Elsevier

Pedosphere

Volume 27, Issue 5, October 2017, Pages 877-889
Pedosphere

A Novel Evolutionary Genetic Optimization-Based Adaptive Neuro-Fuzzy Inference System and Geographical Information Systems Predict and Map Soil Organic Carbon Stocks Across an Afromontane Landscape

https://doi.org/10.1016/S1002-0160(17)60461-2Get rights and content

Abstract

Soil organic carbon (SOC) pool has the potential to mitigate or enhance climate change by either acting as a sink, or a source of atmospheric carbon dioxide (CO2) and also plays a fundamental role in the health and proper functioning of soils to sustain life on Earth. As such, the objective of this study was to investigate the applicability of a novel evolutionary genetic optimization-based adaptive neuro-fuzzy inference system (ANFIS-EG) in predicting and mapping the spatial patterns of SOC stocks in the Eastern Mau Forest Reserve, Kenya. Field measurements and auxiliary data reflecting the soil-forming factors were used to design an ANFIS-EG model, which was then implemented to predict and map the areal differentiation of SOC stocks in the Eastern Mau Forest Reserve. This was achieved with a reasonable level of uncertainty (i.e., root mean square error of 15.07 Mg C ha−1), hence demonstrating the applicability of the ANFIS-EG in SOC mapping studies. There is potential for improving the model performance, as indicated by the current ratio of performance to deviation (1.6). The mapping also revealed marginally higher SOC stocks in the forested ecosystems (i.e., an average of 109.78 Mg C ha−1) than in the agro-ecosystems (i.e., an average of 95.9 Mg C ha−1).

References (54)

  • D Tien Bui et al.

    Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS

    Comput Geosci

    (2012)
  • R A Welikala et al.

    Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy

    Comput Med Imaging Graphs

    (2015)
  • K O Were et al.

    Remotely sensing the spatial and temporal land cover changes in Eastern Mau forest reserve and Lake Nakuru drainage basin, Kenya

    Appl Geogr

    (2013)
  • L Winowiecki et al.

    Effects of land cover on ecosystem services in Tanzania: A spatial assessment of soil organic carbon

    Geoderma

    (2016)
  • L C Ying et al.

    Using adaptive network based fuzzy inference system to forecast regional electricity loads

    Energy Convers Manage

    (2008)
  • M Yolmeh et al.

    Genetic algorithm-artificial neural network and adaptive neuro-fuzzy inference system modeling of antibacterial activity of annatto dye on Salmonella enteritidis

    Microb Pathogen

    (2014)
  • E Aynekulu et al.

    A Protocol for Measurement and Monitoring Soil Carbon Stocks in Agricultural Landscapes. Version 1.1

    (2011)
  • J Batlle-Aguilar et al.

    Modelling soil carbon and nitrogen cycles during land use change. A review

    Agron Sustain Dev

    (2011)
  • G R Blake

    Bulk density

  • J M Bremmer et al.

    Nitrogen—total

  • S L Chiu

    Fuzzy model identification based on cluster estimation

    J Intell Fuzzy Syst

    (1994)
  • S L Chiu

    An efficient method for extracting fuzzy classification rules from high dimensional data

    J Adv Comput Intell

    (1997)
  • P R Day

    Particle fractionation and particle-size analysis

  • D de Brogniez et al.

    A map of the topsoil organic carbon content of Europe generated by a generalized additive model

    Eur J Soil Sci

    (2015)
  • R C Eberhart et al.

    Particle swarm optimization: Developments, applications and resources

  • R P Eclesia et al.

    Shifts in soil organic carbon for plantation and pasture establishment in native forests and grasslands of South America

    Global Change Biol

    (2012)
  • Food and Agriculture Organization (FAO) of the United Nations and Intergovernmental Technical Panel on Soils (ITPS)

    Status of the World's Soil Resources (SWSR)—Main Report

    (2015)
  • Cited by (4)

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