Go to DBLP homepageKDD 2024 Special Day - AI for Environment
Abstract: Environmental problems such as air pollution monitoring and prevention, flood detection and prevention, land use, forest management, river water quality, wastewater treatment supervision, etc. are more complex than typical real-world problems usually AI faces to. This added complexity rises from several aspects, such as the randomness shown by most of environmental processes involved, the 2D/3D nature of involved problems, the temporal aspects, the spatial aspects, the inexactness of the information, etc.In fact, environmental problems belong to the most difficult problems with a lot of inexactness and uncertainty, and possibly conflicting objectives to be solved according to several classifications such as the one by Funtowicz & Ravetz (Funtowicz & Ravetz, 1999), which states that there are 3 kinds of problems. Also, they are non-structured problems in the classification proposed by H. Simon (Simon, 1966).All this complexity means that to effectively solve those problems a lot of knowledge is needed. This knowledge can be theoretical knowledge expressed in mechanistic models, such as the Gravidity Newton's Theory, or it can be empirical knowledge that can be expressed by means of empirical models, originated by some data and observations (data-driven knowledge) or by the expertise gathered by people when coping with such problems (model-driven knowledge, particularly expert-based knowledge).The KDD 2024 Special Day for AI for environment brings together researchers and practitioners to present their perspective on this very timely topic on how AI can be used for good, and improving the environment where we all live in.
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