Drought sensitivity mapping using two one-class support vector machine algorithms

Roodposhti, Majid Shadman; Safarrad, Taher; Shahabi, Himan

doi:10.1016/j.atmosres.2017.04.017

Cited by 82 publications

(43 citation statements)

References 45 publications

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“…Machine learning techniques have recently gained good attention among the environmental modeling research community as they are advantageous in efficiently capturing the complex relationship between the environmental predictors and the response, such as flood [33][34][35][36][37][38][39][40][41], wildfire [42], sinkhole [43], drought [44], gully erosion [45,46], groundwater [47][48][49] and land/ground subsidence [27], and landslide in this case [3,13,[50][51][52][53][54][55][56][57]. In due course, researches have also attempted to improve the prediction accuracy and the interpretability of the models through applying various decision-trees machine learning algorithms such as chi-square automatic interaction detector; quick, unbiased and efficient statistical tree [58]; J48 decision trees [59]; ID3 decision trees [60]; random forests [61]; classification and regression trees [62]; alternating decision trees [63]; reduced error pruning trees [3]; naïve Bayes [35,53]; naïve Bayes tree [13,64]; kernel logistic regression [37]; logistic model tree [38,…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

et al. 2019

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Landslides have multidimensional effects on the socioeconomic as well as environmental conditions of the impacted areas. The aim of this study is the spatial prediction of landslide using hybrid machine learning models including bagging (BA), random subspace (RS) and rotation forest (RF) with alternating decision tree (ADTree) as base classifier in the northern part of the Pithoragarh district, Uttarakhand, Himalaya, India. To construct the database, ten conditioning factors and a total of 103 landslide locations with a ratio of 70/30 were used. The significant factors were determined by chi-square attribute evaluation (CSEA) technique. The validity of the hybrid models was assessed by true positive rate (TP Rate), false positive rate (FP Rate), recall (sensitivity), precision, F-measure and area under the receiver operatic characteristic curve (AUC). Results concluded that land cover was the most important factor while curvature had no effect on landslide occurrence in the study area and it was removed from the modelling process. Additionally, results indicated that although all ensemble models enhanced the power prediction of the ADTree classifier (AUC training = 0.859; AUC validation = 0.813); however, the RS ensemble model (AUC training = 0.883; AUC validation = 0.842) outperformed and outclassed the RF (AUC training = 0.871; AUC validation = 0.840), and the BA (AUC training = 0.865; AUC validation = 0.836) ensemble model. The obtained results would be helpful for recognizing the landslide prone areas in future to better manage and decrease the damage and negative impacts on the environment.

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Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

et al. 2019

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“…RF modeling is a relatively new ensemble algorithm that increases the accuracy and diversity of base classifiers, and it was first proposed by Rodriguez et al [39]. The success of RF modeling depends on the rotation matrix generated by transformations and base classifiers [61,62].…”

Section: Rotational Forest (Rf)mentioning

confidence: 99%

“…Recently, machine-learning (ML) techniques have become popular for the spatial prediction of natural hazards like wildfires [22], sinkholes [23], groundwater depletion and flooding [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38], droughts [39], earthquakes [40], land subsidence [41], and landslides [42][43][44][45][46][47][48]. ML is a type of artificial intelligence (AI) that uses computer algorithms to analyze and forecast information by learning from training data.…”

Section: Introductionmentioning

confidence: 99%

Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran

Arabameri

Chen

Blaschke

et al. 2019

Water

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To more effectively prevent and manage the scourge of gully erosion in arid and semi-arid regions, we present a novel-ensemble intelligence approach-bagging-based alternating decision-tree classifier (bagging-ADTree)-and use it to model a landscape's susceptibility to gully erosion based on 18 gully-erosion conditioning factors. The model's goodness-of-fit and prediction performance are compared to three other machine learning algorithms (single alternating decision tree, rotational-forest-based alternating decision tree (RF-ADTree), and benchmark logistic regression). To achieve this, a gully-erosion inventory was created for the study area, the Chah Mousi watershed, Iran by combining archival records containing reports of gully erosion, remotely sensed data from Google Earth, and geolocated sites of gully head-cuts gathered in a field survey. A total of 119 gully head-cuts were identified and mapped. To train the models' analysis and prediction capabilities, 83 head-cuts (70% of the total) and the corresponding measures of the conditioning factors were input into each model. The results from the models were validated using the data pertaining to the remaining 36 gully locations (30%). Next, the frequency ratio is used to identify which conditioning-factor classes have the strongest correlation with gully erosion. Using random-forest modeling, the relative importance of each of the conditioning factors was determined. Based on the random-forest results, the top eight factors in this study area are distance-to-road, drainage density, distance-to-stream, LU/LC, annual precipitation, topographic wetness index, NDVI, and elevation. Finally, based on goodness-of-fit and AUROC of the success rate curve (SRC) and prediction rate curve (PRC), the results indicate that the bagging-ADTree ensemble model had the best performance, with SRC (0.964) and PRC (0.978). RF-ADTree (SRC = 0.952 and PRC = 0.971), ADTree (SRC = 0.926 and PRC = 0.965), and LR (SRC = 0.867 and PRC = 0.870) were the subsequent best performers. The results also indicate that bagging and RF, as meta-classifiers, improved the performance of the ADTree model as a base classifier. The bagging-ADTree model's results indicate that 24.28% of the study area is classified as having high and very high susceptibility to gully erosion. The new ensemble model accurately Water 2020, 12, 16 2 of 26 identified the areas that are susceptible to gully erosion based on the past patterns of formation, but it also provides highly accurate predictions of future gully development. The novel ensemble method introduced in this research is recommended for use to evaluate the patterns of gullying in arid and semi-arid environments and can effectively identify the most salient conditioning factors that promote the development and expansion of gullies in erosion-susceptible environments.

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“…Their finding show that although MCDM models could predict flood-prone areas, the data mining algorithms had a higher prediction power than MCDMs since MCDMs rely on expert opinion. Arabameri et al [28] applied an EBF model to the generation of flood susceptibility maps and compared the results with FR, TOPSIS, and VIKOR models, concluding that the EBF model performed best.Recently, hybrid machine learning methods have been applied to studies relating to the spatial prediction of natural hazards such as landslides [12,20,, wildfires [50], sinkholes [51], droughts [52], gully erosion [53,54], and groundwater [55,56] and land/ground subsidence [12]. An advantage of the ensemble algorithms is that they have a higher goodness-of-fit and prediction accuracy than individual or single-based methods/algorithms.…”

mentioning

confidence: 99%

“…Recently, hybrid machine learning methods have been applied to studies relating to the spatial prediction of natural hazards such as landslides [12,20,, wildfires [50], sinkholes [51], droughts [52], gully erosion [53,54], and groundwater [55,56] and land/ground subsidence [12]. An advantage of the ensemble algorithms is that they have a higher goodness-of-fit and prediction accuracy than individual or single-based methods/algorithms.…”

mentioning

confidence: 99%

Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model

et al. 2019

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Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improved flood management and decision making. The main objective of this study was to evaluate the performance of an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, in preparing flood susceptibility maps for the Haraz Catchment in the Mazandaran Province, Iran. The spatial database created consisted of a flood inventory, altitude, slope angle, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from river, rainfall, geology, land use, and Normalized Difference Vegetation Index (NDVI) for the region. After obtaining the required information from various sources, 151 of 211 recorded flooding points were used for model training and preparation of the flood susceptibility maps. For validation, the results of the models were compared to the 60 remaining flooding points. The Receiver Operating Characteristic (ROC) curve was drawn, and the Area Under the Curve (AUC) was calculated to obtain the accuracy of the flood susceptibility maps prepared through success rates (using training data) and prediction rates (using validation data). The AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%, respectively. The results showed that the EBF model had the highest accuracy in predicting flood susceptibility within the catchment, in which 15% of the total areas were located in high and very high susceptibility classes, and 62% were located in low and very low susceptibility classes. These results can be used for the planning and management of areas vulnerable to floods in order to prevent flood-induced damage; the results may also be useful for natural disaster assessment.In recent years, many statistical and probabilistic models have been tested to prepare flood susceptibility maps [9,10]. Geographic Information System (GIS) has been used as an effective tool for spatial analysis and data manipulation due to its ability to handle large amounts of spatial data [11]. Specifically, the combination of statistical and probabilistic models with Remote Sensing (RS) and GIS has been widely used by different researchers [1,12]. Additionally, some scientists and researchers have studied natural disasters, specifically floods and FSM, with the help of RS and GIS, using different models such as Decision-Tree (DT) [6,13], Support Vector Machine (SVM) [14,15], Frequency Ratio (FR) [16,17], Evidential Belief Function (EBF) [18][19][20], EBF-AHP (Analytical Hierarchy Process) [21], Logistic Regression (LR) [22], Shannon's entropy and...

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Drought sensitivity mapping using two one-class support vector machine algorithms

Cited by 82 publications

References 45 publications

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms

Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran

Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model

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