Fuzzy pattern recognition method for assessing soil erosion

Saadatpour, Motahareh; Afshar, Abbas; Afshar, M. H.

doi:10.1007/s10661-010-1794-8

Cited by 12 publications

(4 citation statements)

References 36 publications

(37 reference statements)

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“…The assessment of soil erosion is a multiple parameter decision-making process with many difficult measure inexact or qualitative parameters. It has extended version with Multi-parameter fuzzy pattern recognition (MPFPR) considered in (Saadatpour et al, 2011). Based on fuzzy multi-parameter analysis and nonstructured decision making for multi-parameter systems, MPFPR method is consider for obtaining knowledge on the soil erosion and assessment of geological settings under different management strategies.…”

Section: Mpfpr (Multi-parameter Fuzzy Pattern Recognition)mentioning

confidence: 99%

Assessing of Soil Erosion Risk Through Geoinformation Sciences and Remote Sensing—A Review

Filchev

Kolev

2021

Earth and Environmental Sciences Library

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During past decades a marked manifestation of widespread erosion phenomena was studied worldwide. Global conservation community has launched campaigns at local, regional and continental level in developing countries for preservation of soil resources in order not only to stop or mitigate human impact on nature but also to improve life in rural areas introducing new approaches for soil cultivation. After the adoption of Sustainable Development Goals of UNs and launching several world initiatives such as the Land Degradation Neutrality (LDN) the world came to realise the very importance of the soil resources on which the biosphere relies on for its existence. The main goal of the chapter is to review of different types and structures erosion models as well as its some applications. Several methods using spatial analysis capabilities of geographic information systems (GIS) are in operation for soil erosion risk assessment, such as: Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation (RUSLE) in operation worldwide and in USA and Modèle d'Evaluation Spatiale de l'ALéa Erosion des Sols (MESALES) model. These and more models are being discussed in present work alongside more experimental models and methods for assessing soil erosion risk such as Artificial Intelligence (AI), Machine and Deep Learning etc. At the end of this work a prospectus for the future development of soil erosion risk assessment is drawn.

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Section: Mpfpr (Multi-parameter Fuzzy Pattern Recognition)mentioning

confidence: 99%

Assessing of Soil Erosion Risk Through Geoinformation Sciences and Remote Sensing—A Review

Filchev

Kolev

2021

Earth and Environmental Sciences Library

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“…It serves as a logical framework that gives an approximate, rather than an accurate, model intended for human interpretation styles (Kambalimath and Deka 2020). Fuzzy mathematics is being widely applied in a number of domains, including engineering and soil sciences, to take into account non-probabilistic uncertainty in the past few years (Mitra et al 1998), (Tran et al 2002), (Akyürek et al 2006), (Tayfur et al 2003), (Haile 2022) (Saadatpour et al 2011). Research also showed that sediment estimation may be accomplished effectively with fuzzy logic, offering excellent accuracy and dependability (Nivesh et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Identification of soil erosion‑susceptible areas using fuzzy logic and sediment transport index aided by mineralogical-granulometric analysis in Subansiri basin, Assam, India

Dutta,

Srivast,

Boruah

2024

Preprint

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Erosion poses a significant challenge along the river Subansiri in Assam, India impacting river banks and sediment breakdown. This paper presents a comprehensive study on the identification of soil erosion-susceptible areas in the Subansiri Basin using fuzzy logic modeling and sediment transport index (STI) analysis with emphasis on X-Ray Diffraction (XRD) and grain size analysis. The soil erosion map's overall findings showed that the areas it covered were vulnerable to erosion in severe, very high, high, moderate, and low levels. Slope gradients and flow accumulation were used to calculate the STI on the Raster Calculator in ArcGIS. The Sediment Transport Index ranges from 0 to 2770. This study also aims to elucidate the mineral composition and grain size distributions of sediments sourced from the Subansiri river in Assam. Examination of 12 soil samples facilitated the assessment of their mineral composition and size distribution. Sieve analysis unveiled that a predominant 80% of the soil along the Subansiri river constitutes sand, with lesser percentages attributed to silt and clay, following USDA primary classification guidelines. Utilizing XRD methodology, the major crystalline minerals identified in these sands encompassed Quartz, Montmorillonite, Illite, Calcite, and Plagioclase Feldspar Albite. Skewness and kurtosis is also studied to determine the shape and distribution of the soil samples and the values range from 0.25 to 0.70 signifying a fine skewed to very fine skewed and from − 1.16 to -1.93 showing a very platykurtic distribution. Thus, for a thorough investigation of soil erosion, this research provides a novel hybrid methodology that integrates fuzzy logic, STI, XRD and grain size analysis.

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“…Due to deal ability of Fuzzy logic and fuzzy sets to uncertainty and vague data, they have been widely used in environmental issues, such as evaluation of Land suitability based on soil profile [5], soil classification [6], time-series modeling of air pollution [7], soil erosion [8,9], modeling of spatial distribution and density of vegetation [10], environmental impact assessment [11,12], analysis of agricultural suitability [13], risk assessment of natural hazards [14][15][16], fire risk assessment [17,18], salinization assessment in drylands [19][20][21], landslide hazard zoning [22], water resources management [23], renewable energy [24] and desertification assessment [25][26][27][28][29][30][31]. Therefore fuzzy logic can be a powerful method to classify and monitoring of environmental processes over a time period, dempster-shafer theory [26] and fuzzy logic [25,27,31] can handle uncertainty in the assessment of desertification.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Based Detection of Desertification-prone Areas: A Case Study in Khorasan-Razavi Province, Iran

Shiravi¹,

Sepehr²

2017

nrc

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In this article, a desertification susceptibility map was developed over Khorasan Razavi ecoregions located in northeastern Iran with arid and semi-arid environments. For this purpose, a fuzzy algorithm regarding Dempster-Shafer theory was applied based on six main indicators including wind erosion, aridity severity, soil erodibility, landuse type, salinization and vegetation cover density chosen by Delphi techniques. The indicators were combined under linear fuzzy function and gained to a membership function for each layer. The results indicated about 43.5 % region is susceptible to soil erodibility; also cold hyper-arid and ultra-cold arid deserts ecoregions show a high range of sustainability to desertification.

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Fuzzy pattern recognition method for assessing soil erosion

Cited by 12 publications

References 36 publications

Assessing of Soil Erosion Risk Through Geoinformation Sciences and Remote Sensing—A Review

Assessing of Soil Erosion Risk Through Geoinformation Sciences and Remote Sensing—A Review

Identification of soil erosion‑susceptible areas using fuzzy logic and sediment transport index aided by mineralogical-granulometric analysis in Subansiri basin, Assam, India

Fuzzy Based Detection of Desertification-prone Areas: A Case Study in Khorasan-Razavi Province, Iran

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