Ángeles Saavedra

1.2k total citations
56 papers, 876 citations indexed

About

Ángeles Saavedra is a scholar working on Artificial Intelligence, Environmental Engineering and Statistics, Probability and Uncertainty. According to data from OpenAlex, Ángeles Saavedra has authored 56 papers receiving a total of 876 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 16 papers in Environmental Engineering and 10 papers in Statistics, Probability and Uncertainty. Recurrent topics in Ángeles Saavedra's work include Geochemistry and Geologic Mapping (13 papers), Soil Geostatistics and Mapping (9 papers) and Thermochemical Biomass Conversion Processes (7 papers). Ángeles Saavedra is often cited by papers focused on Geochemistry and Geologic Mapping (13 papers), Soil Geostatistics and Mapping (9 papers) and Thermochemical Biomass Conversion Processes (7 papers). Ángeles Saavedra collaborates with scholars based in Spain, Portugal and Ecuador. Ángeles Saavedra's co-authors include Enrique Granada, J. Taboada, Elena Arce, J.L. Mı́guez, Ricardo Cao, Celestino Ordóñez, Antonio Vaamonde Liste, Pablo Eguía, M. Gloria Fiestras-Janeiro and David Araújo‐Vilar and has published in prestigious journals such as SHILAP Revista de lepidopterología, Renewable and Sustainable Energy Reviews and Chemosphere.

In The Last Decade

Ángeles Saavedra

53 papers receiving 844 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ángeles Saavedra Spain 18 166 153 108 102 95 56 876
Jiansong Wu China 27 80 0.5× 241 1.6× 342 3.2× 132 1.3× 67 0.7× 96 1.7k
J.M. Matías Spain 24 218 1.3× 184 1.2× 50 0.5× 93 0.9× 30 0.3× 65 1.3k
Elçin Kentel Türkiye 18 225 1.4× 261 1.7× 93 0.9× 29 0.3× 20 0.2× 44 1.4k
Guozhong Zheng China 20 60 0.4× 233 1.5× 396 3.7× 102 1.0× 40 0.4× 72 1.5k
Eun Sug Park United States 24 70 0.4× 200 1.3× 303 2.8× 85 0.8× 24 0.3× 135 1.9k
Weizhang Liang China 20 166 1.0× 68 0.4× 105 1.0× 175 1.7× 59 0.6× 64 1.7k
Song-Shun Lin China 16 60 0.4× 96 0.6× 149 1.4× 48 0.5× 21 0.2× 26 1.2k
Ana Suárez Sánchez Spain 14 62 0.4× 134 0.9× 94 0.9× 96 0.9× 46 0.5× 21 671
Abbas Roozbahani Iran 25 93 0.6× 404 2.6× 68 0.6× 49 0.5× 12 0.1× 67 1.6k

Countries citing papers authored by Ángeles Saavedra

Since Specialization
Citations

This map shows the geographic impact of Ángeles Saavedra's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ángeles Saavedra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ángeles Saavedra more than expected).

Fields of papers citing papers by Ángeles Saavedra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ángeles Saavedra. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ángeles Saavedra. The network helps show where Ángeles Saavedra may publish in the future.

Co-authorship network of co-authors of Ángeles Saavedra

This figure shows the co-authorship network connecting the top 25 collaborators of Ángeles Saavedra. A scholar is included among the top collaborators of Ángeles Saavedra based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ángeles Saavedra. Ángeles Saavedra is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Olivieri, David N., et al.. (2025). Multivariate functional data analysis and machine learning methods for anomaly detection in water quality sensor data. Environmental Modelling & Software. 190. 106443–106443.
2.
Saavedra, Ángeles, et al.. (2021). Relationships Between Phonological Awareness and Reading in Spanish: A Meta‐Analysis. Language Learning. 72(1). 113–157. 28 indexed citations
3.
Saavedra, Ángeles, et al.. (2021). AI Approaches to Environmental Impact Assessments (EIAs) in the Mining and Metals Sector Using AutoML and Bayesian Modeling. Applied Sciences. 11(17). 7914–7914. 17 indexed citations
4.
Saavedra, Ángeles, et al.. (2019). Differentiating between fatal and non-fatal mining accidents using artificial intelligence techniques. International Journal of Mining Reclamation and Environment. 34(10). 687–699. 8 indexed citations
5.
Abad, Alberto, et al.. (2018). A Bayesian assessment of occupational health surveillance in workers exposed to silica in the energy and construction industry. Environmental Science and Pollution Research. 26(29). 29560–29569. 7 indexed citations
6.
Saavedra, Ángeles, et al.. (2017). Risk Analysis in Tunnel Construction with Bayesian Networks Using Mutual Information for Safety Policy Decisions. WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS. 14. 3 indexed citations
7.
Eguía, Pablo, et al.. (2016). Improving transient thermal simulations of single dwellings using interpolated weather data. Energy and Buildings. 135. 212–224. 6 indexed citations
8.
Martín, J.E., et al.. (2016). Bayesian Decision Tool for the Analysis of Occupational Accidents in the Construction of Embankments. Journal of Construction Engineering and Management. 143(2). 37 indexed citations
9.
Sierra, Carlos, Celestino Ordóñez, Ángeles Saavedra, & J.R. Gallego. (2014). Element enrichment factor calculation using grain-size distribution and functional data regression. Chemosphere. 119. 1192–1199. 12 indexed citations
10.
Arce, Elena, et al.. (2013). Biomass Fuel and Combustion Conditions Selection in a Fixed Bed Combustor. Energies. 6(11). 5973–5989. 26 indexed citations
11.
Granada, Enrique, et al.. (2010). Heterogenic Solid Biofuel Sampling Methodology and Uncertainty Associated with Prompt Analysis. International Journal of Molecular Sciences. 11(5). 2118–2133. 13 indexed citations
12.
Taboada, J., et al.. (2008). Evaluation of the reserve of a granite deposit by fuzzy kriging. Engineering Geology. 99(1-2). 23–30. 17 indexed citations
13.
Taboada, J., Celestino Ordóñez, Ángeles Saavedra, & M. Gloria Fiestras-Janeiro. (2006). Fuzzy expert system for economic zonation of an ornamental slate deposit. Engineering Geology. 84(3-4). 220–228. 22 indexed citations
14.
Taboada, J., et al.. (2006). Risk Communications: Around the World Neural Network Models for Assessing Road Suitability for Dangerous Goods Transport. Human and Ecological Risk Assessment An International Journal. 12(1). 174–191. 4 indexed citations
15.
16.
Uña‐Álvarez, Jacobo de & Ángeles Saavedra. (2004). Bias and Variance of the Nonparametric MLE Under Length-Biased Censored Sampling: A Simulation Study. Communications in Statistics - Simulation and Computation. 33(2). 397–413. 2 indexed citations
17.
Araújo‐Vilar, David, et al.. (2003). Comparison of Several Insulin Sensitivity Indices Derived from Basal Plasma Insulin and Glucose Levels with Minimal Model Indices. Hormone and Metabolic Research. 35(1). 13–17. 40 indexed citations
18.
Taboada, J., Antonio Vaamonde Liste, Ángeles Saavedra, & Celestino Ordóñez. (2002). Geostatistical study of the feldspar content and quality of a granite deposit. Engineering Geology. 65(4). 285–292. 10 indexed citations
19.
Saavedra, Ángeles & Ricardo Cao. (1999). Rate of convergence of a convolution-type estimator of the marginal density of a MA(1) process. Stochastic Processes and their Applications. 80(2). 129–155. 25 indexed citations
20.
Saavedra, Ángeles & Ricardo Cao. (1999). A Comparative Study Of Two Convolution-Type Estimators Of The Marginal Density Of Moving Average Processes. SSRN Electronic Journal. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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