The changing climate affects the agricultural lands, and, in turn, the changes in agricultural lands alter the watershed. A major concern regarding waterbodies is the increased sedimentation rates due to climate change. To improve the water quality, it is crucial to remove fine sediments. Using current environmental dredging methods is challenging because of the sediment volumes that must be dredged, the absence of nearby disposal sites, and the shoreline infrastructure at the dredging locations. To address these issues, we used a surgical dredging method with a variable area suction head that can easily maneuver around the docks, pilings, and other infrastructures. It can also isolate the fine grain material to better manage the dredged volumes in the seabed where nutrients are typically adhered. To this end, a statistical analysis of the dredged samples is essential to improve the design efficiency. In this work, we collected several samples using a variable area suction head with different design settings. The collected samples using each design setting were then used to model the distributions of the different grain sizes in the dredged sediments. The proposed statistical model can be effectively used for the prediction of sediment sampling outcomes to improve the gradation of the fine sediments.
Statistical Modeling of Fine Sediments Dredged Using a Variable Area Dredging Suction Head to Improve Water Quality
