Applications of fuzzy logic to the prediction of soil erosion in a large watershed

Mitra, Bhaskar; Scott, H. D.; Dixon, John C.; McKimmey, J. M.

doi:10.1016/s0016-7061(98)00050-0

Cited by 97 publications

(57 citation statements)

References 3 publications

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“…The measured data collected from Khaldiya residential area were arbitrarily classified into different fuzzy set categories with respective minimum and maximum values of model variables. Then, different scalar ranges of both triangular and trapezoidal membership functions were tested until the satisfactory outputs were obtained with respect to the set of rules used in the FIS, as similarly conducted in previous studies (Mitra et al, 1998;Turkdogan-Aydinol and Yetilmezsoy, 2010;Yetilmezsoy et al, 2012;Yetilmezsoy, 2012). Results of the preliminary analysis indicated that trapezoidal shaped membership functions with ten levels for the input variables and fifteen levels for the output variable demonstrated the optimum prediction performance in estimation of PM levels at the studied area.…”

Section: Selection Of Membership Functionsmentioning

confidence: 90%

A Prognostic Approach Based on Fuzzy-Logic Methodology to Forecast PM10 Levels in Khaldiya Residential Area, Kuwait

Yetilmezsoy

Abdul‐Wahab

2012

Aerosol Air Qual. Res.

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A prognostic approach is proposed based on a fuzzy-logic model to estimate suspended dust concentrations, related to PM 10 , in a specific residential area in Kuwait with high traffic and industrial influences. Seven input variables, including four important meteorological parameters (wind speed, wind direction, relative humidity and solar radiation) and the ambient concentrations of three gaseous pollutants (methane, carbon monoxide and ozone) were fuzzified using a sytem with a graphical user interface (GUI) and an artificial intelligence-based approach. Trapezoidal membership functions with ten and fifteen levels were employed for the fuzzy subsets of each model variable. A Mamdani-type fuzzy inference system (FIS) was developed to introduce a total of 146 rules in the IF-THEN format. The product (prod) and the centre of gravity (centroid) methods were performed as the inference operator and defuzzification methods, respectively, for the proposed FIS. The results obtained using uzzy-logic were compared with the outputs of an exponential regression model. The predictive performances of the models were compared based on various descriptive statistical indicators, and the proposed method was tested against additional observed data. The prognostic model presented in this work produced very small deviations from the actual results, and showed better predictive performance than the other model with regard to forecasting PM 10 levels, with a very high determination coefficient of over 0.99.

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Section: Selection Of Membership Functionsmentioning

confidence: 90%

A Prognostic Approach Based on Fuzzy-Logic Methodology to Forecast PM10 Levels in Khaldiya Residential Area, Kuwait

Yetilmezsoy

Abdul‐Wahab

2012

Aerosol Air Qual. Res.

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“…Integration of fuzzy logic with GIS in a decision-making framework has been used for different purposes, including land suitability based upon soil profiles (Burrough et al 1992), soil classification (Lark and Bolam 1997), landfill site screening (Charnpratheep et al 1997), soil erosion (Mitra et al 1998), crop land suitability analysis (Ahmed et al 2000), ranking burned forests to evaluate the risk of desertification (Sasikala and Petrou 2001), seeking optimum locations for real estate (Zeng and Zhou 2001), assessing vulnerability to natural hazards (Rashed and Weeks 2003;Tangestani 2004;Dixon 2005), estimating risk (Sadiq and Husain 2005), incorporating farmer's knowledge for land suitability classification (Sicat et al 2005), fuel type mapping (Nadeau and Englefield 2006), assessing spatial extent of dry land salinity (Malins and Metternicht 2006), etc. Therefore, many studies have been performed using fuzzy logic integrated with GIS in a MCDM framework demonstrating that the methods are robust and valid.…”

Section: Introductionmentioning

confidence: 99%

Fire risk evaluation using multicriteria analysis—a case study

Vadrevu

Eaturu

Badarinath

2009

Environ Monit Assess

161

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Forest fires are one of the major causes of ecological disturbance and environmental concerns in tropical deciduous forests of south India. In this study, we use fuzzy set theory integrated with decision-making algorithm in a Geographic Information Systems (GIS) framework to map forest fire risk. Fuzzy set theory implements classes or groupings of data with boundaries that are not sharply defined (i.e., fuzzy) and consists of a rule base, membership functions, and an inference procedure. We used satellite remote sensing datasets in conjunction with topographic, vegetation, climate, and socioeconomic datasets to infer the causative factors of fires. Spatial-level data on these biophysical and socioeconomic parameters have been aggregated at the district level and have been organized in a GIS framework. A participatory multicriteria decision-making approach involving Analytical Hierarchy Process has been designed to arrive at a decision matrix that identified the important causative factors of fires. These expert judgments were then integrated using spatial fuzzy decision-making algorithm to map the forest fire risk. Results from this study were quite useful in identifying potential "hotspots" of fire risk, where forest fire protection measures can be taken in advance. Further, this study also demonstrates the potential of multicriteria analysis integrated with GIS as an effective tool in assessing "where and when" forest fires will most likely occur.

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“…The assessment of soil erosion commonly utilizes predictive models such as the USLE or RUSLE [13,59,60]. As the initial application purpose of USLE was to provide conservationists with a tool supporting decisions on sustainable cropping or management strategies its applications are limited to long-term estimations of annual soil loss.…”

Section: C-factor Analysismentioning

confidence: 99%

Capability of Spaceborne Hyperspectral EnMAP Mission for Mapping Fractional Cover for Soil Erosion Modeling

et al. 2015

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Soil erosion can be linked to relative fractional cover of photosynthetic-active vegetation (PV), non-photosynthetic-active vegetation (NPV) and bare soil (BS), which can be integrated into erosion models as the cover-management C-factor. This study investigates the capability of EnMAP imagery to map fractional cover in a region near San Jose, Costa Rica, characterized by spatially extensive coffee plantations and grazing in a mountainous terrain. Simulated EnMAP imagery is based on airborne hyperspectral HyMap data. Fractional cover estimates are derived in an automated fashion by extracting image endmembers to be used with a Multiple End-member Spectral Mixture Analysis approach. The C-factor is calculated based on the fractional cover estimates determined independently for EnMAP and HyMap. Results demonstrate that with EnMAP imagery it is possible to extract quality endmember classes with important spectral features related to PV, NPV and soil, and be able to estimate relative cover fractions. This spectral information is critical to separate BS and NPV which greatly can impact the C-factor OPEN ACCESS Remote Sens. 2015, 7 11777 derivation. From a regional perspective, we can use EnMAP to provide good fractional cover estimates that can be integrated into soil erosion modeling.

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Applications of fuzzy logic to the prediction of soil erosion in a large watershed

Cited by 97 publications

References 3 publications

A Prognostic Approach Based on Fuzzy-Logic Methodology to Forecast PM10 Levels in Khaldiya Residential Area, Kuwait

A Prognostic Approach Based on Fuzzy-Logic Methodology to Forecast PM10 Levels in Khaldiya Residential Area, Kuwait

Fire risk evaluation using multicriteria analysis—a case study

Capability of Spaceborne Hyperspectral EnMAP Mission for Mapping Fractional Cover for Soil Erosion Modeling

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