According to data from Legambiente’s Ecosistema Rischio 2017, hydrogeological risk makes Italy increasingly insecure due to climate changes that amplify the effects of landslides and floods. At least 7.5 million citizens live and work in areas at risk. In 70% of the municipalities surveyed there are homes in such areas, in 27% entire neighborhoods and in 15% schools and hospitals. Over the last 5 years, the country has recorded 102 extreme environmental events that have caused floods or landslides, leading to the need to call for a state of emergency on 56 occasions, with a very high total cost in terms of human lives and an outlay of 7.6 billion euros.
These figures clearly highlight the absolute need for research and innovation projects that can preempt these phenomena, limit them whenever possible but – above all – prevent them from causing further victims, as well as develop methods that allow these projects to be managed with maximum efficiency.
To what extent does digital increase sensitivity to environmental problems?
In recent years digitization processes have played a predominant role and coalesced the attention of research and technological investments. The increasingly mobile and ever active universe of today is driving a fundamental change in our digital culture. It is expected that there will be 5 billion mobile phone users by 2019 and 20 billion IoT devices by 2020. The phenomenon is characterized by pervasiveness and strong integration in every moment of our life. The boundaries between the analogical and digital world are becoming increasingly fluid, also due to voice recognition functions that make it difficult to identify the digital component.
Right now, a new sensitivity to the problems of the physical world – and in particular to the problems of the environment – is growing. The widespread technology of the Internet of Things makes it possible to realize environmental monitoring solutions for predicting and controlling disastrous events such as rock falls, avalanches and fires.
Awareness of technology is growing not only as an aid in controlling the forces of a physical world that frightens us, but of technology as a glue for bringing humans closer to nature, triggering a virtuous path for the promotion of ethical and environmentally friendly behavior and the development of a strategy aimed at sustainability.
Digital innovation can help us create a better world and restore well-being to the community by proposing easily applicable methods, using technologically advanced but easy-to-use tools at affordable costs, involving citizens of all ages, disadvantaged people, institutions, schools, businesses, associations, the scientific world and local communities.
IoT, AI, analysis of allied environmental data
Digital transformation is based on some fundamental elements: the Intelligence of Things, the ability to accumulate large amounts of data, the ability to analyze these data and obtain predictions on the basis of which to carry out actions that are both preventive and of assistance in the life of people and the economy.
IoT is based on the diffusion of connected devices, a multitude of real objects that are networked, which perceive the environment through their sensors, know what is happening around and intercept human behavior. Through their wired logic, they collect pieces of information, compare them and can perform actions autonomously.
The large amount of data generated by intelligent sensors needs to be stored for subsequent processing in the data centers of cloud service providers, possibly passing through a support infrastructure, called edge computing, when there is a need for computational power precisely in the vicinity of the place where there are machines and sensors. Data are analyzed with different techniques according to the need.
Using paradigms of Artificial Intelligence, machines are able to learn, learn autonomously, speak, evaluate a situation and control objects so that they act autonomously.
The data center as enabler of a services ecosystem for the environment
Data management is one of the main evolutionary drivers and Intel has outlined a “Data Center first” world view, predicting that the amount of data we will see generated over the next two years will be 10 times greater than the data generated so far. The centrality of data in development of the digitization process leads to the definition of strategic guidelines dedicated to Information & Analytics, with the aim of enhancing information assets in order to extract information and knowledge from the data to be made available to business functions through quick and informed decisions. These challenges require constant renewal of the physical ecosystem of the Data Center in order to offer acceleration and speed, competitiveness and the achievement of market targets. On the more traditional requirements of high performance, reliability and security are grafted elements of wider scope such as: the need to simplify architectures, making them programmable and reconfigurable in an automated way; the need to operate in an extended and heterogeneous context that includes multi-cloud resources and implementations; and the need to guarantee data management and visibility tools appropriate to the relevance of platforms supporting digital transformation.
Cloud management platforms with interoperability functions facilitate the establishment of digital service ecosystems addressed to different sectors, such as the public administration, education, health and business. All these sectors in fact share the need to have flexible applications available that scale easily to meet their growing needs.
Which digital services support environmental risk management?
There are many virtuous examples of digital application aimed at managing environmental risk in an optimal manner. The following is a short list of the systems already in use in different realities, which will be followed in coming weeks by an in-depth look at specific case histories:
- environmental monitoring systems for air quality and water analysis;
- IoT for the control of infrastructure stability;
- mixed (virtual/augmented) reality techniques for supporting the players involved in management of the territory and of emergency phases, including in planning activities;
- systems based on IoT technology and advanced analytics for monitoring avalanches and rock falls, integrated with the collection of historical data, meteorological data and snowfall measurement surveys, capable of predicting risk situations;
- IoT for the control of safety in road works or in the reservoirs of hydroelectric plants;
- techniques of gamification for social involvement and the creation of social communities;
- monitoring systems based on satellite photo analysis for intercepting potential areas at risk of fire or at risk of excessive snowfall;
- IoT of crop monitoring in order to ensure optimal management and decrease the number of phytotherapeutic treatments;
- crowdsourcing techniques for encouraging the collection of images to be processed in order to automatically identify events of interest for the management of emergencies;
- wearable systems designed to monitor the safety of people operating in areas at risk, such as firefighters and rescue workers;
- telepresence for ensuring coverage of medical assistance services in areas that are difficult to reach;
- communication systems for managing emergency situations that use alternative transmission channels when the main ones are interrupted;
- systems addressed to management of physical safety using systems based on intelligent of video surveillance apparatuses as well as “head counts”;
The new digital services pass quickly from the prototypical research phase to the industrialized and standardized solution phase, and the commitment of a large network of companies and technology suppliers is to widen the offer even more. The most challenging element of innovation that unites all services is the development of artificial intelligence algorithms based on a diversified set of paradigms and techniques. From time to time, it is necessary to use the appropriate tool for the appropriate task: decision trees, Machine Learning, Deep Learning with neural networks, genetic algorithms. Automatic reasoning, based on a training phase, makes it possible to perform effective cause/effect analyses and predict future events for which mitigation or remedial actions can be implemented.
In order to support artificial intelligence, the modern data center will have to make available systems of interoperability and data exchange and will have to equip infrastructures with hybrid services, delivered in cloud mode and similar to those of High Performance Computing. Although this paradigm of concentration of large amounts of data and processing power will involve multiple services, it should be noted that, in some application areas, there will also be the reverse movement of information and processing logic according to the pattern of fog computing towards structures at the limit of field of action of devices (edge computing). The future will not be characterized by the standardization of services, but by their extreme diversification according to the fields of applications, hopefully characterized by the great ability to interoperate thanks to standardization.