A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran

Arabameri, Alireza; Rezaei, Khalil; Cerdà, Artemi; Conoscenti, Christian; Kalantari, Zahra

doi:10.1016/j.scitotenv.2019.01.021

Cited by 235 publications

(139 citation statements)

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“…It enables better land use planning and the management of its impacts on freshwater supplies, treatment and distribution; wastewater collection, treatment, reuse and disposal; stormwater collection, use and disposal; and solid waste collection, recycling and disposal systems. Accordingly, it makes urban development part of integrated basin management, which is oriented toward a more economically, socially and environmentally sustainable mixed urban-rural landscape (Loucks & Van Beek 2017;Kalantari et al 2018;Arabameri et al 2019). IUWM also aims to help cities progress towards a circular economy, thus closing the loop of water resource circulation, and helps to limit the discharge of liquid waste and the constantly growing need for additional water resources (High level Panel on Water 2018).…”

Section: Introductionmentioning

confidence: 99%

A review of nature-based solutions for urban water management in European circular cities: a critical assessment based on case studies and literature

et al. 2020

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Nature-based solutions (NBS) can protect, manage and restore natural or modified ecosystems. They are a multidisciplinary, integrated approach to address societal challenges and some natural hazards effectively and adaptively, simultaneously providing human well-being and biodiversity benefits. NBS applications can be easily noticed in circular cities, establishing an urban system that is regenerative and accessible. This paper aims to offer a review on NBS for urban water management from the literature and some relevant projects running within the COST Action ‘Implementing nature-based solutions for creating a resourceful circular city’. The method used in the study is based on a detailed tracking of specific keywords in the literature using Google Scholar, ResearchGate, Academia.edu, ScienceDirect and Scopus. Based on this review, three main applications were identified: (i) flood and drought protection; (ii) the water-food-energy nexus; and (iii) water purification. The paper shows that NBS provide additional benefits, such as improving water quality, increasing biodiversity, obtaining social co-benefits, improving urban microclimate, and the reduction of energy consumption by improving indoor climate. The paper concludes that a systemic change to NBS should be given a higher priority and be preferred over conventional water infrastructure.

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Section: Introductionmentioning

confidence: 99%

A review of nature-based solutions for urban water management in European circular cities: a critical assessment based on case studies and literature

et al. 2020

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“…Similarly, Arabameri at al. [28] found that EBF model had the highest prediction power over FR, TOPSIS, and VIKOR models. In contrast, Khosravi et al [16], who applied FR and WOE models to a similar case study with similar input data, found that the FR and WOE models had slightly better prediction powers than the EBF model and its ensembles.…”

Section: Discussionmentioning

confidence: 92%

“…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.…”

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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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“…In GESM, testing for collinearity among the effective factors in gullying is very important, because the collinearity reduces the accuracy of the GESM [86][87][88][89]. The variance inflation factor (VIF) and Tolerance (TOL) are very commonly used indicators for checking multicollinearity among parameters [90,91]. TOL values less than 0.1 or 0.2 and VIF values greater than 5 or 10 indicate collinearity between the parameters [17,19,86,89,92].…”

Section: Multicollinearity Assessmentmentioning

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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A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran

Cited by 235 publications

References 95 publications

A review of nature-based solutions for urban water management in European circular cities: a critical assessment based on case studies and literature

A review of nature-based solutions for urban water management in European circular cities: a critical assessment based on case studies and literature

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

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

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