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Olavo Correa Pedrollo

affiliation not provided to SSRN

  • affiliation not provided to SSRN

SCHOLARLY PAPERS

3

DOWNLOADS

131

TOTAL CITATIONS

1

Scholarly Papers (3)

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1.

Exploring the Relationship between Saturated Soil Hydraulic Conductivity and Co-Driven Factors Using Artificial Neural Networks at Watershed Scale

Number of pages: 34 Posted: 19 Jan 2024
Mauricio Fornalski Soares, Romulo Felix Nunes, Tirzah Moreira Siqueira, Olavo Correa Pedrollo, Klaus Reichardt, Willian Silva Barros, Dongli She and Luis Carlos Timm
affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, University of São Paulo (USP), affiliation not provided to SSRN, Hohai University and affiliation not provided to SSRN
Downloads 53 (1,041,696)

Abstract:

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supervised machine learning, soil hydro-physical data, principal component analysis, land use systems, watershed scale

2.

Efficiency of Multi-Spatial Calibration of the Swat Model for Estimating Runoff and Sediment Production in a Rural Watershed

Number of pages: 27 Posted: 29 Feb 2024
Priscila Pacheco Mariani, Nilza Maria dos Reis Castros, Vanessa Sari, Olavo Correa Pedrollo and Pedro Massochin Medeiros
affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN and affiliation not provided to SSRN
Downloads 41 (1,196,171)

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Semi-distributed model, Homogeneous watershed, Hydro-sedimentological monitoring.

3.

Artificial Neural Network Models to Examine the Relationship between Saturated Soil Hydraulic Conductivity and Co-Driven Factors at the Watershed Scale

Number of pages: 33 Posted: 13 Jul 2023
Mauricio Fornalski Soares, Tirzah Moreira Siqueira, Romulo Felix Nunes, Olavo Correa Pedrollo, Klaus Reichardt, Willian Silva Barros and Luis Carlos Timm
affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, University of São Paulo (USP), affiliation not provided to SSRN and affiliation not provided to SSRN
Downloads 37 (1,222,651)
Citation 1

Abstract:

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supervised machine learning, soil hydro-physical data, terrain attributes, Principal component analysis, land use systems, watershed scale.