Artificial intelligence can play a key role in environmental sustainability. For this reason, AI environmental monitoring solutions are emerging, focused on actively monitoring the territory and preventing catastrophic events caused by climate change.
Thanks to its ability to analyze enormous amounts of data in real-time, AI makes it possible to predict extreme weather events, optimize natural resources, and reduce human impact on ecosystems. In particular, among the AI solutions for environmental sustainability, platforms geared towards monitoring wildfires, monitoring and forecasting precipitation, and predicting river flooding are especially relevant.The data speaks for itself: in the last 20 years, 8.5 million hectares have been destroyed by fires in the Mediterranean basin —an area 3.5 times the size of Sardinia. In Italy, an average of over 106,000 hectares of forest fires are recorded annually. Between 2020 and 2021, the situation saw a sharp increase: a 23% rise in fires and a doubling of the affected forest areas. Consequently, the global market for AI in fire prediction continues to grow with a CAGR of 30%, increasing from $400 million in 2024 to a projected $2.5 billion by 2032.
According to the data collected in the 2023 Climate City Report by the National Climate City Observatory of Legambiente, from 2010 to October 31, 2023, the Climate City map recorded 684 floods caused by intense rainfall, 166 river overflows, and 86 landslides due to heavy rain, accounting for 49.1% of all events. The intensification of extreme weather events is now more than evident, as is the urgency of finding a solution as soon as possible.
In all the areas described, Revelis has gained significant experience and offers state-of-the-art technological systems, which will be detailed later in this article.
AI environmental monitoring: Revelis solutions
Over the past two years, Revelis has dedicated an entire line of research to AI applied to environmental sustainability, developing platforms that combine geospatial analysis, Big Data Analytics, and deep learning.
One of our most significant experiences is ADELE4RAIN (A DEep LEarning‑based framework for RAINfall estimation and forecasting), a solution designed to integrate networks of rain gauge stations, weather radars, and civil APIs into a single predictive ecosystem. The ADELE4RAIN platform is based on ensembles of neural networks capable of detecting complex patterns in real-time, even in complex hydrographic contexts like that of the Calabria region. The platform provides functionalities to monitor rainfall and issue early warnings in the case of cloudbursts. Thanks to ADELE4RAIN’s predictive models, it is possible to optimize hydrogeological risk management by supporting decisions for the pre-positioning of emergency vehicles or the preventive closure of critical infrastructure.
With regard to AI environmental monitoring solutions for wildfire management, Revelis has created PRIMA (Piattaforma Real-time per l’Ingestion, il monitoraggio e l’analisi degli Incendi boschivi – Real-time Platform for the Ingestion, Monitoring, and Analysis of Wildfires). Its implementation for wildfire management is built as a vertical on PlugAIn, the Revelis solution for the manufacturing, smart cities, and multi-utilities sectors, and a platform that enables IoT Intelligence applications. PRIMA is an innovative project that aims to develop an integrated platform for the real-time monitoring and analysis of wildfires, providing comprehensive and timely decision-making support to enable optimal fire management. The combination of fire propagation prediction algorithms, data analytics techniques, and GenAI systems provides Control Rooms with advanced tools to optimize emergency response efforts.
AI environmental monitoring: the advantages
The advantages of monitoring the territory using AI environmental monitoring solutions are numerous.
First and foremost, perhaps the most critical aspect is the capacity for prediction and early warning. Unlike traditional systems, which often react to events already underway, AI environmental monitoring makes it possible to analyze a myriad of parameters in real-time—weather data, soil saturation, river levels, vegetation humidity, surface temperatures, ground movements—and identify patterns and anomalies that indicate an imminent risk, offering authorities the chance to activate preventive action plans.
In the context of intense rainfall, for example, predictive models based on machine learning can anticipate the risk of floods or landslides, giving authorities precious time to warn the population and secure at-risk areas. For fires, the early detection of outbreaks, even small ones, allows for rapid and effective interventions that can prevent the uncontrolled spread of devastating blazes.
Another fundamental advantage is resource optimization. Decisions based on AI are guided by precise data and advanced analysis, allowing for a more efficient use of personnel, emergency vehicles, and economic resources. Instead of deploying forces across vast areas for manual checks, AI precisely indicates the critical points where efforts should be concentrated, improving the effectiveness of interventions and reducing waste.
Scalability and coverage are also high; while human surveillance or systems based on a few sensors have inherent limits, AI solutions can integrate data from thousands of sensors distributed over vast territories, drones, and satellites, offering a holistic and constant view. This is particularly advantageous for monitoring remote or hard-to-reach areas, such as large natural parks or mountain regions.
Furthermore, precision and reliability are improved. Machine learning algorithms, trained on enormous historical datasets, learn to distinguish with great accuracy between real danger signals and false positives, reducing unwarranted alarms and increasing confidence in monitoring systems.
Finally, a significant benefit is the ability to provide in-depth post-event analysis. After a flood or fire, AI can analyze the collected data to better understand the event’s dynamics, identify its causes, and evaluate the effectiveness of the countermeasures taken. This information is crucial for continuously improving prevention and response strategies, making our communities more resilient to the challenges posed by climate change.
Conclusions
AI applications in environmental sustainability enable wildfire monitoring, precipitation forecasting, and the optimization of natural resources.
The strategic adoption of AI environmental monitoring is therefore a decisive lever for the environmental sustainability of our territories. Today, the same technology that analyzes millions of pixels to detect a fire outbreak or billions of radar measurements to anticipate a cloudburst can transform a foreseen crisis into a manageable risk.
Revelis, with its ADELE4RAIN and PRIMA platforms and its suite of geospatial intelligence solutions, demonstrates that a data-driven approach not only lowers costs and response times but also generates shared value for communities, businesses, and institutions.
On the energy front, AI-IoT integration allows for shifting loads, modulating consumption, and optimizing renewable sources, contributing to the achievement of European climate neutrality targets and strengthening the security of critical infrastructures.
Looking to the future, the priorities are clear: investing in AI environmental monitoring solutions and promoting a public-private collaboration ecosystem to accelerate the spread of best practices will allow Artificial Intelligence to express its full potential and become our best ally in the process of improving environmental sustainability.
