Machine learning for ecosystem services
نویسندگان
چکیده
منابع مشابه
Machine Learning in Ecosystem Informatics
The emerging field of Ecosystem Informatics applies methods from computer science and mathematics to address fundamental and applied problems in the ecosystem sciences. The ecosystem sciences are in the midst of a revolution driven by a combination of emerging technologies for improved sensing and the critical need for better science to help manage global climate change. This paper describes se...
متن کاملMachine Learning in Ecosystem Informatics and Sustainability
Ecosystem Informatics brings together mathematical and computational tools to address scientific and policy challenges in the ecosystem sciences. These challenges include novel sensors for collecting data, algorithms for automated data cleaning, learning methods for building statistical models from data and for fitting mechanistic models to data, and algorithms for designing optimal policies fo...
متن کاملMachine Learning for Annotating Semantic Web Services
Emerging Semantic Web standards promise the automated discovery, composition and invocation of Web Services. Unfortunately, this vision requires that services describe themselves with large amounts of hand-crafted semantic metadata. We are investigating the use of machine learning techniques for semi-automatically classifying Web Services and their messages into ontologies. From such semantical...
متن کاملFishing for ecosystem services.
Ecosystems are commonly exploited and manipulated to maximize certain human benefits. Such changes can degrade systems, leading to cascading negative effects that may be initially undetected, yet ultimately result in a reduction, or complete loss, of certain valuable ecosystem services. Ecosystem-based management is intended to maintain ecosystem quality and minimize the risk of irreversible ch...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Ecosystem Services
سال: 2018
ISSN: 2212-0416
DOI: 10.1016/j.ecoser.2018.04.004