Misgana K. Muleta
Water Science and Technology · 2005–2012
A researcher in the Science corpus: 2 papers; trusts 15 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Water Science and Technology — 14 citations into it; #302 of 1,711 by trust here, #1,033 by citations
- Environmental Engineering — 11 citations into it
- Global and Planetary Change — 6 citations into it; #2,428 of 10,225 by trust here, #6,762 by citations
- Ocean Engineering — 5 citations into it; #769 of 3,193 by trust here, #1,513 by citations
Whom Misgana K. Muleta trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Keith Beven
- Ilya M. Sobol
- Bryan Tolson
- Vijay P. Singh
- John H. Holland
- B. Schaefli
- Holger R. Maier
- Hoshin V. Gupta
- Philip Gassman
- Qingyun Duan
Who trusts Misgana K. Muleta overall
- Andreas Efstratiadis
- Xuesong Zhang
- H. Sellami
- Xiaomeng Song
- Demetris Koutsoyiannis
- Raghavan Srinivasan
- I. La Jeunesse
- Jianyun Zhang
- Mojtaba Sadegh
- Jasper A. Vrugt
Papers from Misgana K. Muleta's perspective
Ordered by how strongly the researchers Misgana K. Muleta trusts endorse each paper. Same corpus, a different order for every researcher.
- Effective and efficient global optimization for conceptual rainfall‐runoff models
- Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology
- The future of distributed models: model calibration and uncertainty prediction.
- Multi-objective global optimization for hydrologic models
- Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters
- Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence
- Bayesian recursive parameter estimation for hydrologic models
- Do Nash values have value?
- A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code
- Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model