# AUV Adaptive Sampling Methods: A Review
## 3 Target of Interest
### Biological Features
An advection effect, owing to ocean currents as an external force, was primarily focused on to predict the dynamics of the algal bloom. The analysis step of the predictive model consisted of two parts, as shown in Table 6. A recursive grid search was performed using a rewarding system. Cells were split into a \"\(4\times 4\)\" grid. Then, inside each survey rectangle region, rewards were set based on the measured intensity in the recursive algorithms. A lawn-mower template for an AUV survey pattern was initially adopted. This approach allowed the vehicle to respond to the acquired measurements in the vicinity of the hotspots. [PERSON] et al. [46] then introduced a hierarchical probabilistic regression, an adaptive model-based active learning algorithm using Gaussian progress regression to achieve precisely targeted sampling which showed promising results. They focused on the relationship between measurable in-situ environmental parameters and the biological phenomena of interest: such as the correlation between temperature or salinity and organism abundance. The first environmental model is trained from the data from an AUV deployment, while the second biological model was trained from the data from the analysed water samples in the laboratory. Using an iterative Gaussian process, the environmental model function infers the predictive distribution of environmental conditions at unobserved locations. The biological model function infers that of organism abundance at unobserved environmental conditions. [PERSON] et al. [47] introduced a data-assimilation approach to define the locations of interest (waypoints) for their vehicles to visit and sample in a two-dimensional plane. They developed a model-based 2D trajectory design method that combines the predictive model and the tracks for the autonomous gliders. The predictive tool for ocean modelling that was utilised in their method was the Regional Ocean Model System (ROMS). The ROMS initially determines the characteristics of interest for input into the algorithm, which determines the sampling mission plan for the gliders. Once the data is sampled by the vehicles, it is returned into the ROMS as inputs in reverse. Based on the new waypoints computed, the current mission is overwritten, and a new sampling plan is generated. This process repeats until the target is out of either range or interest. Their particular target of interest was \begin{table}
\begin{tabular}{c c c} \hline \hline
**Step** & **1. Hotspot Detection** & **2. Hotspot Advection** \\ \hline Type & Remote sensing data & Surface current data \\ Source & Satellite & High frequency radar stations \\ Proxy & Ocean colour, emitted radiance & Radial ocean surface current \\ Description & Onset of a bloom & Transport of the bloom by current and wind \\  & Fluorescence Line Height (FLH) & \\ Product & from MODerate Resolution & \\  & Imaging Spectroradiometer) & Open-Boundary Modal Analysis (OMA) \\  & MODIS instrument & \\ Other application & NASA’s Terra, Aqua & Regional Ocean Model System (ROMS) \\  & Earth-orbiting spacecraft & model output could be coupled with OMA \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Predictive computation process, consisting of two steps: detection and advection developed from [24]. when and where the harmful algal blooms may occur. A fresh water plume was the key feature in their ocean model to track the microscopic organisms. This approach is valid assuming that the feature of the plume, having low salinity and density, remains detectable for weeks. [PERSON] et al. extended their previous work from a 2D planar trajectory planning to 3D path planning [48] by adding another spatial dimension, so as to track and sample dynamically an evolving biological feature. Their objectives here were twofold: tracking the centroid of the plume and sampling the boundary of the plume. Three algorithms were designed: waypoints selection for centroid tracking, waypoints selection for boundary tracking and 4D (three spatial and time dimension) ocean current prediction.