Journal ArticleDOI
GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran
Reads0
Chats0
TLDR
In this article, the authors evaluated and compared groundwater spring potential maps produced with two different models, namely multivariate adaptive regression spline (MARS) and random forest (RF), using geographic information system (GIS).Abstract:
This study evaluated and compared groundwater spring potential maps produced with two different models—namely multivariate adaptive regression spline (MARS) and random forest (RF)—using geographic information system (GIS). In total, 234 spring locations were identified in the Boujnord, North Khorasan, Iran and a GIS spring inventory map was prepared. Of these, 176 (70 %) locations were employed to produce spring potential maps (training), while the remaining 58 (30 %) cases were used to validate the model. The explanatory variables used to predict spring location were altitude, slope aspect, slope degree, slope length, topographic wetness index (TWI), plan curvature, profile curvature, land use, lithology, distance to rivers, drainage density, distance to faults, and fault density. Furthermore, the spatial relationships between spring occurrence and explanatory variables were performed using a Certainty Factor (CF) model. For validation, area under a receiver operating characteristics (ROC) curves (AUC) was used. The validation results showed that the AUC for calibration is almost identical (0.79) in both models, while for prediction, the MARS model (73.26 %) performed better than RF (70.98 %) model. These results indicate that the MARS and RF models are good estimators of groundwater spring potential in the study area. These groundwater spring potential maps can be applied to groundwater management and groundwater resource exploration.read more
Citations
More filters
Journal ArticleDOI
Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms.
Seyed Vahid Razavi Termeh,Aiding Kornejady,Hamid Reza Pourghasemi,Saskia Keesstra,Saskia Keesstra +4 more
TL;DR: The ANFIS-PSO was found to be the most practical model in term of producing the highly focused flood susceptibility map with lesser spatial distribution related to highly susceptible classes, and was introduced as the premier model in the study area.
Journal ArticleDOI
GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models
Wei Chen,Hui Li,Enke Hou,Wang Shengquan,Guirong Wang,Mahdi Panahi,Tao Li,Tao Peng,Chen Guo,Niu Chao,Lele Xiao,Jiale Wang,Xiaoshen Xie,Baharin Bin Ahmad +13 more
TL;DR: The prediction capability of the FT model, with the highest AUC values, the smallest standard errors, the narrowest CIs, and the smallest P values for the training and validation datasets, is better compared to those of other models.
Journal ArticleDOI
Assessment of groundwater potential zones using multi-influencing factor (MIF) and GIS: a case study from Birbhum district, West Bengal
TL;DR: In this article, the authors proposed a method for delineation of groundwater potential zone in Birbhum district by using remote sensing and GIS data, where various thematic layers viz. geology, geomorphology, soil type, elevation, lineament and fault density are digitized and transformed into raster data in ArcGIS 10.3 environment as input factors.
Journal ArticleDOI
Landslide susceptibility assessment using maximum entropy model with two different data sampling methods
TL;DR: In this paper, the authors used maximum entropy (ME) as a machine learning model, with two sampling strategies: Mahalanobis distance (MEMD) and random sampling (MERS), to map landslide susceptibility over the Ziarat watershed in the Golestan Province, Iran.
Journal ArticleDOI
Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS
TL;DR: This study aims to validate the performance of C5.0, random forest (RF), and multivariate adaptive regression splines (MARS) algorithms for generating GPMs in the eastern part of Mashhad Plain, Iran and indicated that AUC-ROC values for the employed models are more than 70% which shows their acceptable performance.
References
More filters
Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Book
C4.5: Programs for Machine Learning
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Classification and Regression by randomForest
Andy Liaw,Matthew C. Wiener +1 more
TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.
Book
Classification and regression trees
TL;DR: The methodology used to construct tree structured rules is the focus of a monograph as mentioned in this paper, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Journal ArticleDOI
Measuring the accuracy of diagnostic systems
TL;DR: For diagnostic systems used to distinguish between two classes of events, analysis in terms of the "relative operating characteristic" of signal detection theory provides a precise and valid measure of diagnostic accuracy.