Environmental software are computer tools that collect, analyse and visualise data from the environment. This data can come from a variety of sources, such as sensors, satellites, weather stations and government agencies. Their main objective is to help public and private agencies make more informed decisions for the protection of the environment.
Artificial intelligence (AI), instead, can:
- help interpret data;
- improve the accuracy of weather forecasts;
- identify areas where environmental restoration work is needed.
Table of Contents
Environmental software types
There are many environmental software that use artificial intelligence to fight rising waste and thus ensure a more sustainable future. Let’s discover the most common ones together.
Environmental monitoring systems
Environmental monitoring systems help users monitor the environment by means of sensors that collect data on air quality, water quality, temperature and the amount of solar radiation.
Environmental modelling software
Environmental modelling software works with specific algorithms capable of predicting the behaviour of the environment under different scenarios. For example, the impact of a climate change on biodiversity or that of a development project on water resources.
Environmental data analysis software
Environmental data analysis software is based on in-depth analysis to identify trends and patterns in the environment. They can identify air pollution trends in a given geographical area or predict global temperature patterns.
Examples of AI applied to environmental software
An example of how AI can be applied to environmental software is the case of the Atmosphere platform, created by the company Clarity Movement.
Atmosphere employs a network of sensors to monitor air quality in real time and AI to analyse the collected data. In doing so, the system is able to identify sources of air contamination and suggest solutions to improve air quality.
A second example is the Rainforest Connection project, which employs acoustic sensors and AI to monitor rainforests and prevent illegal deforestation. In practice, the sensors detect the sounds of loggers and the noises associated with deforestation processes; at the same time, the AI analyses the data and identifies the areas most at risk in order to alert the authorities if necessary.
The challenges that AI must overcome to be even more effective
AI still has to overcome many challenges to call itself mature and make environmental software more effective. Let’s see in detail what this is all about.
Data quality
AI relies on the quality of the data, so the data must be as accurate and representative as possible. This is not always easy to achieve, especially considering that sensors can generate incomplete data.
Data Interpretation
In order to interpret data correctly, the AI must be able to recognise the relationships between data and results.
Computational limitations
AI requires a lot of resources, which is a problem if the communication networks and data processing infrastructure are computationally limited.
Privacy and security
It may happen that the environmental data collected contain sensible information, such as the location of people or animals. The AI must then protect the data and ensure that they are only used for authorised purposes.
Accessibility and inclusion
AI is an expensive and complex tool to use, precluded to many regions of the world. But yet, AI can be a very important resource for environmental software.
Indeed, with the advancement of technology and collaboration between AI experts and environmentalists, it is possible to overcome the aforementioned challenges and employ AI more effectively for environmental protection, ensuring accessibility and inclusion.
How to make AI algorithms inclusive and not discriminatory
In order for the algorithms used in environmental software to be inclusive and not discriminatory, it is necessary to adopt a holistic approach that covers every aspect of AI decision-making, from the training of the algorithm to its implementation and monitoring.
We will shortly look at some holistic approaches that can be used by AI.
Data diversity
Data diversity is important: the data must represent the diversity of people living in a specific geographical area or affected by an environmental problem.
Data analysis
Equally important is data analysis to train algorithms to identify biases or inequalities. This can prevent the same algorithms from reproducing or, worse, amplifying existing inequalities.
Selection of attributes
The selection of algorithm attributes should be conducted carefully and based on objective criteria. It is best to avoid attributes that in any way discriminate, such as race or religion.
Algorithms validation
Algorithms must be validated so that they do not discriminate. This is done using sensitivity tests, which test the codes on different population groups.
Transparency and responsibility
Algorithms must follow the principles of transparency and responsibility. Only then can users fully understand how AIs make decisions and achieve their results.
Stakeholders involvement
Stakeholders, e. g. local communities, must absolutely be involved in the AI decision-making process. This can help to ensure that algorithms are more attentive to the needs of local communities and, above all, that there are no inequalities.
Furthermore, it is important that AI experts work closely with environmental experts to ensure that algorithms are developed taking into account the needs of the territory and the complexity of the problems affecting it.
In short: ensuring that the algorithms developed for environmental software are truly inclusive and not discriminatory requires a holistic approach that takes into account every aspect of AI decision making, from the writing of the code to its implementation and monitoring.
The combination of artificial intelligence and environmental software can help institutions and companies make more informed decisions to protect the environment.
Thanks to AI, it is now possible to analyse large amounts of environmental data and identify areas where environmental remediation is needed. All would lead to better monitoring and protection of the ecosystem.
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