Your search found 3 records
1 Ahmed, S. S.; Bali, R.; Khan, H.; Mohamed, H. I.; Sharma, S. K. 2021. Improved water resource management framework for water sustainability and security. Environmental Research, 201:111527. (Online first) [doi: https://doi.org/10.1016/j.envres.2021.111527]
Water resource management ; Frameworks ; Water security ; Sustainability ; Water distribution systems ; Rural area ; Monitoring systems ; Technology ; Ultrasonic devices ; Water supply ; Water demand ; Water quality ; Drinking water ; Water levels ; Moisture content ; Forecasting
(Location: IWMI HQ Call no: e-copy only Record No: H050472)
(6.53 MB)
The water resource is an essential field of economic growth, social progress, and environmental integrity. A novel solution is offered to meet water needs, distribution, and IoT-based quality management requirements. With technological growth, this paper presents an IoT-enabled Water Resource Management and Distribution Monitoring System (IWRM-DMS) using sensors, gauge meters, flow meters, ultrasonic sensors, motors to implement in rural cities. Thus, research proposes that the IWRM-DMS establish the rural demand for water and the water supply system to minimize water demand. The system proposed includes different sensors, such as the water flow sensor, the pH sensor, the water pressure valve, the flow meters, and ultrasound sensors. This water system has been developed, which addresses the demand for domestic water in the village. Machine Intelligence has been designed for demand prediction in the decision support system. The simulation results confirm the applicability of the proposed framework in real-time environments. The proposed IWRM-DMS has been proposed to analyse the water quality to ensure water distribution in a rural area to achieve less MAPE (21.41%) and RMSE(15.12%), improve efficiency (96.93%), Reliability (98.24%), enhance prediction (95.29%)), the overall performance (97.34%), moisture content ratio (7.4%), cost-effectiveness ratio (95.7%) when compared to other popular methods.
2 Liu, H. 2021. Agricultural water management based on the internet of things and data analysis. Acta Agriculturae Scandinavica, Section B - Soil and Plant Science, 13p. (Online first) [doi: https://doi.org/10.1080/09064710.2021.1966496]
Agriculture ; Water management ; Data analysis ; Technology ; Time series analysis ; Forecasting ; Models ; Monitoring systems ; Irrigation systems ; Water demand ; Irrigation water ; Farmland ; River basins
(Location: IWMI HQ Call no: e-copy only Record No: H050624)
https://www.tandfonline.com/doi/pdf/10.1080/09064710.2021.1966496
https://vlibrary.iwmi.org/pdf/H050624.pdf
https://vlibrary.iwmi.org/pdf/H050624.pdf
(2.23 MB) (2.23 MB)
To improve the effect of agricultural water management, this paper builds an agricultural water management system based on the Internet of Things and data analysis, and designs an intelligent analysis model of the system using the method of time series forecasting. Moreover, this paper designs the software and hardware of the ZigBee wireless sensor network monitoring node, including the hardware circuit design of the ZigBee network monitoring node and the software acquisition program design to realise the data acquisition and short-distance transmission of the farmland environment. In addition, this paper designs a farmland irrigation system based on the Internet of Things, which can also realise real-time monitoring of agricultural water quality. Finally, this paper designs an experiment to analyse the performance of the system constructed in this paper. Judging from the performance of the agricultural water management system, it can be seen that its performance can meet the actual needs of agricultural water management.
3 Kolarik, N. K.; Roopsind, A.; Pickens, A.; Brandt, J. S. 2023. A satellite-based monitoring system for quantifying surface water and mesic vegetation dynamics in a semi-arid region. Ecological Indicators, 147:109965. (Online first) [doi: https://doi.org/10.1016/j.ecolind.2023.109965]
Surface water ; Water resources ; Satellites ; Remote sensing ; Monitoring systems ; Vegetation ; Semiarid zones ; Restoration ; Data fusion ; Land use ; Water availability ; Landscape ; Riparian zones / United States of America
(Location: IWMI HQ Call no: e-copy only Record No: H051700)
https://www.sciencedirect.com/science/article/pii/S1470160X23001073/pdfft?md5=6875ffa9aa8c0ae7220a3d5d089883ef&pid=1-s2.0-S1470160X23001073-main.pdf
https://vlibrary.iwmi.org/pdf/H051700.pdf
https://vlibrary.iwmi.org/pdf/H051700.pdf
(15.70 MB) (15.7 MB)
Semi-arid and arid systems cover one third of the earth’s land surface, and are becoming increasingly drier, but existing datasets do not capture all of the types of water resources that sustain these systems. In semi-arid environments, small surface water bodies and areas of mesic vegetation (wetlands, wet meadows, riparian zones) function as critical water resources. However, the most commonly-used maps of water resources are derived from the Landsat time series or single date aerial photographs, and are too coarse either spatially or temporally to effectively monitor water resource dynamics. In this study, we produced a Sentinel Fusion (SF) water resources product for a semi-arid mountainous region of the western United States, which includes monthly maps of both a) surface water and b) mesic vegetation at 10 m spatial resolution using freely available Earth observation data on an open access platform. We applied random forest classifiers to optical data from the Sentinel-2 time series, synthetic aperture radar (SAR) data from the Sentinel-1 time series, and topographic variables. We compared our SF product with three commonly used and publicly available datasets in the western U.S. We found that our surface water class contained fewer omission errors than a leading global surface water product in (94 % producer’s accuracy (PA) vs 84 %) and comparable user’s accuracy (UA) (91 % vs 97 %) with commission errors occurring largely in mixed water pixels. Our mesic vegetation class had up to 43 % higher PAs compared to the National Wetlands Inventory (NWI) estimates and up to 78 % higher UAs over the Sage Grouse Initiative mesic resources maps during the most critical part of the water year. We found that while inclusion of SAR data from the C-band Sentinel-1 sensor consistently improved estimates of water resources in each of the last four months of the 2021 water year when compared to optical-only + topographic variables, only in September did those improvements lie outside of the 95 % confidence interval. With nine times finer spatial resolution and more frequent image collection, our SF maps characterize intra-annual dynamics of smaller water bodies (<30 m wide) and mesic vegetation integral to ecosystem functioning in semi-arid systems compared to leading Landsat-derived products. Further, our workflow is easily reproducible using freely available data on an open access platform, and can be adopted to help guide land use decisions related to water resources by farmers, ranchers, and conservationists in semi-arid environments.
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