Quantifying the scale of erosion along major coastal aquifers of Pakistan using geospatial and machine learning approaches

Insufficient freshwater recharge and climate change resulted in seawater intrusion in most of the coastal aquifers in Pakistan. Coastal aquifers represent diverse landcover types with varying spectral properties, making it challenging to extract information about their state hence, such investigation requires a combination of geospatial tools. This study aims to monitor erosion along the major coastal aquifers of Pakistan and propose an approach that combines data fusion into the machine and deep learning image segmentation architectures for the erosion and accretion assessment in seascapes. The analysis demonstrated the image segmentation U-Net with EfficientNet backbone achieved the highest F 1 score of 0.93, while ResNet101 achieved the lowest F 1 score of 0.77. Resultant erosion maps indicated that Sandspit experiencing erosion at 3.14 km 2 area. Indus delta is showing erosion, approximately 143 km 2 of land over the past 30 years. Sonmiani has undergone substantial erosion with 52.2 km 2 land. Miani Hor has experienced erosion up to 298 km 2 , Bhuri creek has eroded over 4.11 km 2 , east Phitii creek over 3.30 km 2 , and Waddi creek over 3.082 km 2 land. Tummi creek demonstrates erosion, at 7.12 km 2 of land, and East Khalri creek near Keti Bandar has undergone a measured loss of 5.2 km 2 land linked with quantified reduction in the vertical sediment flow from 50 (billion cubic meters) to 10 BCM. Our analysis suggests that intense erosions are primarily a result of reduced sediment flow and climate change. Addressing this issue needs to be prioritized coastal management and climate change mitigation framework in Pakistan to safeguard communities. Leveraging emerging solutions, such as loss and damage financing and the integration of nature-based solutions (NbS), should be prioritized for the revival of the coastal aquifers.