Comparison of Groundwater Level Estimation Using Neuro-fuzzy and Ordinary Kriging

Kholghi, Majid; Hosseini, Seiyed Mossa

doi:10.1007/s10666-008-9174-2

Cited by 72 publications

(19 citation statements)

References 22 publications

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“…The ANFIS model overcomes the more traditional methods of electrical conductivity modelling based on the total dissolved solids in water. In 2009, Kholghi and Hosseini (2009) applied the neuro-fuzzy model and the kriging method for the water level assessment, in the Qazvin plain (Iran), in areas where no data were available. The neurofuzzy models manage the uncertainty and lack of data well.…”

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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“…[19]. Ordinary kriging is a linear weighted-average technique which is unbiased with regard to the expected value of residuals [22]. In this context, we assume that the mean is constant in the local neighborhood of each estimation point and m(u) = m(u α ) for each nearby data value and Z(u α ) is used to estimate Z(u) [23].…”

Section: Coastline Extractionmentioning

confidence: 99%

Generate Reservoir Depths Mapping by Using Digital Elevation Model: A Case Study of Mosul Dam Lake, Northern Iraq

Khattab¹,

Abo²,

Al-Muqdadi³

et al. 2017

ARS

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Not many bathymetric maps are available for many lakes and reservoirs in developing countries. Usually the bathymetric mapping requires investment in expensive equipment and fieldwork, both of which are not accessible in these countries. This work demonstrates the ability to develop bathymetric map of Mosul Lake by using a digital elevation model (DEM). The depths model of the lake was designed through the use of three main stages; a coastline extraction, dataset interpolation and a triangular irregular network model. The normalized difference water index (NDWI) was used for automatic delineation of the lake coastline from satellite images. The ordinary kriging interpolation with a stable model was used to interpolate the water depths dataset. Finally a triangulated irregular network (TIN) model was used to visualize the resulting interpolation model. Calculated values of area and volume of a TIN model during 2011 were compared with values of supposed initial operation of the reservoir. The differences of water volume storage between these stages at 321 m water level was about 0.81 × 10 9 m 3 , where the lake lost around 10% of storage value. Also the results of depths lake model show that the change in water storage between March and July 2011 was about 3.08 × 10 9 m 3 .

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“…Kriging has been broadly used in geology, hydrology, environmental monitoring, atmospheric sciences and pedology for interpolation of spatial data (McBratney et al, 1982;Stein, 1999;poon et al, 2000;Gringarten and deutsch, 2001;Jost and et al, 2005;Kholghi and hosseini, 2009;Oliver and webster, 2014;Jeihouni et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…They showed that ANFIS with a few data has better capability to model EC than regression based conventional methods. Kholghi and hosseini (2009) compared both ANFIS and OK to estimate groundwater level and they found that ANFIS is more efficient than OK. Kazemi and hosseini (2011) studied OK, ANN and ANFIS for interpolating heavy metals in the Caspian Sea and they reported that ANFIS is the model with the lowest simulation error.…”

Section: Introductionmentioning

confidence: 99%

Spatial analysis of groundwater electrical conductivity using ordinary kriging and artificial intelligence methods (Case study: Tabriz plain, Iran)

et al. 2015

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Artificial intelligence (AI) systems have opened a new horizon to analyze water engineering and environmental problems in recent decades. In this study performances of ordinary kriging (OK) as a linear geostatistical estimator and two intelligent methods including artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are investigated. For this purpose, geographical coordinates of 120 observation wells that located in Tabriz plain, north-west of Iran, were defined as inputs and groundwater electrical conductivities (EC) were set as output of models. Eighty percent of data were randomly selected to train and develop mentioned models and twenty percent of data used for testing and validating. Finally, the outputs of models were compared with the corresponding measured values in observation wells. Results indicated that ANFIS model provided the best accuracy among models with the root mean squared error (RMSE) value of 1.69 dS.m -1 and correlation coefficient (R) of 0.84. The RMSE values in ANN and OK were calculated 1.97 and 2.14 dS.m -1 and the R values were determined 0.79 and 0.76, respectively. According to the results, the ANFIS method predicted EC precisely and can be advised for modeling groundwater salinity.

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Comparison of Groundwater Level Estimation Using Neuro-fuzzy and Ordinary Kriging

Cited by 72 publications

References 22 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

Generate Reservoir Depths Mapping by Using Digital Elevation Model: A Case Study of Mosul Dam Lake, Northern Iraq

Spatial analysis of groundwater electrical conductivity using ordinary kriging and artificial intelligence methods (Case study: Tabriz plain, Iran)

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