A novel hybrid artificial intelligence approach for flood susceptibility assessment

Chapi, Kamran; Singh, Vijay P.; Shirzadi, Ataollah; Shahabi, Himan; Bui, Dieu Tien; Pham, Binh Thai; Khosravi, Khabat

doi:10.1016/j.envsoft.2017.06.012

Cited by 460 publications

(260 citation statements)

References 67 publications

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“…LR is a multivariate statistical technique, in which the dependent variable should be binary or dichotomous, such as 0 and 1, or presence and absence of an event, whereas independent variables (conditioning factors) can be continuous and categorical (Chapi et al, ; Shahabi, Khezri, Ahmad, & Hashim, ; Shirzadi, Saro, Joo, & Chapi, ). It is a generalization of a linear model whereby relationships between the probability of sinkhole occurrence and the 10 independent variables can be quantitatively computed as follows:

P_{LR} = \frac{e^{z}}{1 + e^{z}},

Z = log italicit ((), P) = italicLn ((), \frac{P}{1 - P}) = c_{0} + a_{1} x_{1}, a_{2} x_{2}, \dots, a_{n} x_{n},

where P LR is the probability of sinkhole occurrence, Z is the weighted linear combination of the independent variables, c 0 is the constant or intercept of model, a i ( i = 0, 1, 2, … , n ) are the coefficients, and x i = ( i = 0, 1, 2, … , n ) are the independent variables (Chen, Pourghasemi, & Zhao, ).…”

Section: Methodsmentioning

confidence: 99%

“…The BLR classifier is a combination of the NB classifier and the LR function. This classifier constructs the relationships between dependent (sinkhole and no sinkhole) and independent (10 conditioning factors) variables (Chapi et al, ). The Bayesian framework is constructed in three steps: First, the PP is specified for each parameter, then the likelihood function is obtained for the dataset, and finally, the posterior probability distribution for the parameters is calculated (Avali, Cooper, & Gopalakrishnan, ).…”

Section: Methodsmentioning

confidence: 99%

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Sinkhole susceptibility mapping: A comparison between Bayes‐based machine learning algorithms

Taheri¹,

Shahabi

Chapi

et al. 2019

Land Degrad Dev

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Land degradation has been recognized as one of the most adverse environmental impacts during the last century. The occurrence of sinkholes is increasing dramatically in many regions worldwide contributing to land degradation. The rise in the sinkhole frequency is largely due to human‐induced hydrological alterations that favour dissolution and subsidence processes. Mitigating detrimental impacts associated with sinkholes requires understanding different aspects of this phenomenon such as the controlling factors and the spatial distribution patterns. This research illustrates the development and validation of sinkhole susceptibility models in Hamadan Province, Iran, where a large number of sinkholes are occurring under poorly understood circumstances. Several susceptibility models were developed with a training sample of sinkholes, a number of conditioning factors, and four different statistical approaches: naïve Bayes, Bayes net (BN), logistic regression, and Bayesian logistic regression. Ten conditioning factors were initially considered. Factors with negligible contribution to the quality of predictions, according to the information gain ratio technique, were discarded for the development of the final models. The validation of susceptibility models, performed using different statistical indices and receiver operating characteristic curves, revealed that the BN model has the highest prediction capability in the study area. This model provides reliable predictions on the future distribution of sinkholes, which can be used by watershed and land use managers for designing hazard and land‐degradation mitigation plans.

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P_{LR} = \frac{e^{z}}{1 + e^{z}},

Z = log italicit ((), P) = italicLn ((), \frac{P}{1 - P}) = c_{0} + a_{1} x_{1}, a_{2} x_{2}, \dots, a_{n} x_{n},

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Sinkhole susceptibility mapping: A comparison between Bayes‐based machine learning algorithms

Taheri¹,

Shahabi

Chapi

et al. 2019

Land Degrad Dev

Self Cite

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“…Among natural disasters, floods are considered to be one of the most devastating [1], and an accurate assessment of its risks is hampered by a lack of data and knowledge about flood losses at different scales [2]. During a heavy rainfall event, the amount of flow discharge in a river increases rapidly and the water level will rise above its normal bed, covering the flood plain and surrounding areas [3]. Life-threatening water overflow in residential areas is a common occurrence in Iran after earthquakes (http://www3.irna.ir/fa/NewsPrint.aspx?ID=214943), and catastrophic flooding events happen annually in the Mazandaran, Guilan, and Golestan Provinces in Northern Iran.…”

Section: Introductionmentioning

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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“…Various data-mining approaches have been used for decades to predict and assess flood occurrence [11,12]. In general, soft computing or statistical data-mining techniques have recently been used, such as the decision-tree based model [13][14][15], artificial neural network (ANN) based model [16][17][18], support-vector machine [19][20][21] or the logistic regression (LR) model [22,23]. Proactively, a basic statistical model, frequency ratio (FR), is used to observe trends between flood-related factors and flood events [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Spatial Assessment of Urban Flood Susceptibility Using Data Mining and Geographic Information System (GIS) Tools

Lee

et al. 2018

Sustainability

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Using geographic information system (GIS) tools and data-mining models, this study analyzed the relationships between flood areas and correlated hydrological factors to map the regional flood susceptibility of the Seoul metropolitan area in South Korea. We created a spatial database of data describing factors including topography, geology, soil, and land use. We used 2010 flood data for training and 2011 data for model validation. Frequency ratio (FR) and logistic regression (LR) models were applied to 2010 flood data to determine the relationships between the flooded area and its causal factors and to derive flood-susceptibility maps, which were substantiated using the area flooded in 2011 (not used for training). As a result of the accuracy validation, FR and LR model results were shown to have 79.61% and 79.05% accuracy, respectively. In terms of sustainability, floods affect water health as well as causing economic and social damage. These maps will provide useful information to decision makers for the implementation of flood-mitigation policies in priority areas in urban sustainable development and for flood prevention and management. In addition to this study, further analysis including data on economic and social activities, proximity to nature, and data on population and building density, will make it possible to improve sustainability.

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A novel hybrid artificial intelligence approach for flood susceptibility assessment

Cited by 460 publications

References 67 publications

Sinkhole susceptibility mapping: A comparison between Bayes‐based machine learning algorithms

Sinkhole susceptibility mapping: A comparison between Bayes‐based machine learning algorithms

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

Spatial Assessment of Urban Flood Susceptibility Using Data Mining and Geographic Information System (GIS) Tools

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