Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis

Dixon, Barnali M.

doi:10.1016/j.jhydrol.2004.11.010

Cited by 193 publications

(106 citation statements)

References 32 publications

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“…The evaluation at the regional scale of groundwater vulnerability is a problematic issue; thus, several studies have been carried out to address this problem. In fact, Dixon (2005) evaluated the vulnerability with a neuro-fuzzy analysis and GIS applications. The author carried out a sensitivity analysis, which assessed that the neuro-fuzzy models are sensitive to the form of fuzzy sets, to the fuzzy set number, to the nature of the weights of the rules and validation techniques used during the learning processes.…”

Section: Introductionmentioning

confidence: 99%

Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera

Sdao

Lioi

Pascale

et al. 2013

Nat. Hazards Earth Syst. Sci.

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Abstract. The complete assessment of landslide susceptibility needs uniformly distributed detailed information on the territory. This information, which is related to the temporal occurrence of landslide phenomena and their causes, is often fragmented and heterogeneous. The present study evaluates the landslide susceptibility map of the Natural Archaeological Park of Matera (Southern Italy) (Sassi and area Rupestrian Churches sites). The assessment of the degree of "spatial hazard" or "susceptibility" was carried out by the spatial prediction regardless of the return time of the events. The evaluation model for the susceptibility presented in this paper is very focused on the use of innovative techniques of artificial intelligence such as Neural Network, Fuzzy Logic and Neuro-fuzzy Network. The method described in this paper is a novel technique based on a neuro-fuzzy system. It is able to train data like neural network and it is able to shape and control uncertain and complex systems like a fuzzy system. This methodology allows us to derive susceptibility maps of the study area. These data are obtained from thematic maps representing the parameters responsible for the instability of the slopes. The parameters used in the analysis are: plan curvature, elevation (DEM), angle and aspect of the slope, lithology, fracture density, kinematic hazard index of planar and wedge sliding and toppling. Moreover, this method is characterized by the network training which uses a training matrix, consisting of input and output training data, which determine the landslide susceptibility. The neuro-fuzzy method was integrated to a sensitivity analysis in order to overcome the uncertainty linked to the used membership functions. The method was compared to the landslide inventory map and was validated by applying three methods: a ROC (Receiver Operating Characteristic) analysis, a confusion matrix and a SCAI method. The developed neuro-fuzzy method showed a good performance in the determination of the landslide susceptibility map.

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Section: Introductionmentioning

confidence: 99%

Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera

Sdao

Lioi

Pascale

et al. 2013

Nat. Hazards Earth Syst. Sci.

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“…They are capable of providing a neuron computing approach to solve complex problems. In the last decade, ANNs have been widely successfully applied to various water resources problems, such as hydrological processes (Nayak et al 2004;Sahoo et al 2005;Dastorani et al 2010;Guo et al 2011;Wu and Chau 2011;Senkal et al 2012), water resources management (Kralisch et al 2003;Sreekanth and Datta 2010), groundwater problems (Daliakopoulos et al 2005;Dixon 2005;Garcia and Shigidi 2006;Nayak et al 2006;Ghose et al 2010;Banerjee et al 2011), and water quality (Ha and Stenstrom 2003;Kuo et al 2006;Anctil et al 2009;da Costa et al 2009;Dogan et al 2009;Chang et al 2010;He et al 2011). ANNs also have been used for modeling and forecasting DO (Kuo et al 2007;Singh et al 2009;Ranković et al 2010;Najah et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Wen

Fang

Diao

et al. 2012

Environ Monit Assess

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Identification and quantification of dissolved oxygen (DO) profiles of river is one of the primary concerns for water resources managers. In this research, an artificial neural network (ANN) was developed to simulate the DO concentrations in the Heihe River, Northwestern China. A three-layer back-propagation ANN was used with the Bayesian regularization training algorithm. The input variables of the neural network were pH, electrical conductivity, chloride (Cl . Cl − was found to be least effective variables on the proposed model. The identified ANN model can be used to simulate the water quality parameters.

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“…The approach is based on fuzzy inference systems already used extensively in hydrological and water quality modelling (e.g. Chen et al, 2006;Dixon, 2005;Haberlandt et al, 2002;Jacquin and Shamseldin, 2006;Marce et al, 2004;Nayak et al, 2004). These modelling systems integrate the outputs from a number of sub-models to estimate a single overall output.…”

Section: Estimation Of Nutrients Loads Using Catchment Characteristicsmentioning

confidence: 99%

Derivation of a fuzzy national phosphorus export model using 84 Irish catchments

Nasr¹,

Bruen

2013

Science of The Total Environment

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► Develops a new national phosphorus export model for agricultural catchments in Ireland ► Improves on earlier empirical phosphorus export models by using k-means clustering method for partitioning data ► Uses ANFIS model to predict annual average ortho-phosphorus concentrations using catchment characteristics ► Phosphorus desorption index (PDI) and runoff risk index (RRI) are essential predictors in the model. a b s t r a c t a r t i c l e i n f o Implementation of appropriate management strategies to mitigate diffuse phosphorus (P) pollution at the catchment scale is vitally important for the sustainable development of water resources in Ireland. An important element in the process of implementing such strategies is the prediction of their impacts on P concentrations in a catchment using a reliable mathematical model. In this study, a state-of-the-art adaptive neuro-fuzzy inference system (ANFIS) has been used to develop a new national P model capable of estimating average annual ortho-P concentrations at un-gauged catchments. Data from 84 catchments dominated by diffuse P pollution were used in developing and testing the model. Six different split-sample scenarios were used to partition the total number of the catchments into two sets, one to calibrate and the other to validate the model. The k-means clustering algorithm was used to partition the sets into clusters of catchments with similar features. Then for each scenario and for each cluster case, 11 different models, each of which consists of a linear regression sub-model for each cluster, were formulated by using different input variables selected from among six spatially distributed variables including phosphorus desorption index (PDI), runoff risk index (RRI), geology (GEO), groundwater (GW), land use (LU), and soil (SO). The success of the new approach over the conventional lumped, empirical, modelling approach was evident from the improved results obtained for most of the cases. In addition the results highlighted the importance of using information on PDI and RRI as explanatory input variables to simulate the average annual ortho-P concentrations.

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Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis

Cited by 193 publications

References 32 publications

Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera

Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera

Artificial neural network modeling of dissolved oxygen in the Heihe River, Northwestern China

Derivation of a fuzzy national phosphorus export model using 84 Irish catchments

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