In the development of the IoT-based Predictive Water and Snow Management System, two distinct approaches have been pursued: one focusing on the development of Long Short-Term Memory (LSTM) neural networks for predictive modeling, and the other on the IoT sensor architecture. While the LSTM model has advanced significantly, the sensor-based IoT framework is still in its architectural and variable testing stages.

1. LSTM Development for Predictive Modeling:

This approach involves the creation and optimization of LSTM neural networks to predict environmental trends. LSTMs are particularly suited for processing time-series data, enabling the prediction of crucial ecological variables such as snow water equivalent, soil temperature, and snow depth.

Key Developments in LSTM Modeling:

• Data from Arpa Piemonte: The data for this modeling has been provided by Arpa Piemonte - Agenzia Regionale per la Protezione Ambientale, ensuring access to accurate and comprehensive datasets on environmental conditions. This data forms the foundation of the LSTM model's training and testing.
• Initial Testing with Piamprato Station Dataset: The LSTM model has been successfully tested using historical datasets from Piamprato Station, a critical site for this project. Early results have shown the model's capability in accurately forecasting environmental changes related to water and snow management.
• Expansion to New Locations: In the next stage of development, the LSTM model will be tested on datasets from additional locations provided by Arpa Piemonte, including:
  1. Alagna Valsesia Stazione
  2. Rosone
  3. Ceresole
  4. Sestriere
  5. Praly

Simultaneous testing across these locations will further refine the model's ability to predict environmental conditions across diverse geographic settings. By assessing data from these various regions, the LSTM model will be optimized to ensure robust forecasting for real-time management of water and snow resources.

• Predictive Capability: The LSTM's ability to model complex relationships between time-dependent variables enhances its effectiveness in generating real-time predictions. Integrating this predictive capability within the IoT framework will enable the system to proactively manage environmental conditions, ensuring timely interventions in water and snow management.

2. Sensor-Based IoT Architecture for Environmental Monitoring:

This approach focuses on the IoT sensor deployment and the design of a scalable system architecture. While the LSTM development is well underway, the work on the sensor-based IoT framework is currently in its initial stages, concentrating on sensor testing and architectural design.

Key Developments in Sensor and IoT Architecture:

• IoT Sensor Testing: Extensive testing has been conducted on various sensors to monitor key environmental variables, such as temperature, humidity, particulate matter, and snow depth. The goal here is to ensure reliable data collection for use in predictive models, though full deployment is yet to be realized.
• System Architecture Design: A scalable, cloud-based system architecture has been designed to manage large-scale sensor deployments. This architecture facilitates real-time data transmission from sensors to a centralized cloud system for processing by the predictive models, ensuring seamless communication between IoT devices and cloud storage.
• Variable Analysis: A comprehensive review of critical ecological variables, such as snow water equivalent, soil temperature, and wind speed, has helped prioritize the most important factors for monitoring. This ensures that the system will be effective in managing both water and snow resources, and that key environmental data is properly integrated into the predictive models.

Conclusion:

The project is advancing along two primary lines: LSTM-based predictive modeling and IoT sensor architecture development. The LSTM model, using data from Arpa Piemonte - Agenzia Regionale per la Protezione Ambientale, has already shown promising results with datasets from Piamprato Station, and will now be tested across additional sites. Meanwhile, the sensor-based IoT system is still in its testing and architectural design phases, with a focus on ensuring reliable data acquisition and scalability.

These two approaches will eventually converge, creating a fully automated, real-time system for managing water and snow resources. The integration of machine learning with IoT technology will result in a highly effective and responsive system capable of addressing both current and future challenges in environmental management. The combination of LSTM-based forecasting and real-time IoT data collection promises a significant advancement in water and snow resource management, ensuring efficient and proactive interventions.