Blue Energy Intelligence: A Hybrid Wavelet–AI Model for Sustainable Marine Energy Harvesting and Hydrological Signal Monitoring via IoT Systems

A clever and integrated system of sustainable hydrological monitoring and prediction has been implemented with an Internet of Things platform with enhanced data analysis. The system comprises low-power, solar-operated sensor nodes that are equipped with groundwater level, temperature and environmental sensors, which send data over LoRaWAN at fixed intervals to a central gateway. In order to guarantee data consistency and integrity, raw sensor readings first undergo rigorous preprocessing, including outlier removal, imputation of missing values and normalization through Min–Max scaling and Z-score methods. Data exploration is then conducted to detect anomaly changes with seasons and hidden trends for the time-series data. The structured and prepared data are then fed into a long short-term memory artificial neural network in Python for accurate forecasting of hydrological parameters. The LSTM model forecasts with high accuracy because of its capability to model complex temporal relations and timewise dependence. It is scalable, standalone and can be operated in remote or hostile environments, where its performance has been validated through simple metrics like RMSE and MAE. Together with the hardware and software components, it provides agile insight and intelligent management of water resources. Its early warning capacities endorse its usefulness, while enabling policymakers to make informed sustainable decisions with regard to hydrological resilience. The prediction accuracy of the trained model of 98.9% exceeds existing techniques including XGBoost, Random Forest, STW-DBSCAN, thereby validating the applicability of the proposed hybrid IoT–AI framework toward smart and sustainable hydrological resource management.