Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis

doi:10.1080/19475705.2015.1045043

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Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis

Journal Article•DOI•

Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis

Omid Rahmati¹, Hossein Zeinivand¹, Mosa Besharat¹•Institutions (1)

Lorestan University¹

03 May 2016-Geomatics, Natural Hazards and Risk (Taylor & Francis)-Vol. 7, Iss: 3, pp 1000-1017

TL;DR: In this article, the authors assess the efficiency of analytical hierarchical process (AHP) to identify potential flood hazard zones by comparing with the results of a hydraulic model, and the normalized weights of criteria/parameters were determined based on Saaty's nine-point scale and its importance in specifying flood hazard potential zones using the AHP and eigenvector methods.

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Abstract: Flood is considered to be the most common natural disaster worldwide during the last decades. Flood hazard potential mapping is required for management and mitigation of flood. The present research was aimed to assess the efficiency of analytical hierarchical process (AHP) to identify potential flood hazard zones by comparing with the results of a hydraulic model. Initially, four parameters via distance to river, land use, elevation and land slope were used in some part of the Yasooj River, Iran. In order to determine the weight of each effective factor, questionnaires of comparison ratings on the Saaty's scale were prepared and distributed to eight experts. The normalized weights of criteria/parameters were determined based on Saaty's nine-point scale and its importance in specifying flood hazard potential zones using the AHP and eigenvector methods. The set of criteria were integrated by weighted linear combination method using ArcGIS 10.2 software to generate flood hazard prediction map. The inundation...

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Journal Article•DOI•

A novel hybrid artificial intelligence approach for flood susceptibility assessment

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Kamran Chapi¹, Vijay P. Singh², Ataollah Shirzadi¹, Himan Shahabi¹, Dieu Tien Bui³, Binh Thai Pham, Khabat Khosravi - Show less +3 more•Institutions (3)

University of Kurdistan¹, Texas A&M University², University College of Southeast Norway³

01 Sep 2017-Environmental Modelling and Software

TL;DR: Results indicate that the proposed Bagging-LMT model can be used for sustainable management of flood-prone areas and outperformed all state-of-the-art benchmark soft computing models.

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Abstract: A new artificial intelligence (AI) model, called Bagging-LMT - a combination of bagging ensemble and Logistic Model Tree (LMT) - is introduced for mapping flood susceptibility. A spatial database was generated for the Haraz watershed, northern Iran, that included a flood inventory map and eleven flood conditioning factors based on the Information Gain Ratio (IGR). The model was evaluated using precision, sensitivity, specificity, accuracy, Root Mean Square Error, Mean Absolute Error, Kappa and area under the receiver operating characteristic curve criteria. The model was also compared with four state-of-the-art benchmark soft computing models, including LMT, logistic regression, Bayesian logistic regression, and random forest. Results revealed that the proposed model outperformed all these models and indicate that the proposed model can be used for sustainable management of flood-prone areas.

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372 citations

Cites background from "Flood hazard zoning in Yasooj regio..."

...…bivariate and statistical models, have are frequency ratio (Lee, 2012; Tehrany et al., 2015a), analytical hierarchy process (Kazakis et al., 2015; Rahmati et al., 2016), logistic regression (Fekete, 2009; Tehrany et al., 2014a), and weights-of evidence (WOE) (Rahmati et al., 2016; Tehrany et…...
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...2014; Rahmati et al., 2016; Tehrany et al., 2015b; Wanders et al., 2014)....
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...…and statistical models, have are frequency ratio (Lee, 2012; Tehrany et al., 2015a), analytical hierarchy process (Kazakis et al., 2015; Rahmati et al., 2016), logistic regression (Fekete, 2009; Tehrany et al., 2014a), and weights-of evidence (WOE) (Rahmati et al., 2016; Tehrany et al., 2014b)....
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...Examples of the GIS-spatial models in flood studies, including bivariate and statistical models, have are frequency ratio (Lee, 2012; Tehrany et al., 2015a), analytical hierarchy process (Kazakis et al., 2015; Rahmati et al., 2016), logistic regression (Fekete, 2009; Tehrany et al., 2014a), and weights-of evidence (WOE) (Rahmati et al., 2016; Tehrany et al., 2014b)....
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Journal Article•DOI•

Suitability estimation for urban development using multi-hazard assessment map.

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George D. Bathrellos¹, Hariklia D. Skilodimou¹, Konstantinos Chousianitis, Ahmed M. Youssef², Biswajeet Pradhan³, Biswajeet Pradhan⁴ - Show less +2 more•Institutions (4)

National and Kapodistrian University of Athens¹, Sohag University², Universiti Putra Malaysia³, Sejong University⁴

01 Jan 2017-Science of The Total Environment

TL;DR: The produced suitability map for urban development proves a satisfactory agreement between the suitability zones and the landslide and flood phenomena that have affected the study area.

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303 citations

Cites background or methods from "Flood hazard zoning in Yasooj regio..."

...Alternatively, several researchers have used flood hazard models via the AHP method to define flood prone areas (i.e. Bathrellos et al., 2016; Fernández and Lutz, 2010; Papaioannou et al., 2015; Rahmati et al., 2016; Stefanidis and Stathis, 2013)....
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...The selection of the factors and the determination of the class numbers as well as their boundary values was based on a review of the literature (Bathrellos et al., 2016; Fernández and Lutz, 2010; Rahmati et al., 2016; Stefanidis and Stathis, 2013), personal experience and data availability....
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...Elevation is an important aspect of assessingflood hazard, as it influences the runoff direction movement, the extent and the depth of theflood (Rahmati et al., 2016)....
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Journal Article•DOI•

A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique

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Khabat Khosravi¹, Ebrahim Nohani², Edris Maroufinia², Hamid Reza Pourghasemi³•Institutions (3)

Sari Agricultural Sciences and Natural Resources University¹, Islamic Azad University², Shiraz University³

25 May 2016-Natural Hazards

TL;DR: In this paper, the authors used four models, namely frequency ratio (FR), weights-of-evidence (WofE), analytical hierarchy process (AHP), and ensemble of frequency ratio with AHP (FR-AHP) to compare them at Haraz Watershed in Mazandaran Province, Iran.

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Abstract: Flood is one of the most prevalent natural disasters that frequently occur in the northern part of Iran reported in hot spots of flood occurrences The main aim of the current study was to prepare flood susceptibility maps using four models, namely frequency ratio (FR), weights-of-evidence (WofE), analytical hierarchy process (AHP), and ensemble of frequency ratio with AHP (FR-AHP), and to compare them at Haraz Watershed in Mazandaran Province, Iran A total of 211 flood locations were prepared in GIS environment, of which 151 locations were randomly selected for modeling and the remaining 60 locations were used for validation aims In the next step, 10 flood-conditioning factors were prepared including slope angle, plan curvature, elevation, topographic wetness index, stream power index, rainfall, distance from river, geology, landuse, and normalized difference vegetation index The receiver operating characteristic curve and the area under the curve (AUC) were created for different flood susceptibility maps Validation of results showed that AUC values for success rate in training data set, for FR, WofE, AHP, and FR-AHP, were 9707, 9896, 9591, and 8619 % with prediction rates of 09657 (9657 %), 09596 (9596 %), 09492 (9492 %), and 08469 (8469 %), respectively Moreover, the results showed that the frequency ratio model had the highest AUC in comparison with other models Generally, the four models show a reasonable accuracy in flood-susceptible areas The results of this study can be useful for managers, researchers, and planners to manage the susceptible areas to flood and reduce damages

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295 citations

Journal Article•DOI•

Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China.

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Haoyuan Hong¹, Paraskevas Tsangaratos², Ioanna Ilia², Junzhi Liu¹, Junzhi Liu³, A-Xing Zhu¹, Wei Chen⁴ - Show less +3 more•Institutions (4)

Nanjing Normal University¹, National Technical University of Athens², Nanjing University³, Xi'an University of Science and Technology⁴

01 Jun 2018-Science of The Total Environment

TL;DR: A novel approach to construct a flood susceptibility map in the Poyang County, JiangXi Province, China is proposed by implementing fuzzy weight of evidence (fuzzy-WofE) and data mining methods and the fuzzy WofE-SVM model was the model with the highest predictive performance.

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262 citations

Journal Article•DOI•

Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques

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Hamid Darabi¹, Bahram Choubin², Omid Rahmati³, Ali Torabi Haghighi¹, Biswajeet Pradhan⁴, Biswajeet Pradhan⁵, Bjørn Kløve¹ - Show less +3 more•Institutions (5)

University of Oulu¹, University of Tehran², Islamic Azad University³, University of Technology, Sydney⁴, Sejong University⁵

01 Feb 2019-Journal of Hydrology

TL;DR: In this article, a flood risk map was produced with limited hydrological and hydraulic data using two state-of-the-art machine learning models: Genetic Algorithm Rule-Set Production (GARP) and Quick Unbiased Efficient Statistical Tree (QUEST).

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234 citations

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References

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Open Access

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Book•

The analytic hierarchy process : planning, priority setting, resource allocation

[...]

Thomas L. Saaty

01 Jan 1980

6,172 citations

Journal Article•DOI•

GIS‐based multicriteria decision analysis: a survey of the literature

[...]

Jacek Malczewski¹•Institutions (1)

University of Western Ontario¹

01 Aug 2006-International Journal of Geographical Information Science

TL;DR: The GIS‐based multicriteria decision analysis (GIS‐MCDA) approaches are surveyed using a literature review and classification of articles from 1990 to 2004 and taxonomy of those articles is provided.

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Abstract: The integration of GIS and multicriteria decision analysis has attracted significant interest over the last 15 years or so This paper surveys the GIS‐based multicriteria decision analysis (GIS‐MCDA) approaches using a literature review and classification of articles from 1990 to 2004 An electronic search indicated that over 300 articles appeared in refereed journals The paper provides taxonomy of those articles and identifies trends and developments in GIS‐MCDA

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1,694 citations

"Flood hazard zoning in Yasooj regio..." refers methods in this paper

...Multi-criteria decision analysis (MCDA) has been recognized as an important tool for analyzing complex decision problems, which often involve incommensurable data or criteria (Hwang & Lin 1987; Malczewski 2006)....
[...]
...Coupled MCDA-GIS approaches have been employed in spatial modelling and natural hazards analysis (Malczewski 2006; Scheuer et al. 2011; Paquette & Lowry 2012; Sol ın 2012)....
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Journal Article•DOI•

Review article "Assessment of economic flood damage"

[...]

Bruno Merz, Heidi Kreibich, Reimund Schwarze¹, Annegret H. Thieken•Institutions (1)

Helmholtz Centre for Environmental Research - UFZ¹

18 Aug 2010-Natural Hazards and Earth System Sciences

TL;DR: A review of the state-of-the-art and research directions of economic flood damage assessment can be found in this paper, where the authors identify research directions for economic flood risk assessment.

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Abstract: . Damage assessments of natural hazards supply crucial information to decision support and policy development in the fields of natural hazard management and adaptation planning to climate change. Specifically, the estimation of economic flood damage is gaining greater importance as flood risk management is becoming the dominant approach of flood control policies throughout Europe. This paper reviews the state-of-the-art and identifies research directions of economic flood damage assessment. Despite the fact that considerable research effort has been spent and progress has been made on damage data collection, data analysis and model development in recent years, there still seems to be a mismatch between the relevance of damage assessments and the quality of the available models and datasets. Often, simple approaches are used, mainly due to limitations in available data and knowledge on damage mechanisms. The results of damage assessments depend on many assumptions, e.g. the selection of spatial and temporal boundaries, and there are many pitfalls in economic evaluation, e.g. the choice between replacement costs or depreciated values. Much larger efforts are required for empirical and synthetic data collection and for providing consistent, reliable data to scientists and practitioners. A major shortcoming of damage modelling is that model validation is scarcely performed. Uncertainty analyses and thorough scrutiny of model inputs and assumptions should be mandatory for each damage model development and application, respectively. In our view, flood risk assessments are often not well balanced. Much more attention is given to the hazard assessment part, whereas damage assessment is treated as some kind of appendix within the risk analysis. Advances in flood damage assessment could trigger subsequent methodological improvements in other natural hazard areas with comparable time-space properties.

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984 citations

"Flood hazard zoning in Yasooj regio..." refers background in this paper

...A flood is an overflow of water that submerges land, and may cause damage to agricultural lands, urban areas, and may even result in loss of lives (Huang et al. 2008; Veerbeek & Zevenbergen 2009; Merz et al. 2010; Markantonis et al. 2013; Hudson et al. 2014; Perera et al. 2015; Yang et al. 2015)....
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Journal Article•DOI•

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree

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Dieu Tien Bui¹, Dieu Tien Bui², Tran Anh Tuan³, Harald Klempe², Biswajeet Pradhan⁴, Inge Revhaug⁵ - Show less +2 more•Institutions (5)

Hanoi University of Mining and Geology¹, Telemark University College², Vietnam Academy of Science and Technology³, Universiti Putra Malaysia⁴, Norwegian University of Life Sciences⁵

01 Apr 2016-Landslides

TL;DR: This study introduces a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods and demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptible mapping.

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Abstract: Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping.

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861 citations

"Flood hazard zoning in Yasooj regio..." refers background in this paper

...Different studies have demonstrated that these techniques can be used for generating hazard maps (Emmanouloudis et al. 2008; Sinha et al. 2008; Lim & Lee 2009; Akgun & Turk 2010; Kritikos & Davies 2011; Bui et al. 2015)....
[...]

Journal Article•DOI•

Parameterisation, calibration and validation of distributed hydrological models

[...]

Jens Christian Refsgaard¹•Institutions (1)

DHI Water & Environment¹

01 Nov 1997-Journal of Hydrology

TL;DR: In this article, a case study based on the MIKE SHE code and the 440 km 2 Karup catchment in Denmark is presented, where the importance of a rigorous and purposeful parameterisation is emphasized in order to get as few free parameters as possible for which assessments through calibration are required.

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800 citations