GIS-fuzzy logic based approach in modeling soil texture: Using parts of the Clay Belt and Hornepayne region in Ontario Canada as a case study

Akumu, Clement E.; Johnson, Jesse W.; Etheridge, David A.; Uhlig, P.; Woods, Murray; Pitt, Doug; McMurray, S.

doi:10.1016/j.geoderma.2014.09.021

Cited by 42 publications

(21 citation statements)

References 17 publications

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“…However, making comparisons within same soil property, the methods performed in contrast, with extreme high values of MPE and RMSPE in some cases. Considering results from the literature related with soil physical properties, most results showed suitable accuracy indexes, similar to those presented in this work, but they are mostly related to soil texture mapping (Akumu et al, 2015;Qi et al, 2006;Zhu et al, 2010). Thus, it is not possible to make any specific comparison between MPE and RM-SPE values.…”

Section: Anova Testsupporting

confidence: 71%

“…Considering the scale of variations of the studied sites, relief and organisms are the main drivers of soil variability, and the other soil forming factors are considered a constant. Additionally, terrain derivatives are strongly related to soil properties and have been useful in digital soil mapping for this reason (Akumu et al, 2015). Digital elevation models with pixels of 20 m resolution were generated from contour lines at 1:50,000 (IBGE) scale.…”

Section: Knowledge-based Digital Soil Mapping Techniquementioning

confidence: 99%

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Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds

Menezes

Silva

Mello

et al. 2018

Sci. agric. (Piracicaba, Braz.)

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ABSTRACT:The estimation of soil physical and chemical properties at non-sampled areas is valuable information for land management, sustainability and water yield. This work aimed to model and map soil physical-chemical properties by means of knowledge-based digital soil mapping approach as a study case in two watersheds representative of different physiographical regions in Brazil. Two watersheds with contrasting soil-landscape features were studied regarding the spatial modeling and prediction of physical and chemical properties. Since the method uses only one value of soil property for each soil type, the way of choosing typical values as well the role of land use as a covariate in the prediction were tested. Mean prediction error (MPE) and root mean square prediction error (RMSPE) were used to assess the accuracy of the prediction methods. The knowledge-based digital soil mapping by means of fuzzy logics is an accurate option for spatial prediction of soil properties considering: 1) lesser intense sampling scheme;2) scarce financial resources for intensive sampling in Brazil; 3) adequacy to properties with non-linearity distribution, such as saturated hydraulic conductivity. Land use seems to influence spatial distribution of soil properties thus, it was applied in the soil modeling and prediction. The way of choosing typical values for each condition varied not only according to the prediction method, but also with the nature of spatial distribution of each soil property.

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Section: Anova Testsupporting

confidence: 71%

Section: Knowledge-based Digital Soil Mapping Techniquementioning

confidence: 99%

Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds

Menezes

Silva

Mello

et al. 2018

Sci. agric. (Piracicaba, Braz.)

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“…Therefore, more clay can be expected in the lower parts of the study area. Akumu et al (2015) also used terrain covariates such as elevation, curvature, slope position classification, landforms, slope, smooth multi-path WI and land cover successfully to predict and map soil texture.…”

Section: Environmental Covariate Selectionmentioning

confidence: 99%

Predicting and mapping of soil particle‐size fractions with adaptive neuro‐fuzzy inference and ant colony optimization in central Iran

Taghizadeh‐Mehrjardi¹,

Toomanian

Khavaninzadeh³

et al. 2016

European J Soil Science

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In arid regions, knowledge of the variation in soil texture is crucial for land management because it affects soil physical, chemical, biological and most importantly hydrological properties. The availability of information on soil texture is scarce even though it is required to support land‐use management and sustainable development. Because it is costly to obtain information about the individual particle‐size fractions (PSFs), we used digital soil mapping methods (DSM) with environmental covariates that are less costly to obtain. Specifically, we explored the use of a digital elevation model and remote sensing data as environmental covariates to predict the vertical (i.e. 0–0.15, 0.15–0.3, 0.3–0.6 and 0.6–1 m) and lateral variation in PSFs over a 150‐km2 area in central Iran. We used a combination of equal‐area spline depth functions and three data‐mining techniques: multiple linear regression (MLR), artificial neural networks (ANN) and the neuro‐fuzzy inference system (ANFIS). In addition, we explored the effect of the reduction in dimension of feature space with ant colony optimization (ACO) and correlation‐based feature selection (CFS) on the accuracy of prediction of spatial models for each PSF. The results showed that the prediction of clay at 0–0.15‐m depth with ACO indicated the importance of including Landsat ETM+, the digital numbers of band 7 of Landsat images (B7) and clay index, whereas at 0.60–1‐m depth the wetness index and multi‐resolution valley bottom flatness index (MRVBF) were important. Model evaluation by leave‐one‐out cross‐validation with 191 soil observations indicated that the predictions by the ACO‐based ANFIS model (RMSE = 4.51% and R2 = 0.74 for clay at 0–0.15‐m) were more accurate than those by MLR and ANN. Spatial prediction was also better for the topsoil (0–0.15‐m) than at depth (RMSE = 7.1% for clay at 0.6–1 m); therefore, we conclude that the environmental covariates tested cannot resolve subsurface variation as accurately. Nevertheless, we recommend prediction by the ACO‐based ANFIS model and splines of lateral and vertical distribution of PSFs in other arid regions of Iran with the same agro‐ecological conditions. Highlights Digital soil mapping of particle size‐fractions (PSF) by adaptive neuro‐fuzzy inference and ant colony optimization. Use of ant colony optimization (ACO) to assist in feature selection of environmental covariates. Neuro‐fuzzy inference system (ANFIS) superior to multiple linear regression (MLR) and artificial neural networks (ANN). PSF prediction by ACO‐based ANFIS model and splines is optimal.

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“…Fuzzy modelling allows flexible standardization and aggregation by partial membership. Numerous studies attempted to use fuzzy modelling in development of water quality index and water quality assessment (Dahiya et al 2007;Icaga 2007;S¸en 2009;Mofarrah & Husain 2011;Sahu et al 2011;Gharibi et al 2012;Liu & Zou 2012;Scannapieco et al 2012;Kord & Asghari Moghaddam 2014) or in the other proposes that are near to aim of our study (Chen & Paydar 2012;Lewis et al 2014;Akumu et al 2015;Caniani et al 2016;Araya-Muñoz et al 2017;Hellwig et al 2017;Zhang et al 2017). All of these researches are emphasized on the very high accuracy of the results due to use of the fuzzy logic method.…”

Section: Introductionmentioning

confidence: 86%

Qualitative zoning of groundwater to assessment suitable drinking water using fuzzy logic spatial modelling via GIS

Tafreshi

Keshavarzi

2018

Water & Environment J

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Due to shortage of surface water, resources of ground water in Iran (like many other countries with dry and semi‐dry weather) have turned into the most important and reliable source for water supply. Thus, it is necessary to consider the quality of this resource for drinking purposes. The aim of this study was to assessment suitable drinking water based on a fuzzy approach in Asalem region, Iran. For reduce the uncertainty, fuzzy logic spatial modelling via GIS was applied. To receive these aims, four stages were performed. In stage 1, we calculate the values of eighteen conventional and effective factors used for drinking water quality classification upon analysing the statistical quality data regarding various ions existing in 15 sample wells dug in 2015. In stage 2, interpolate these factors by kriging method via ArcGIS software. In stage 3, parameter standardization with fuzzy membership function were done. And in stage 4, for Aggregation factors, several fuzzy overlay operations were used. Finally, to identify the best operation, the ‘Optimum Index Factor’ was used. Results showed that the ‘GAMMA 0.9’ with highest OIF in most of the factors, is the best overly operation in our study area. According to classified the best operation map, 1.42% of understudied regions are classified in very poor class, 12.48% is grouped in poor class, 78.55% is grouped in fair class, 7.53% is classified in good group and 0.01% is classified as excellent group. In this regard only central parts of the study area have ‘fair’ to ‘good’ and ‘excellent’ groundwater quality for drinking water and in other parts, the quality is within ‘very poor’ and ‘poor’ to ‘fair’.

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GIS-fuzzy logic based approach in modeling soil texture: Using parts of the Clay Belt and Hornepayne region in Ontario Canada as a case study

Cited by 42 publications

References 17 publications

Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds

Knowledge-based digital soil mapping for predicting soil properties in two representative watersheds

Predicting and mapping of soil particle‐size fractions with adaptive neuro‐fuzzy inference and ant colony optimization in central Iran

Qualitative zoning of groundwater to assessment suitable drinking water using fuzzy logic spatial modelling via GIS

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