Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Rapidly Varying Flow01:24

Rapidly Varying Flow

45
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
45
Responses to Drought and Flooding02:41

Responses to Drought and Flooding

10.6K
Water plays a significant role in the life cycle of plants. However, insufficient or excess of water can be detrimental and pose a serious threat to plants.
10.6K
Response Surface Methodology01:16

Response Surface Methodology

83
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
83

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Analysis of Models to Estimate Morbidity Rates of Respiratory Diseases Through Deep Learning.

Tropical medicine & international health : TM & IH·2026
Same author

Financial expenditure as a criterion for choosing the most appropriate method for ecological corridor implementation.

Anais da Academia Brasileira de Ciencias·2025
Same author

Application of spatial environmental indicators in the assessment of degradation potential of water resources in water basins.

Environmental monitoring and assessment·2023
Same author

Geomorphometric environmental fragility of a watershed: a multicriteria spatial approach.

Environmental monitoring and assessment·2021
Same author

Valuation methodology of laminar erosion potential using fuzzy inference systems in a Brazilian savanna.

Environmental monitoring and assessment·2019

Related Experiment Video

Updated: May 25, 2025

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.1K

Random forest algorithm applied to model soil textural classification in a river basin.

Arthur Pereira Dos Santos1, Alessandro Xavier da Silva Junior2, Liliane Moreira Nery2

  • 1Department of Environmental Science, São Paulo State University (UNESP), Sorocaba, São Paulo, Brazil. arthur.p.santos@unesp.br.

Environmental Monitoring and Assessment
|February 26, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts soil texture, crucial for agriculture and environment. This study used random forest in the Sorocabuçu River Basin, achieving high accuracy and supporting sustainable land management.

Keywords:
Machine learningPrecision agricultureSoil erosionSoil textural classification

More Related Videos

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.1K
Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

34.4K

Related Experiment Videos

Last Updated: May 25, 2025

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.1K
Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.1K
Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

34.4K

Area of Science:

  • Agricultural Science
  • Environmental Science
  • Geosciences

Background:

  • Soil texture, defined by sand, silt, and clay proportions, is vital for agricultural and ecological functions.
  • Traditional soil texture classification methods are expensive and time-consuming, limiting widespread application.
  • Machine learning offers a cost-effective and efficient alternative for precise soil texture prediction.

Purpose of the Study:

  • To integrate geoprocessing, precision agriculture, and machine learning for accurate soil texture classification.
  • To assess the predictive performance of the random forest algorithm for soil texture in the Sorocabuçu River Basin (SRB).
  • To provide a foundation for sustainable land management and food security in agricultural regions.

Main Methods:

  • Selected 27 sampling points based on topography and land use in the SRB.
  • Conducted granulometric analysis using the pipette method for soil component separation.
  • Employed the random forest algorithm for soil texture classification, with spatial interpolation via GIS.

Main Results:

  • The random forest model achieved high accuracy (0.92 overall accuracy, 0.88 kappa index) with a low out-of-bag error (2.78%).
  • Identified varied spatial distribution of clay and high sand/silt levels, indicating potential erosion risk without conservation practices.
  • Observed classification challenges with the Sandy Clay Loam (SCL) class due to its intermediate properties.

Conclusions:

  • The integrated approach demonstrates excellent predictive capacity for soil texture classification.
  • Findings support enhanced understanding of soil structure for improved agricultural and environmental sustainability in the SRB.
  • The methodology is adaptable to other regions and agricultural contexts, with potential for refinement in homogeneous soil areas.