Towards a generic theoretical framework for pattern-based LUCC modeling
نویسندگان
چکیده
Several modeling strategies have been proposed to study Land Use and Cover changes (LUCC). However, substantial discrepancies noted between different models for the same problem, questioning their overall reliability reproducibility. To address this challenge, we elaborate a generic, formally correct, theoretical framework pattern-based LUCC modeling, which is implemented in our own software, CLUMPY (Comprehensive [and cover] Modeling PYthon). The present work focuses on calibration. We devise kernel density calibration-estimation method (Bayes-eKDE) that shown synthetic artificial data be both accurate algorithmically efficient. also introduce generic evaluation allows us compare calibration efficiency of existing models. gain precision computational time precisely quantified way. • Accurate methods are needed ensure reliability. A based Kernel Density Estimation has developed (Bayes-eKDE). Our substantially more efficient compared ones. software Model Python). Comparison validation must performed explanatory variable space, not from map comparison.
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ژورنال
عنوان ژورنال: Environmental Modelling and Software
سال: 2022
ISSN: ['1364-8152', '1873-6726']
DOI: https://doi.org/10.1016/j.envsoft.2022.105551