diff --git "a/0tAzT4oBgHgl3EQfRPvU/content/tmp_files/load_file.txt" "b/0tAzT4oBgHgl3EQfRPvU/content/tmp_files/load_file.txt" new file mode 100644--- /dev/null +++ "b/0tAzT4oBgHgl3EQfRPvU/content/tmp_files/load_file.txt" @@ -0,0 +1,1760 @@ +filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf,len=1759 +page_content='Comparison of tree-based ensemble algorithms for merging satellite and earth-observed precipitation data at the daily time scale Georgia Papacharalampous1, Hristos Tyralis2, Anastasios Doulamis3, Nikolaos Doulamis4 1 Department of Topography, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, Iroon Polytechniou 5, 157 80 Zografou, Greece (papacharalampous.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='georgia@gmail.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='com, https://orcid.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='org/0000-0001-5446-954X) 2 Department of Topography, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, Iroon Polytechniou 5, 157 80 Zografou, Greece (montchrister@gmail.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='com, hristos@itia.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='ntua.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='gr, https://orcid.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='org/0000-0002-8932- 4997) 3 Department of Topography, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, Iroon Polytechniou 5, 157 80 Zografou, Greece (adoulam@cs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='ntua.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='gr, https://orcid.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='org/0000-0002-0612-5889) 4 Department of Topography, School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, Iroon Polytechniou 5, 157 80 Zografou, Greece (ndoulam@cs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='ntua.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='gr, https://orcid.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='org/0000-0002-4064-8990) Abstract: Merging satellite products and ground-based measurements is often required for obtaining precipitation datasets that simultaneously cover large regions with high density and are more accurate than pure satellite precipitation products.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Machine and statistical learning regression algorithms are regularly utilized in this endeavour.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' At the same time, tree-based ensemble algorithms for regression are adopted in various fields for solving algorithmic problems with high accuracy and low computational cost.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' The latter can constitute a crucial factor for selecting algorithms for satellite precipitation product correction at the daily and finer time scales, where the size of the datasets is particularly large.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Still, information on which tree-based ensemble algorithm to select in such a case for the contiguous United States (US) is missing from the literature.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' In this work, we conduct an extensive comparison between three tree-based ensemble algorithms, specifically random forests, gradient boosting machines (gbm) and extreme gradient boosting (XGBoost), in the context of interest.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' We use daily data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and the IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded 2 datasets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' We also use earth-observed precipitation data from the Global Historical Climatology Network daily (GHCNd) database.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' The experiments refer to the entire contiguous US and additionally include the application of the linear regression algorithm for benchmarking purposes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' The results suggest that XGBoost is the best-performing tree- based ensemble algorithm among those compared.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' They also suggest that IMERG is more useful than PERSIANN in the context investigated.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Keywords: contiguous US;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' gradient boosting machines;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' IMERG;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' machine learning;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' PERSIANN;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' random forests;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' remote sensing;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' satellite precipitation correction;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' spatial interpolation;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' XGBoost 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Introduction Machine and statistical learning algorithms (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=', those documented in Hastie et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2009;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' James et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2013;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Efron and Hastie 2016) are increasingly adopted for solving a variety of practical problems in hydrology (Dogulu et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2015;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Xu et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2018;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Quilty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2019;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Curceac et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2020;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Jehn et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2020;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Quilty and Adamowski 2020;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Rahman et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2020;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Althoff et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2021;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Fischer and Schumann 2021;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Papacharalampous and Tyralis 2022b) and beyond (Ahmed et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2010;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' García-Gutiérrez et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2015;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Goetz et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2015;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Asri et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2016;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Idowu et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2016;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Bahl et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2018;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Feng et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2020;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Khanam and Foo 2021;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Rustam et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2021;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Bamisile et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Among the entire pool of such algorithms, the tree-based ensemble ones (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=', those combining decision trees under properly designed ensemble learning strategies;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Sagi and Rokach 2018) are of special interest for many practical problems, as they can offer high predictive performance with low computational cost, among their remaining benefits (Tyralis et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2019;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Tyralis and Papacharalampous 2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Still, the known theoretical properties of the various tree-based ensemble algorithms (including random forests, gradient boosting machines − gbm and extreme gradient boosting – XGBoost;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Breiman 2001, Friedman 2001, Chen and Guestrin 2016) cannot support the selection of the most appropriate one among them for each practical problem.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Instead, such a selection could rely on attentively designed empirical comparisons.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Thus, such comparisons of tree-based ensemble algorithms are conducted with increasing frequency in various scientific fields (Adler et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2011;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Ahmad et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2018;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Fan et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2018;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Besler et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2019;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Ahmad and Zhang 2020;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Ampomah et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2020;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Liu et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2020;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Rahaman et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2021;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Ziane et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2021;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Khorrami et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2022;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Mittendorf e al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2022;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Park and Kim 2022;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Wei et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2022;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Yao et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 3 Tree-based ensemble algorithms are regularly applied and compared to other machine and statistical learning algorithms for the task of merging satellite products and ground- based measurements.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' This task is the general focus of this work, together with the general concept of tree-based ensemble algorithms, and is commonly executed in the literature in the direction of obtaining precipitation datasets that cover large geographical regions with high density and, simultaneously, are more accurate than pure satellite precipitation products.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' The importance of this same task could be perceived through the inspection of the major research topics appearing in the hydrological literature (see, e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=', those discussed in Montanari et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2013, Blöschl et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Relevant examples of applications and comparisons are available in He et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' (2016), Meyer et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' (2016), Baez-Villanueva et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' (2020), Chen et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2021, Nguyen et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' (2021), Shen and Yong (2021), Zhang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' (2021), Fernandez-Palomino et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' (2022), Lei et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' (2022), Lin et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' (2022), Zandi et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' (2022) and Militino et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' (2023).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' These examples refer to various temporal resolutions and many different geographical regions around the globe (see also the reviews by Hu et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2019 and Abdollahipour et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2022), with the daily temporal resolution and the Unites States (US) being frequent cases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Nonetheless, a relevant comparison of tree-based ensemble algorithms for the latter temporal resolution and the latter geographical region is missing from the literature, with the closest investigations at the moment being those available in the work by Lei et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' (2022), which however focus on China.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' In this work, we fill this specific literature gap.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Notably, the selection of the most accurate regression algorithm from the tree-based ensemble family could be particularly useful at the daily temporal scale, in which the size of the datasets for large geographical areas might impose significant limitations on the application of other accurate machine and statistical learning regression algorithms due to their large computational cost.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' The remainder of the paper is structured as follows: Section 2 describes the tree-based ensemble algorithms compared in this work.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' It also describes a machine learning metric that is utilized for ensuring some degree of explainability.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Moreover, Section 3 presents the data retrieved and utilized for the comparisons.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' The same section outlines the way in which the tree-based ensemble algorithms are compared with each other.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Furthermore, Sections 4, 5 and 6 present the results, provide their discussion in light of the literature and conclude the work, respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Lastly, Appendix A provides statistical software information that assures the work’s reproducibility.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 4 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Methods Random forests, gradient boosting machines (gbm) and extreme gradient boosting (XGBoost) were applied in a cross-validation setting (see Section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='2) for conducting an extensive comparison in the context of merging gridded satellite products and gauge- based measurements at the daily time scale.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Additionally, the linear regression algorithm was applied in the same setting for benchmarking purposes.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' In this section, we provide brief descriptions of the four afore-mentioned algorithms.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Extended descriptions are out of the scope of this work, as they are widely available in the machine and statistical learning literature (e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=', in Hastie et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2009;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' James et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2013;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Efron and Hastie 2016).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='1 Linear regression The results of this work are reported in terms of relative scores (see Section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='3).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' These scores were computed with respect to the linear regression algorithm, which models the dependent variable as a linear weighted sum of the predictor variables (Hastie et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2009, pp 43–55).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' A squared error scoring function facilitates this algorithm’s fitting.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='2 Random forests Random forests (Breiman 2001) are the most commonly used algorithm in the context of merging gridded satellite products and gauge-based measurements (see the examples in Hengl et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' A detailed description of this algorithm can be found in Tyralis et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' (2019b), along with a systematic review of its application in water resources.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Notably, random forests are an ensemble learning algorithm and, more precisely, an ensemble of regression trees that is based on bagging (acronym for “bootstrap aggregation”) but with an additional randomization procedure.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' The latter aims at reducing overfitting.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' In this work, random forests were implemented with all their hyperparameters kept as default.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' For instance, the number of trees was equal to 500.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='3 Gradient boosting machines Gradient boosting machines (Friedman 2001, Mayr et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2014) are also an ensemble learning algorithm that is herein used with regression trees as base learners.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' The main concept behind this ensemble algorithm and, more generally, behind all the boosting algorithms (including the one described in Section 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='4) is the iterative training of new base learners using the errors of previously trained base learners (Natekin and Knoll 2013, Tyralis and Papacharalampous 2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' For gradient boosting machines, a gradient 5 descent algorithm adapts the loss function for achieving optimal fitting.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' This loss function is the squared error scoring function herein.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Consistency with the respect to the implementation of random forests is ensured by setting the number of trees equal to 500.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' The remaining hyperparameters were kept as default.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='4 Extreme gradient boosting Extreme gradient boosting (XGBoost;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Chen and Guestrin 2016) consists the third tree- based ensemble learning and the second boosting algorithm implemented in this work.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' In the implementations of this work, all the hyperparameters were kept as default, except for the maximum number of iterations that were set to 500.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Aside from applying XGBoost in a cross-validation setting for its comparison to the remaining algorithms, we also utilized it with the same hyperparameter values for assuring some degree of explainability in terms of variable importance under the more general explainable machine learning culture (Linardatos et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2020, Roscher et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2020, Belle and Papantonis 2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Specifically, we computed the gain importance metric, which is available in the XGBoost algorithm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' This metric assesses the “fractional contribution of each feature to the model based on the total gain of this feature’s splits”, with higher values indicating more important features (Chen et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2022c).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Data and application 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='1 Data For our experiments, we retrieved and used daily earth-observed precipitation, gridded satellite precipitation and elevation data for the gauged locations and grid points shown in Figures 1 and 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 6 Figure 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Map of the geographical locations of the earth-located stations that offered data for this work.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 120 100 80 Longitude (°)3itude97 Figure 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Maps of the geographical locations of the points composing the (a) PERSIANN and (b) IMERG grids utilized in this work.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 120 100 80 Longitude (°)(b)8(a)&58 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='1 Earth-observed precipitation data Daily precipitation totals from the Global Historical Climatology Network daily (GHCNd) (Durre et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2008, 2010, Menne et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2012) were used for comparing the algorithms.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' More precisely, data from 7 264 earth-located stations spanning across the contiguous United States (see Figure 1) were extracted.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' This data cover the two-year time period 2014−2015.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Data retrieval was made from the website of the NOAA National Climatic Data Center (https://www1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='ncdc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='noaa.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='gov/pub/data/ghcn/daily;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' assessed on 2022-02-27).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='2 Satellite precipitation data For comparing the algorithms, we additionally used gridded satellite daily precipitation data from the current operational PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) system (see the geographical locations of the extracted PERSIANN grid with a spatial resolution of 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='25 degree x 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='25 degree in Figure 2a) and the GPM IMERG (Integrated Multi-satellitE Retrievals) Late Precipitation L3 1 day 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='1 degree x 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='1 degree V06 (see the geographical locations of the extracted IMERG grid in Figure 2b).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' These two gridded satellite precipitation databases were developed by the Centre for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine (UCI) and the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC), respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' More precisely, the PERSIANN data were retrieved from the website of the Center for Hydrometeorology and Remote Sensing (CHRS) (https://chrsdata.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='eng.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='uci.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='edu;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' assessed on 2022-03-07) and the IMERG data were retrieved from the website of NASA (National Aeronautics and Space Administration) Earth Data (https://doi.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='org/10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='5067/GPM/IMERGDL/DAY/06;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' assessed on 2022-12- 10).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' The extracted data cover the entire contiguous United States at the two-year time period 2014−2015.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='3 Elevation data Elevation is a key predictor variable for many hydrological processes (Xiong et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 2022).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Therefore, we estimated its value for all the geographical locations shown in Figures 1 and 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' For this estimation, we relied on the Amazon Web Services (AWS) Terrain Tiles (https://registry.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='opendata.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='aws/terrain-tiles;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' assessed on 2022-09-25).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content='2 Validation setting and predictor variables To formulate the regression settings of this work, we first defined earth-observed daily 9 total precipitation at a point of interest (which could be station 1 in Figure 3) as the dependent variable.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Then, we adopted procedures proposed in Papacharalampous et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' (2023) to compute the observations of possible predictor variables.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' Separately for each of the two satellite precipitation grids (see Figure 2), we determined the closest four grid points to each ground-based station from those depicted in Figure 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/0tAzT4oBgHgl3EQfRPvU/content/2301.01214v1.pdf'} +page_content=' We also computed the distances di, i = 1, 2, 3, 4 from these grid points and indexed the latter as Si, i = 1, 2, 3, 4 based on the following order: d1 < d2