AI solutions for weather forecasting are today a fundamental tool for monitoring and predicting rainfall and for making important decisions proactively. While traditionally, weather forecasts have been based on complex physical models and the analysis of enormous amounts of atmospheric data, today the advent of Artificial Intelligence is opening up unprecedented horizons, radically transforming the way we understand and anticipate the future of weather.
In this article, we will therefore explore how our AI-based solutions are revolutionizing weather forecasting, offering companies or municipalities powerful tools to make more informed decisions, optimize resources, and mitigate the risks associated with atmospheric events.
The calabrian context
The increased frequency and intensity of extreme weather events, such as floods and landslides, have made the use of AI solutions for weather forecasting essential for monitoring and providing rainfall predictions. These phenomena not only threaten human life but also have a devastating impact on infrastructure, agriculture, and the economy. A tragic example of such an impact occurred on August 20, 2018, when heavy rain caused the collapse of a dam, sweeping away a group of hikers crossing the Raganello Gorge in the province of Cosenza, Calabria. This incident highlighted the urgency of more accurate and timely forecasting systems to prevent similar tragedies and environmental disasters.
Calabria, with its particular orography and the presence of small and vulnerable hydrographic basins, is a region particularly exposed to extreme events such as landslides and flash floods. To address these challenges, it is crucial to develop more sophisticated tools for rainfall monitoring, considering the limitations of existing technologies. Although rain gauges, weather radars, and geostationary satellites provide useful data, they present difficulties related to accuracy and reliability. For example, radars can estimate rainfall intensity in real-time, but their effectiveness is reduced by the presence of physical obstacles and atmospheric variability. Furthermore, rain gauges offer point measurements that may not reflect the spatial distribution of rainfall over large areas.
In this scenario, artificial intelligence is establishing itself as an innovative solution to enhance the accuracy of weather forecasts. Thanks to the use of sophisticated algorithms capable of analyzing large volumes of data from various sources, it is possible to obtain more precise and reliable predictions.
ADELE4RAIN: here’s how ai for weather forecasting works
The ADELE4RAIN project (A DEep LEarning-based framework for RAINfall estimation and forecasting), as an AI solution for weather forecasting, aims to create an integrated and comprehensive platform for monitoring and predicting rainfall by combining modules that leverage advanced technologies.
A case study in Calabria, with its climatic variability and vulnerability to floods, offers the opportunity to test and optimize this innovative system, thereby contributing to a more effective management of hydrogeological risk in the region.
The framework that has been decided to adopt and proposed in [Guarascio et al., 2020], is as follows:
It consists of three macro-components, illustrated in Figure 1, which shows the entire workflow, highlighting the intermediate results obtained from each component.
The Information Retrieval macro-module is dedicated to the extraction and integration of different types of information. Specifically, the main data sources used include:
- Weather Station Networks: directly measure the amount of rain through rain gauges, which record the water that falls in a defined area during a specific period of time.
- Weather Radar Systems: by using radio waves to detect water particles suspended in the atmosphere, radar allows for obtaining a dynamic and high spatial resolution view of ongoing rainfall, overcoming the limitations of the surface station network.
Rainfall data was retrieved through the AllertaCAL platform, developed by the Functional Meteorological, Hydrographic, and Oceanographic Center of the Calabria Region. AllertaCAL collects and publicly exposes data from approximately 260 weather stations scattered throughout the regional territory. The stations are equipped with sensors such as rain gauges, thermometers, hydrometers, barometers, hygrometers, etc. The collected data is made available both in aggregated and point form. The information system provides REST services that allow access to the collected data, as well as the list of weather stations and the sensors present in each of them.
Regarding radar data, the Radar-DPC platform of the Civil Protection Department (DPC) of the Presidency of the Council of Ministers was used. The Platform adopts a very flexible approach and has been designed to manage, even incrementally, geo-referenced products of different natures and semantics. The Radar-DPC platform integrates a stack of REST (Representational State Transfer) services, used both by the back-end of the applications and as APIs (Application Programming Interface) for downloading radar data. These services also allow interaction with third-party applications, facilitating access and processing of the information provided. All services are accessible from this URL.
The data acquisition component was implemented using the PlugAIn platform, developed by Revelis.
The Data Analytics module includes the following three modules:
- Data Preprocessing;
- Data Sampling;
- Model Building.
The Data Preprocessing module offers cleaning methods to handle data issues such as missing values, outliers, and noise, with different strategies for each.
The framework includes a Sampler module that addresses class imbalance through under-sampling, creating a preprocessed dataset for the REM (Rainfall Estimation Model) Builder module. This module utilizes machine learning, deep learning, and deep ensemble learning algorithms to develop models capable of identifying complex patterns in rainfall data. In particular, a probabilistic hybrid ensemble classifier (HPEC), based on Random Forest and Stacking, has been proposed, along with deep learning models such as feed-forward neural networks and a deep ensemble learning approach to optimize predictions.
The combination of the data extraction and integration module with the estimation methods ensures a smooth and well-coordinated operation of the framework, creating the conditions for subsequent evaluation and validation processes. In these phases, the results obtained are crucial for refining the solution, optimizing its performance, and preparing it for real-world application.
ADELE4RAIN: the objectives
Having explored the technical details regarding the platform’s design and the methodologies used to integrate data from heterogeneous sources, it is evident that this solution represents a significant advancement in rainfall monitoring and forecasting. The combination of advanced machine learning and deep learning algorithms, real-time data processing, and the use of innovative technologies allows for increasingly accurate and timely predictions.
However, the benefits of this platform extend far beyond the technical aspect, with a direct impact on:
- management of extreme weather events;
- protection of natural resources;
- improvement of community resilience;
- prevention of environmental disasters.
At this point, it is useful to reflect on the potential impacts and future developments that this platform could enable. By providing accurate and timely forecasts, the project aims to allow infrastructure managers and decision-makers to adopt proactive measures to:
- protect infrastructure assets;
- reduce vulnerabilities;
- improve resilience to climate-related risks.
The project aims to improve agricultural management by optimizing resource use. By providing farmers with precise information on rainfall patterns and trends, the intention is to enhance irrigation scheduling, crop management, and water resource strategies, increasing productivity and promoting sustainable agricultural practices.
The project has the objective of enhancing early warning systems, evacuation plans, and emergency responses, with the intent of reducing risks to people, property, and the economy during extreme weather events.
Conclusions
In conclusion, the creation of an advanced platform for estimating and forecasting rainfall is fundamental for addressing the challenges arising from climate change and extreme weather events for the prevention of environmental disasters. Looking to the future, the adoption of solutions like this is therefore essential for building a more resilient society, ready to face climate-related challenges, and promoting sustainable and proactive adaptation to constantly evolving weather conditions.
Author Emanuela Tarantino
