Accurate monitoring of chlorophyll-a (Chl-a) is critical for assessing eutrophication and water quality in inland aquatic ecosystems. While integrating Sentinel-2 MultiSpectral Instrument (MSI) imagery with machine learning (ML) is widely used for water quality assessment, challenges remain in optimizing Chl-a retrieval in optically complex small-scale reservoirs, particularly in semi-arid environments where traditional models often fail. This study introduces a machine learning (ML)-based approach to enhance Sentinel-2 MSI Chl-a retrieval, systematically evaluating six ML models: Artificial Neural Networks (ANN), CatBoost (CB), random forest (RF), ridge regression (RR), support vector regression (SVR), and extreme gradient boosting (XGB). By integrating Sentinel-2 MSI reflectance data with in-situ measurements from reservoirs exhibiting diverse trophic conditions, this study not only compares model performance but also applies feature importance analysis to refine spectral band selection for improved Chl-a retrieval. Among the models, RF demonstrated the highest predictive accuracy (R² = 0.93 calibration, R² = 0.91 validation), outperforming CB and XGB, whereas linear models such as RR and SVR were inadequate for capturing the nonlinear spectral relationships of Chl-a. The results highlight the advantages of ensemble-based ML models in handling complex water quality datasets, particularly in semi-arid and data-scarce regions. The proposed framework provides a robust, data-driven approach for integrating Sentinel-2 MSI imagery with AI techniques, supporting real-time water quality assessment and environmental management in diverse aquatic ecosystems. This research advances the application of ML for remote sensing-based water quality monitoring by systematically assessing model reliability, feature selection, and efficient application in semi-arid small-scale reservoirs. The proposed approach enhances the scalability of ML-driven remote sensing, offering a transferable framework for Chl-a estimation in data-scarce and environmentally vulnerable regions.

This work suggests a solution for preventing/eliminating the predicted Sea Level Rise (SLR) by seawater desalination and storage through a large number of desalination plants distributed worldwide; it also comprises that the desalinated seawater can resolve the global water scarcity by complete coverage for global water demand. Sea level rise can be prevented by desalinating the additional water accumulated into oceans annually for human consumption, while the excess amount of water can be stored in dams and lakes. It is predicted that SLR can be prevented by desalination plants. The chosen desalination plants for the study were Multi-Effect Desalination (MED) and Reverse Osmosis (RO) plants that are powered by renewable energy using wind and solar technologies. It is observed that the two main goals of the study are fulfilled when preventing an SLR between 1.0 m and 1.3 m by 2100 through seawater desalination, as the amount of desalinated water within that range can cover the global water demand while being economically viable.