Version 1
: Received: 8 July 2021 / Approved: 9 July 2021 / Online: 9 July 2021 (14:41:13 CEST)
Version 2
: Received: 27 October 2021 / Approved: 28 October 2021 / Online: 28 October 2021 (10:01:47 CEST)
Version 3
: Received: 23 December 2021 / Approved: 28 December 2021 / Online: 28 December 2021 (12:10:17 CET)
How to cite:
Aderemi, B.A.; Olwal, T.O.; Ndambuki, J.M.; Rwanga, S.S. Groundwater Level Resources Management Modelling: A Review. Preprints2021, 2021070227. https://doi.org/10.20944/preprints202107.0227.v1
Aderemi, B.A.; Olwal, T.O.; Ndambuki, J.M.; Rwanga, S.S. Groundwater Level Resources Management Modelling: A Review. Preprints 2021, 2021070227. https://doi.org/10.20944/preprints202107.0227.v1
Aderemi, B.A., Julius Musyoka Ndambuki and Sophiar S. Rwanga. 2021 "Groundwater Level Resources Management Modelling: A Review" Preprints. https://doi.org/10.20944/preprints202107.0227.v1
Abstract
Globally, groundwater is the largest distributed storage of freshwater that plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, this resource is not unlimited and its sustainability is highly dependent on its prudent use. Thus, efficient management of groundwater resources to prevent overexploitation, scarcity and drought has become a major challenge for researchers as well as water managers. To solve these challenges, many solutions such as simulation and optimisation models have been proffered through the use of historical data. Therefore, this has made efficient data gathering essential to maintain data-driven groundwater level resource management models from the observation site. The global evolution of the Internet of Things (IoTs), has increased the nature of data gathering for the management of groundwater resources. Recently, a number of research challenges such as the lack of computational efficiency and scalability due to uncertainties from input parameters to the groundwater level resource model have been revealed in the management of groundwater level resources. In addition, efficient data-driven groundwater level resource management relies hugely on information relating to changes in groundwater resource levels as well as its availability. At the moment, the groundwater managers lack an efficient and scalable groundwater management system to gather the required data. The literature revealed that the existing methods of collecting data lack efficiency to meet computational model requirements and meet management objectives. Although the IoTs enabled automated data processing systems are in existence by transmitting the generated data from IoT enabled devices into the cloud through the Internet. However, traditional IoT Internet is not scalable and efficient enough to process the generated vast IoT data. Thus, it is necessary to have an efficient and scalable IoT system to extract valuable information in real-time for groundwater level resource management. Unlike previous surveys which solely focussed on particular groundwater issues related to simulation and optimisation management models, nonetheless, this paper seeks to highlight the current groundwater level resources management models as well as the IoT contributions.
Keywords
Internet of Things (IoTs); Groundwater Level Resource; Groundwater Management Model; Groundwater Measurement; Optimisation; Remote Sensing; Sensor Network; Simulation
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
