Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling

Pourghasemi, Hamid Reza; Yousefi, Saleh; Kornejady, Aiding; Cerdà, Artemi

doi:10.1016/j.scitotenv.2017.07.198

Cited by 288 publications

(160 citation statements)

References 98 publications

(108 reference statements)

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“…Nowadays, the necessity of using machine learning techniques is increasingly emphasized in the susceptibility modeling of geomorphological features and processes 37 . A universal framework describing which factors to compare is required.…”

Section: Discussionmentioning

confidence: 99%

“…The ROC curve represents the trade-off between two rates (the false-positive and true-positive rates on the X and Y axes). The AUC values are interpreted as reflecting the following model accuracies: 0.6-0.7 poor, 0.6-0.7 medium, 0.7-0.8 good, 0.8-0.9 very good, and 0.9-1 excellent 37,38 . In the current study, different techniques and measures were applied to evaluate the robustness and uncertainty of the RF model for three different hazards, namely, floods, forest fires, and landslides.…”

Section: Construction Of Flood Forest Fire and Landslide Conditionimentioning

confidence: 99%

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Assessing and mapping multi-hazard risk susceptibility using a machine learning technique

Pourghasemi

Kariminejad

Amiri

et al. 2020

Sci Rep

Self Cite

168

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the aim of the current study was to suggest a multi-hazard probability assessment in fars province, Shiraz City, and its four strategic watersheds. At first, we construct maps depicting the most effective factors on floods (12 factors), forest fires (10 factors), and landslides (10 factors), and used the Boruta algorithm to prioritize the impact of each respective factor on the occurrence of each hazard. Subsequently, flood, landslides, and forest fire susceptibility maps prepared using a Random Forest (RF) model in the R statistical software. Results indicate that 42.83% of the study area are not susceptible to any hazards, while 2.67% of the area is at risk of all three hazards. The results of the multi-hazard map in Shiraz City indicate that 25% of Shiraz city is very susceptible to flooding, while 16% is very susceptible to landslide occurrences. for four strategic watersheds, it is notable that in the Dorodzan Watershed, landslides and floods are the most important hazards; whereas, flood occurrences cover the largest area of the Maharlou Watershed. In contrast, the Tashk-Bakhtegan Watershed is so sensible to floods and landslides, respectively. Finally, in the Ghareaghaj Watershed, forest fire ranks as the strongest hazard, followed by floods. The validation results indicate an AUC of 0.834, 0.939, and 0.943 for the flood, landslide, and forest fire susceptibility maps, respectively. Also, other accuracy measures including, specificity, sensitivity, TSS, CCI, and Gini coefficient confirmed results of the AUC values. These results allow us to forecast the spatial behavior of such multi-hazard events, and researchers and stakeholders alike can apply them to evaluate hazards under various mitigation scenarios.The Sendai Framework, with its comprehensive vision, recommends more efforts to decrease disaster risk and increase sustainable development. Especially communities who are increasingly susceptible to natural hazards should adhere to these guidelines and plan accordingly. In this regard, the multi-hazard approach is often used in risk reduction projects and studies addressing risks associated with human activities or climate change on a regional and local scale 1 . It is obvious that introducing a universal set of multi-hazard assessment techniques is of fundamental importance for reducing disaster risk, and constitutes a valuable asset to share with other stakeholders, including the private sectors, local governments, and other stakeholders.The use of the term multi-hazard in the current research is related to the objective of risk reduction among natural hazards, including flood, landslides, and forest fires, in a specified spatial distribution in this study 2,3 . Recently, susceptibility modeling approaches related to single processes have advanced considerably for river floods 4 and landslides 5-7 . However, there is still neither a common terminology nor a uniform conceptual approach for analyzing multiple hazards in conjunction. This is not unexpected because multi-hazard analyses are not the sum of ...

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Section: Discussionmentioning

confidence: 99%

Section: Construction Of Flood Forest Fire and Landslide Conditionimentioning

confidence: 99%

Assessing and mapping multi-hazard risk susceptibility using a machine learning technique

Pourghasemi

Kariminejad

Amiri

et al. 2020

Sci Rep

Self Cite

168

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“…SVM is a distinguished supervised classifier and based on statistical learning theory [31]. This method maps the original input space through multiple dimensional space [32].…”

Section: Support Vector Machinementioning

confidence: 99%

“…SVM is a distinguished supervised classifier and based on statistical learning theory . This method maps the original input space through multiple dimensional space . This method minimizes learning error by minimizing structural risk mapping the original finite‐dimensional space into a much higher‐dimensional space where linear learning algorithms can be used .…”

Section: Introductionmentioning

confidence: 99%

A Hybrid clustering and classification technique for forecasting short‐term energy consumption

Torabi

Hashemi

Saybani

et al. 2018

Env Prog and Sustain Energy

122

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This paper presents a hybrid approach to predict the electric energy usage of weather‐sensitive loads. The presented method utilizes the clustering paradigm along with ANN and SVM approaches for accurate short‐term prediction of electric energy usage, using weather data. Since the methodology being invoked in this research is based on CRISP data mining, data preparation has received a great deal of attention in this research. Once data pre‐processing was done, the underlying pattern of electric energy consumption was extracted by the means of machine learning methods to precisely forecast short‐term energy consumption. The proposed approach (CBA‐ANN‐SVM) was applied to real load data and resulting higher accuracy comparing to the existing models. © 2018 American Institute of Chemical Engineers Environ Prog, 38: 66–76, 2019

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“…ML is a type of artificial intelligence (AI) that uses computer algorithms to analyze and forecast information by learning from training data. ML algorithms that have been used for GESM include random forest (RF), boosted regression tree (BRT), support vector machine (SVM), classification and regression trees (CART), artificial neural networks (ANN), stochastic gradient tree-boost (SGT), maximum entropy (ME), and multivariate adaptive regression splines (MARS) [13,[49][50][51][52][53][54][55][56][57][58].…”

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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Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling

Cited by 288 publications

References 98 publications

Assessing and mapping multi-hazard risk susceptibility using a machine learning technique

Assessing and mapping multi-hazard risk susceptibility using a machine learning technique

A Hybrid clustering and classification technique for forecasting short‐term energy consumption

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

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