کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6539915 | 1421104 | 2018 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Generalizability of gene expression programming and random forest methodologies in estimating cropland and grassland leaf area index
ترجمه فارسی عنوان
تعاریف برنامه نویسی بیان ژن ها و متدولوژی های جنگل تصادفی در برآورد شاخص سطح برگ در مزرعه
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کلمات کلیدی
داده های هواشناسی، شاخص منطقه برگ، برنامه نویسی بیان ژن، جنگل تصادفی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
Leaf Area Index (LAI) is a very important structural attribute of ecosystems which affects the energy, water and carbon exchanges between the land surface and atmosphere. Direct measurement of LAI is costly and time consuming so indirect measurement approaches have been developed for determining its magnitude. The present paper aimed at modeling LAI in cropland and grassland sites using the available meteorological data through two heuristic data driven techniques, namely, gene expression programming (GEP) and random forest (RF). Different data set organizations were designed using local (temporal) and external (spatial) norms to provide a thoroughgoing data scanning strategy. The results showed that the external GEP and RF models (EGEP and ERF) might be suitable approaches for modeling LAI by average scatter index (SI) values of 0.275 and 0.270 (for cropland) and 0.273 and 0.279 (for grassland) when compared to the local GEP and RF models with average SI values of 0.207 and 0.204 (cropland), and 0.249 and 0.204 (grassland), respectively. The presented methodology allowed the evaluation in each site of models developed (trained) using local patterns and the models developed using the exogenous data (patterns from ancillary sites).
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers and Electronics in Agriculture - Volume 144, January 2018, Pages 232-240
Journal: Computers and Electronics in Agriculture - Volume 144, January 2018, Pages 232-240
نویسندگان
Sepideh Karimi, Ali Ashraf Sadraddini, Amir Hossein Nazemi, Tongren Xu, Ahmad Fakheri Fard,