A Review of Groundwater Management Models with a Focus on IoT-Based Systems

Julius Musyoka Ndambuki,Banjo Ayoade Aderemi,Sophia Sudi Rwanga,Thomas Otieno Olwal

doi:10.3390/su14010148

Julius Musyoka Ndambuki, Banjo Ayoade Aderemi + Show 2 more

Open Access

https://doi.org/10.3390/su14010148

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Abstract

Globally, groundwater is the largest distributed storage of freshwater and plays an important role in an ecosystem’s sustainability in addition to aiding human adaptation to both climatic change and variability. However, groundwater resources are dynamic and often change as a result of land usage, abstraction, as well as variation in climate. To solve these challenges, many conventional solutions, such as certain numerical techniques, have been proffered for groundwater modelling. The global evolution of the Internet of Things (IoT) has enhanced the culture of data gathering for the management of groundwater resources. In addition, efficient data-driven groundwater resource management relies hugely on information relating to changes in groundwater resources as well as their availability. At the moment, some studies in the literature reveal that groundwater managers lack an efficient and real-time groundwater management system which is needed to gather the required data. Additionally, the literature reveals that the existing methods of collecting data lack the required efficiency to meet computational model requirements and meet management objectives. Unlike previous surveys, which solely focussed on particular groundwater issues related to simulation and optimisation management methods, this paper seeks to highlight the current groundwater management models as well as the IoT contributions.

Highlights

About approximately one-third of global freshwater consumption depends on groundwater resources; it has become an important source of freshwater globally [1]
The results showed that Artificial Neutral Networks (ANNs) models are superior to the conventional regression method
The results show that the support vector machine (SVM) model performed better than the ANN model

Summary

Introduction

About approximately one-third of global freshwater consumption depends on groundwater resources; it has become an important source of freshwater globally [1]. Groundwater resource information is strenuous to collect and use due to the lack of proper integration between the equipment deployed, irrelevant and inconsistent data as a result of lack of stationary large-scale flow rate hindrances, un-automated groundwater analysis processes, and a lack of interoperability in previous systems [177,178,179,180,181,182]. These limitations are causing untold strains in groundwater resource management. Via the advancements recorded in the field of Information and Communication Technologies (ICT), there are many opportunities available to manage the groundwater resource crises

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