Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques

Tehrany, Mahyat Shafapour; Jones, Simon; Shabani, Farzin

doi:10.1016/j.catena.2018.12.011

Cited by 214 publications

(85 citation statements)

References 103 publications

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“…For example, using the multivariate discriminant analysis (MDA), classification and regression trees (CART), SVM [22], genetic algorithm rule-set production (GARP), quick unbiased efficient statistical tree (QUEST) [28], ANN [23], adaptive neuro fuzzy inference system (ANFIS) [55] and boosted regression trees (BRT) [9] methods, the researchers successfully predicted and mapped the distribution of flood susceptibilities within different regions of Iran. Similar results have also been reported from USA [56], Australia [57], China [58], Vietnam [6] and Romania [59]. Additionally, the literature consists of several successful experiments of using machine learning methods for the prediction of landslide [39], wildfire [50] and gully erosion [60].…”

Section: Discussionsupporting

confidence: 77%

Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

et al. 2019

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Floods are some of the most destructive and catastrophic disasters worldwide. Development of management plans needs a deep understanding of the likelihood and magnitude of future flood events. The purpose of this research was to estimate flash flood susceptibility in the Tafresh watershed, Iran, using five machine learning methods, i.e., alternating decision tree (ADT), functional tree (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), and quadratic discriminant analysis (QDA). A geospatial database including 320 historical flood events was constructed and eight geo-environmental variables—elevation, slope, slope aspect, distance from rivers, average annual rainfall, land use, soil type, and lithology—were used as flood influencing factors. Based on a variety of performance metrics, it is revealed that the ADT method was dominant over the other methods. The FT method was ranked as the second-best method, followed by the KLR, MLP, and QDA. Given a few differences between the goodness-of-fit and prediction success of the methods, we concluded that all these five machine-learning-based models are applicable for flood susceptibility mapping in other areas to protect societies from devastating floods.

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Section: Discussionsupporting

confidence: 77%

Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

et al. 2019

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“…This model is used to determine and select the classes that maximize the posterior probabilities using the following steps: (a) Collecting the samples, (b) assessing a prior probability for any defined class, (c) assessing the mean value of defined classes, (d) creating the covariance matrix and assessing the inverse and determinant for any defined class [100] and (e) structuring the discriminant function for any defined class [101]. There exist different kernel functions such as linear, polynomial and Gaussian [102].…”

Section: Characteristic Functionmentioning

confidence: 99%

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

et al. 2019

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Although snow avalanches are among the most destructive natural disasters, and result in losses of life and economic damages in mountainous regions, far too little attention has been paid to the prediction of the snow avalanche hazard using advanced machine learning (ML) models. In this study, the applicability and efficiency of four ML models: support vector machine (SVM), random forest (RF), naïve Bayes (NB) and generalized additive model (GAM), for snow avalanche hazard mapping, were evaluated. Fourteen geomorphometric, topographic and hydrologic factors were selected as predictor variables in the modeling. This study was conducted in the Darvan and Zarrinehroud watersheds of Iran. The goodness-of-fit and predictive performance of the models was evaluated using two statistical measures: the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS). Finally, an ensemble model was developed based upon the results of the individual models. Results show that, among individual models, RF was best, performing well in both the Darvan (AUROC = 0.964, TSS = 0.862) and Zarrinehroud (AUROC = 0.956, TSS = 0.881) watersheds. The accuracy of the ensemble model was slightly better than all individual models for generating the snow avalanche hazard map, as validation analyses showed an AUROC = 0.966 and a TSS = 0.865 in the Darvan watershed, and an AUROC value of 0.958 and a TSS value of 0.877 for the Zarrinehroud watershed. The results indicate that slope length, lithology and relative slope position (RSP) are the most important factors controlling snow avalanche distribution. The methodology developed in this study can improve risk-based decision making, increases the credibility and reliability of snow avalanche hazard predictions and can provide critical information for hazard managers.

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“…Using Eqs. (4) and (6), the entropy of this index ranges from 0 to 0.43 and 0 to 0.44, in the absence and presence of check dams, respectively (Figure 3) (Wang et al 2015;Sepehri et al 2017;Tehrany et al 2019).…”

Section: Resultsmentioning

confidence: 99%

“…The first step in this regard is to select appropriate indices (Wagener et al 2004;Razavi and Coulibaly 2012;Sepehri et al 2019b). Flood risk variables vary from region to region based on the specific features of each (Tehrany et al 2019). An indicator that may be important in flood studies in a region may not be important in another area (Kia et al 2012).…”

Section: Flood Indicesmentioning

confidence: 99%

Assessment of check dams’ role in flood hazard mapping in a semi-arid environment

Sepehri

Ildoromi

Malekinezhad

et al. 2019

Geomatics, Natural Hazards and Risk

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This study aimed to examine flood hazard zoning and assess the role of check dams as effective hydraulic structures in reducing flood hazards. To this end, factors associated with topographic, hydrologic and human characteristics were used to develop indices for flood mapping and assessment. These indices and their components were weighed for flood hazard zoning using two methods: (i) a multi-criterion decision-making model in fuzzy logic and (ii) entropy weight. After preparing the flood hazard map by using the above indices and methods, the characteristics of the change-point were used to assess the role of the check dams in reducing flood risk. The method was used in the Ilanlu catchment, located in the northwest of Hamadan province, Iran, where it is prone to frequent flood events. The results showed that the area of 'very low', 'low' and 'moderate' flood hazard zones increased from about 2.2% to 7.3%, 8.6% to 19.6% and 22.7% to 31.2% after the construction of check dams, respectively. Moreover, the area of 'high' and 'very high' flood hazard zones decreased from 39.8% to 29.6%, and 26.7% to 12.2%, respectively.

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Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques

Cited by 214 publications

References 103 publications

Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

Assessment of check dams’ role in flood hazard mapping in a semi-arid environment

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