IoT-based data and analytic hierarchy process to map groundwater recharge with stormwater

Abstract

The sustainable management of groundwater resources in developing countries is often challenging due to limited measurement and monitoring infrastructure to collect data necessary for decision support. To make a contribution towards addressing these challenges, this study investigated the use of Internet of Things (IoT) technology and low-cost sensors to collect the required groundwater-level data and develop a model to map the recharge potential with stormwater. The study focused on two stormwater ponds located in a highly urbanised area in Cape Town, South Africa. A combination of Geographic Information System and analytic hierarchy process was integrated to generate a groundwater recharge potential zone map of the study area. The IoT-based data were used to develop and calibrate a numerical groundwater model in MODFLOW. The study determined that retrofitted stormwater ponds are potential groundwater augmentation zones and can provide opportunity for stormwater recharge in urban areas. Overall, this study highlights the potential of IoT to collect hydrogeological data with low-cost sensors. Data can be collected at high temporal resolution, and the spatial scale can be increased due to availability of low-cost sensors. HIGHLIGHTS Internet of Things (IoT)-based data were leveraged to address challenges in groundwater management.; Rapid battery drain powering the IoT system was mitigated by reducing the data collection frequency and dead sleep mechanisms.; Geographic Information System–based analysis and analytic hierarchy process were employed to map the recharge potential in a highly urbanised area in Cape Town.; This study demonstrated that stormwater ponds have the potential to recharge groundwater aquifers through infiltration.;

Keywords

• analytic hierarchy process
• cape town
• groundwater recharge
• iot-based data
• modflow
• stormwater