such as index, often require extensive data collection laboratory analysis, making them resource-intensive. The weighted arithmetic index is employed alongside models, specifically RF, LightGBM, XGBoost, predict quality. models' performance was evaluated using metrics MAE, RMSE, R2, R. results demonstrated high predictive accuracy, XGBoost showing best (R2 = 0.992, R 0.996, MAE 0.825, RMSE 1.381). Additionally, SHAP were used interpret model's predictions, revealing that COD BOD are most influential factors determining quality, while electrical conductivity, chloride, nitrate had minimal impact. High dissolved oxygen levels associated lower indicative excellent pH consistently influenced predictions. findings suggest proposed approach offers a reliable interpretable method for prediction, which can significantly benefit specialists decision-makers.
Язык: Английский
Процитировано
20
Results in Engineering, Год журнала: 2024, Номер 21, С. 101920 - 101920
Опубликована: Фев. 15, 2024
Streamflow forecasting is crucial for effective water resource planning and early warning systems, especially in regions with complex hydrological behaviors uncertainties. While machine learning (ML) has gained popularity streamflow prediction, many studies have overlooked the predictability of future events considering anthropogenic, static physiographic, dynamic climate variables. This study, first time, used a modified generative adversarial network (GAN) based model to predict streamflow. The training concept modifies enhances existing data embed featureful information enough capture extreme rather than generating synthetic instances. was trained using (sparse data) combination variables obtained from an ungauged basin monthly GAN-based interpreted time local interpretable model-agnostic explanations (LIME), explaining decision-making process model. achieved R2 0.933 0.942 0.93–0.94 testing. Also, testing period been reasonably well captured. LIME generally adhere physical provided by related work. approach looks promising as it worked sparse basin. authors suggest this research work that focuses on learning-based predictions.
Язык: Английский
Процитировано
17
Results in Engineering, Год журнала: 2024, Номер 24, С. 103392 - 103392
Опубликована: Ноя. 10, 2024
Язык: Английский
Процитировано
15
Results in Engineering, Год журнала: 2024, Номер 21, С. 101828 - 101828
Опубликована: Янв. 29, 2024
In recent times, the densely populated Bengaluru metropolis in India has faced challenges related to water scarcity, particularly relying on Krishna Raja Sagara (KRS) dam. The forecasting of reservoir levels become challenging due spatio-temporal fluctuations meteorological conditions and complex physical processes. As a result, developing suitable management meet population's demand requires an accurate dependable estimate dam's level. This work attempted use daily weather data by utilizing long short-term memory (LSTM) networks. Seven high-performance models (viz., M1 M7) with varying window sizes horizons have been trained this their performance is compared. metrics revealed that M7 model outperformed other for level prediction, coefficient determination (R2) score 0.93, root mean square error (RMSE) 2.94, absolute percentage (MAPE) 0.01. study also provides valuable dashboard tracking forecasted largest Cauvery basin. Finally, innovative integration LSTM technology predictions dam not only addresses posed but sets new standard precision forecasting, thereby establishing crucial decision-support tool real-time monitoring enhanced resource India's metropolis.
Язык: Английский
Процитировано
14
International Journal of Science and Research Archive, Год журнала: 2024, Номер 11(2), С. 600 - 606
Опубликована: Март 24, 2024
This review paper presents a comparative study of the theoretical models underpinning Internet Things (IoT)-based irrigation systems, focusing on their application in managing water scarcity within agricultural sectors Africa and United States. By examining adaptation implementation these models, sheds light diverse strategies employed to optimize use enhance crop yields response pressing challenge scarcity. In Africa, emphasis is low-cost, innovative solutions tailored local conditions resource constraints, whereas U.S., focus shifts leveraging advanced technology, scalability, economic viability. The underscores significance IoT-based systems promoting sustainable agriculture management practices across different environmental socio-economic contexts. Through detailed discussion, reflects practical implications findings for policymakers, farmers, technology developers, acknowledging limitations current analysis while suggesting directions future research. insights highlight potential IoT technologies revolutionize globally, advocating increased collaboration innovation development solutions.
Язык: Английский
Процитировано
14