## 4 Advancements in Remote Sensing Technologies for Coastal Monitoring
### Applications of Machine Learning and Artificial Intelligence in Coastal Monitoring
It was finally observed and concluded in this research that the backtracking search algorithm enhances performance due to the improvement of mutation and crossover operations. Using experimental data from Santorini, Greece, it was displayed that the proposed method prevails over the existing modeling techniques. AI models, such as WaterNet, significantly outperform traditional methods like the DSA and NDWI regarding shoreline variability. This improvement is due to the ability of AI to integrate spatial and spectral features, process extensive data efficiently, and adapt to diverse environmental conditions. For instance, WaterNet achieves over 90% classification accuracy in complex environments, whereas the NDWI typically performs at 70-80% due to the misclassification of mixed land-water features [54; 55]. Traditional systems, such as the DSA, rely much more on predefined parameters and thus perform inefficiently under dynamic conditions. WaterNet, trained on various datasets, copes with different tidal levels and sedimentation. Studies have shown that it reduces the sedimentation-rich coast classification error from 25% for the DSA to below 10% [56; 57]. In terms of accuracy metrics, WaterNet achieves a root mean square error (RMSE) of 5 m compared to 15 m for the DSA and 18 m for the NDWI, particularly in areas with vegetation interference [66]. Precision and recall are also superior, with WaterNet scoring 0.92 and 0.89, respectively, versus 0.81 and 0.78 for the DSA [37]. While AI and ML models like WaterNet have shown unparalleled advancement in the field of shoreline detection, scalability to diverse geographies and shorelines should be cautiously considered. Such models are developed for different environments, including sediment-heavy deltas, vegetated coasts, and urbanized shorelines, based on multispectral and temporal datasets [4; 12]. WaterNet can process high-resolution UAV imagery or medium-resolution Sentinel-2 datasets, making the technology suitable for modeling complicated shorelines and capturing many environments [2; 10]. However, the deployment of such technologies in underdeveloped regions faces serious challenges. Among the critical challenges are high-quality training datasets, which are mostly unavailable within less-developed regions because historic shoreline monitoring has not been performed, or inconsistent data collection practices. Indeed, [PERSON] et al. (2023) [62] and [PERSON] et al. (2021) [6] presented these findings. In addition, the computational infrastructure necessary for training and deploying AI models, such as access to high-speed internet and cloud computing platforms, is usually lacking in these areas. This creates a bottleneck in processing large datasets and implementing complex algorithms ([PERSON] et al., 2021; [PERSON] et al., 2023) [5; 11]. Another important barrier to scalability involves shoreline variability across geographies. Most AI models, like WaterNet, are trained on regional datasets that cannot capture the geomorphological variability of coastlines around the world. For instance, models that have training data derived from sandy beaches have been shown to not generalize well to rocky coasts or mudflats, requiring regional calibration or retraining of the models themselves [3; 7]. This is an expensive process and may hinder the deployment of such models in resource- and expertise-scarce underdeveloped regions. Moreover, the absence of GTD in large, underdeveloped areas has implications for the validation and accuracy of the models. Without strong GTD for calibration, the AI models may fail to capture local tidal variations, vegetation interference, or anthropogenic impacts and thus have higher error margins [8; 44]. This calls for methods that effectively use limited or low-cost GTD, such as UAV-based data collection or community-led shoreline surveys.