GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran

Zabihi, Mohsen; Pourghasemi, Hamid Reza; Pourtaghi, Zohre Sadat; Behzadfar, Morteza

doi:10.1007/s12665-016-5424-9

Cited by 169 publications

(78 citation statements)

References 49 publications

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“…Several statistical methods can also be adopted for groundwater mapping where adequate information on different influencing parameters to groundwater accumulation and movement are available. These include frequency ratio (Davoodi et al 2013), multi-criteria decision evaluation (Murthy and Mamo 2009;Kumar et al 2014), logistic regression model (Ozdemir 2011), weights-of-evidence model (Ozdemir 2011;Pourtaghi and Pourghasemi 2014), random forest model (Rahmati et al 2016Naghibi et al 2016, maximum entropy model (Rahmati et al 2016), boosted regression tree (Naghibi et al 2016;Naghibi and Pourghasemi 2015), classification and regression tree (Naghibi et al 2016), multivariate adaptive regression spline model (Zabihi et al 2016), certainty factor model (Zabihi et al 2016), evidential belief function (Pourghasemi and Beheshtirad 2015;Naghibi and Pourghasemi 2015), and generalized linear model (Naghibi and Pourghasemi 2015). These information are lacking in many third world country hence proper understanding of hydrogeological characteristics for successful exploitation of groundwater in basement areas depend largely on geophysical methods.…”

Section: Introductionmentioning

confidence: 99%

Geophysical evaluation of groundwater potential in part of southwestern Basement Complex terrain of Nigeria

et al. 2017

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The geophysical assessment of groundwater in Awa-Ilaporu, near Ago Iwoye southwestern Nigeria was carried out with the aim of delineating probable areas of high groundwater potential. The area falls within the Crystalline Basement Complex of southwestern Nigeria which is predominantly underlain by banded gneiss, granite gneiss and pegmatite. The geophysical investigation involves the very low frequency electromagnetic (VLF-EM) and Vertical Electrical Sounding (VES) methods. The VLF-EM survey was at 10 m interval along eight traverses ranging between 290 and 700 m in length using ABEM WADI VLF-EM unit. The VLF-EM survey was used to delineate areas with conductive/fractured zones. Twenty-three VES surveys were carried out with the use of Campus Ohmega resistivity meter at different location and at locations areas delineated as high conductive areas by VLF-EM survey. The result of VLF-EM survey along its traverse was used in delineating high conductive/fractured zones, it is, however, in agreement with the delineation of the VES survey. The VES results showed 3-4 geoelectric layers inferred as sandy topsoil, sandy clay, clayey and fractured/fresh basement. The combination of these two methods, therefore, helped in resolving the prospecting location for the groundwater yield in the study area.

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Section: Introductionmentioning

confidence: 99%

Geophysical evaluation of groundwater potential in part of southwestern Basement Complex terrain of Nigeria

et al. 2017

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“…Table 1 and Table 2 are sample of the dataset that was used for the study. The MARS model uses a nonparametric modelling approach that does not require assumptions about the form of the relationship between the independent and dependent variables [37,60]. The MARS model works by dividing the ranges of the explanatory variables into regions and by producing for each of these regions a linear regression equations [60].…”

Section: Resources and Methods Usedmentioning

confidence: 99%

“…The MARS model uses a nonparametric modelling approach that does not require assumptions about the form of the relationship between the independent and dependent variables [37,60]. The MARS model works by dividing the ranges of the explanatory variables into regions and by producing for each of these regions a linear regression equations [60]. The breaks values between each regions are called knots, whiles the term basis functions (BFs) are used to demonstrate each distinct interval of the predictors [45,60].…”

Section: Resources and Methods Usedmentioning

confidence: 99%

“…The MARS model works by dividing the ranges of the explanatory variables into regions and by producing for each of these regions a linear regression equations [60]. The breaks values between each regions are called knots, whiles the term basis functions (BFs) are used to demonstrate each distinct interval of the predictors [45,60]. BFs are functions of the following form as denoted by Equation (1):…”

Section: Resources and Methods Usedmentioning

confidence: 99%

“…Where x is an independent variable and k is a constant corresponding to a knot [60]. The general formula for the MARS model is given by Equation (7) as denoted by [31]:…”

Section: Resources and Methods Usedmentioning

confidence: 99%

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Performance Evaluation of Multivariate Adaptive Regression Splines (MARS) and Multiple Linear Regression (MLR) for Forward Conversion of Geodetic Coordinates (ϕ, λ, h) to Cartesian Coordinates (X, Y, Z)

Peprah¹,

Mensah²,

Akresi³

2017

JGG

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In Ghana's local Geodetic Reference Network, the standard forward transformation equation has played a major role in coordinate transformation between World Geodetic System 1984 (WGS84) and local geodetic datum. Thus, it is an initial step in forward conversion of geodetic coordinates (ϕ , λ, h) to Cartesian coordinates (X, Y, Z) in transformation from global to local datum and vice versa. Several studies in the recent decades have been conducted on converting Cartesian coordinates to geodetic coordinates (reverse procedure) through the utilisation of iterative, approximate, closed form, vector-based and computational intelligence algorithms. However, based on the existing literature covered pertaining to this present study, it was found that the existing knowledge do not fully adhere to the issue of evaluating alternative techniques in the case of the forward conversion. Hence, the aim of this present study was to explore the coordinate conversion performance of the Multivariate Adaptive Regression Splines ( ). The statistical findings revealed that the MARS and MLR offered satisfactory prediction of Cartesian coordinates. However, the MLR compared to MARS showed better stability and more accurate prediction results. From the results of this present study, the main conclusion drawn is that, MLR provides a promising alternative in the forward conversion of geodetic coordinates into Cartesian coordinates. Therefore, the capability of MLR as a powerful tool for solving majority of function approximation problems in mathematical geodesy has been demonstrated in this present study.

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An ANFIS‐based model for solar energy forecasting and its smart grid application

Perveen

Rizwan

Goel

2019

Engineering Reports

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In the present work, a model underlying the principle of adaptive neural‐fuzzy inference system (ANFIS) architecture is proposed. This research investigates the ANFIS‐based model performance to forecast global solar energy under different weather conditions, namely, clear sky, hazy sky, hazy and cloudy sky, and cloudy sky; and for distinct climate zones using salient meteorological parameters. Power generation from solar photovoltaic (PV) may be affected by different factors, such as cloud cover, topographical locations, time, and seasonal variations. Therefore, accurate forecasting of PV power is an important factor for system reliability and robustness. In this work, the proposed model is implemented for smart grid applications in forecasting short‐term PV power in a composite climate zone. Finally, the accuracy of the model is compared with other approaches, namely, support vector machine, feedforward neural network, multivariate adaptive regression splines, generalized regression neural network, linear neural network, fuzzy logic‐based model, and empirical models based on multiple regression analysis using statistical performance indicators. The overall analysis reveals that the ANFIS‐based model outperforms the other models in terms of accuracy.

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GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran

Cited by 169 publications

References 49 publications

Geophysical evaluation of groundwater potential in part of southwestern Basement Complex terrain of Nigeria

Geophysical evaluation of groundwater potential in part of southwestern Basement Complex terrain of Nigeria

Performance Evaluation of Multivariate Adaptive Regression Splines (MARS) and Multiple Linear Regression (MLR) for Forward Conversion of Geodetic Coordinates (ϕ, λ, h) to Cartesian Coordinates (X, Y, Z)

An ANFIS‐based model for solar energy forecasting and its smart grid application

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