Analysis of Data Driven Modelling in Ecosystem Services
Machine Learning
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Data-driven modelling is the area of hydro informatics undergoing fast development. This book reviews the main concepts and approaches of data-driven modelling, which is based on computational intelligence and machine-learning methods. A brief overview of the main methods - neural networks, fuzzy rule-based systems and genetic algorithms. Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behavior of a system by computing and exploiting correlations between observed variables within it. Machine learning ...
Data-driven modelling is the area of hydro informatics undergoing fast development. This book reviews the main concepts and approaches of data-driven modelling, which is based on computational intelligence and machine-learning methods. A brief overview of the main methods - neural networks, fuzzy rule-based systems and genetic algorithms. Recent developments in machine learning have expanded data-driven modelling (DDM) capabilities, allowing artificial intelligence to infer the behavior of a system by computing and exploiting correlations between observed variables within it. Machine learning algorithms may enable the use of increasingly available 'big data' and assist applying ecosystem service models across scales, analyzing and predicting the flows of these services to disaggregated beneficiaries.
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