Gully Erosion Modeling Using GIS-Based Data Mining Techniques in Northern Iran: A Comparison Between Boosted Regression Tree and Multivariate Adaptive Regression Spline

Zabihi, Mohsen; Pourghasemi, Hamid Reza; Motevalli, Alireza; Zakeri, Mohamad Ali

doi:10.1007/978-3-319-73383-8_1

Cited by 37 publications

(24 citation statements)

References 73 publications

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“…This has many benefits over recursive tree construction techniques such as classification and regression tree (CART) [51]. Furthermore, it is unbiased in selecting splitting rules and does not use an exhaustive variable search routine [52]. By using an unbiased variable selection method in modeling, QUEST has negligible bias [53].…”

Section: Quick Unbiased Efficient Statistical Tree (Quest)mentioning

confidence: 99%

A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models

Moghaddam

Rahmati

Haghizadeh

et al. 2020

Water

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In some arid regions, groundwater is the only source of water for human needs, so understanding groundwater potential is essential to ensure its sustainable use. In this study, three machine learning models (Genetic Algorithm for Rule-Set Production (GARP), Quick Unbiased Efficient Statistical Tree (QUEST), and Random Forest (RF)) were applied and verified for spatial prediction of groundwater in a mountain bedrock aquifer in Piranshahr Watershed, Iran. A spring location dataset consisting of 141 springs was prepared by field surveys, and from this three different sample datasets (S1–S3) were randomly generated (70% for training and 30% for validation). A total of 10 groundwater conditioning factors were prepared for modeling, namely slope percent, relative slope position (RSP), plan curvature, altitude, drainage density, slope aspect, topographic wetness index (TWI), terrain ruggedness index (TRI), land use, and lithology. The area under the receiver operating characteristic curve (AUC) and true skill statistic (TSS) were used to evaluate the accuracy of models. The results indicated that all models had excellent goodness-of-fit and predictive performance, but that RF (AUCmean = 0.995, TSSmean = 0.89) and GARP (AUCmean = 0.957, TSSmean = 0.82) outperformed QUEST (AUCmean = 0.949, TSSmean = 0.74). In robustness analysis, RF was slightly more sensitive than GARP and QUEST, making it necessary to consider several random partitioning options for preparing training and validation groups. The outcomes of this study can be useful in sustainable management of groundwater resources in the study region.

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Section: Quick Unbiased Efficient Statistical Tree (Quest)mentioning

confidence: 99%

A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models

Moghaddam

Rahmati

Haghizadeh

et al. 2020

Water

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“…Soil erosion in the form of gully erosion is a serious global problem, and it continues to pose a threat to soil and water resources, particularly in arid and semi-arid regions of Iran [ 2 , 3 ]. Among the several types of water-induced erosion, gully erosion is a more intense form of soil erosion [ 4 ] and is one of the most complex geomorphic phenomena on the Earth’s surface [ 5 ]. Such erosional activities also change the shape of the Earth’s landform and produce a rugged topography, which is not suitable for production activities, construction of communication networks, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Some of the widely used statistical methods to predict GES mapping are frequency ratio [ 7 ], logistic regression [ 18 ], weight of evidence (WoE) [ 19 ], index of entropy (IoE) [ 5 ], etc. Besides statistical methods, different ML algorithms have also been widely used to predict GES mapping such as artificial neural network (ANN) [ 20 ], support vector machine (SVM) [ 20 ], random forest (RF) (Hosseinalizadeh et al 2019), multi-layer perception (MLPC) approaches [ 21 ], classification and regression tree (CART) [ 22 ], boosted regression tree (BRT) [ 7 ], particle swarm optimization (PSO) [ 23 ], multi-variate adaptive regression spline (MARS) [ 5 ], and maximum entropy [ 24 ]. Ensemble models have also been widely used for their novelties and capabilities in the comprehensive analysis of GES mapping [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Band

Janizadeh

Pal

et al. 2020

Sensors

129

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This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.

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“…This is a suggestion for future research efforts in species distribution modeling [82][83][84]. In general, there are different data mining techniques that are applied in geosciences and environmental engineering in cases of landslides, forest fires, land subsidence, flood, and gully erosion, including random forest-RF [85][86][87][88][89][90], support vector machine-SVM [90][91][92], multivariate adaptive spline regression-MARS [93,94], and boosted regression tree-BRT [95]. Even in a case on groundwater modeling, Rahmati et al 2016 [69] considered a comparison between two data mining techniques, including random forest and Maxent models.…”

Section: Validation Of the Hsi Maps And Comparison Between Fr And Maxmentioning

confidence: 99%

Maxent Data Mining Technique and Its Comparison with a Bivariate Statistical Model for Predicting the Potential Distribution of Astragalus Fasciculifolius Boiss. in Fars, Iran

et al. 2019

Self Cite

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The identification of geographical distribution of a plant species is crucial for understanding the importance of environmental variables affecting plant habitat. In the present study, the spatial potential distribution of Astragalus fasciculifolius Boiss. as a key specie was mapped using maximum entropy (Maxent) as data mining technique and bivariate statistical model (FR: frequency ratio) in marl soils of southern Zagros, Iran. The A. fasciculifolius locations were identified and recorded by intensive field campaigns. Then, localities points were randomly split into a 70% training dataset and 30% for validation. Two climatic, four topographic, and eight edaphic variables were used to model the A. fasciculifolius distribution and its habitat potential. Maps of environmental variables were generated using Geographic Information System (GIS). Next, the habitat suitability index (HSI) maps were produced and classified by means of Maxent and FR approaches. Finally, the area under the receiver operating characteristic (AUC-ROC) curve was used to compare the performance of maps produced by Maxent and FR models. The interpretation of environmental variables revealed that the climatic and topographic parameters had less impact compared to edaphic variables in habitat distribution of A. fasciculifolius. The results showed that bulk density, nitrogen, acidity (pH), sand, and electrical conductivity (EC) of soil are the most significant variables that affect distribution of A. fasciculifolius. The validation of results showed that AUC values of Maxent and FR models are 0.83 and 0.76, respectively. The habitat suitability map by the better model (Maxent) showed that areas with high and very high suitable classes cover approximately 22% of the study area. Generally, the habitat suitability map produced using Maxent model could provide important information for conservation planning and a reclamation project of the degraded habitat of intended plant species. The distribution of the plants identifies the water, soil, and nutrient resources and affects the fauna distribution, and this is why it is relevant to research and to understand the plant distribution to properly improve the management and to achieve a sustainable management.

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Gully Erosion Modeling Using GIS-Based Data Mining Techniques in Northern Iran: A Comparison Between Boosted Regression Tree and Multivariate Adaptive Regression Spline

Cited by 37 publications

References 73 publications

A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models

A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models

Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Maxent Data Mining Technique and Its Comparison with a Bivariate Statistical Model for Predicting the Potential Distribution of Astragalus Fasciculifolius Boiss. in Fars, Iran

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