A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran.

American Society for Testing and Materials

An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines

Environmental hazards: Assessing risk and reducing disaster

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.

A novel hybrid artificial intelligence approach for flood susceptibility assessment

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

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

Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the study area.

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Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models

Nanoparticles are one of the newest materials that can be used in soil stabilization. The effect of Na + modified montmorillonite nanoclay on clay engineering properties has been examined in this study. At first, it is used for dispersing nanoparticles into soil and preparing samples. Used nanoclay content by dry weight percentage of soil is 0.5, 1, 1.5 and 2%. Variations caused by increasing nanoclay content were studied. Soil compaction test was performed in order to achieve maximum specific weight and optimum moisture content. Tests were conducted using nanoclay and then without it. The obtained results show that liquid and plastic limits rise with increasing nanoclay content. In the uniaxial and CBR tests, the highest soil resistivity can be seen by adding 1.5% of nanoclay. Low percentage of nanoclay generally improves the soil properties. DOI: http://dx.doi.org/10.3126/ijls.v9i4.12670

The Effect of Nanoparticles on Geotechnical Properties of Clay

This research tried to study hydraulic performance of morning glory spillway by numerical modeling. The Flow 3D software was used for numerical modeling and the morning glory spillway was studied based on USBR standard. Using a numerical model, the hydraulic parameters of the flow such as coefficient of discharge, water level profile and pressure distribution were determined and compared with the data obtained from USACE and USBR models. Although comparing the parameters shows a good and show agreement between the results, however, using the numerical model in real samples has limitations such as the number and size of the network, computational time and accurate modeling of the weather, which require more research in this area. DOI: http://dx.doi.org/10.3126/ijls.v9i4.12671

Retracted: Numerical Simulation of the Flow Pattern on Morning Glory Spillways

Morning glory spillways with drop inlets are normally employed in dams built on narrow valleys or placed on steep slopes. In Iran, morning glory spillways have been commonly used in large Dam projects such as Sefidrood dam, Alborz dam, and Haraz dam. Physical models should be built to accurately determine hydraulic parameters of the flow and flow field in spillways. Establishment of a physical model involves extravagant costs and conditions that cannot be justified in some cases. Therefore, suitable numerical models can be proposed for such circumstances. Using FLOW3D numerical models, 3-dimensional numerical modelling of the flow was calibrated and validated by experimental information associated with morning glory spillway of Alborz dam and accuracy of numerical modelling was determined by relative error of numerical model. So it was attempted to determine flow pattern and control conditions of morning glory spillways in different modes using boundary conditions, inlet conditions and grid spacing of flow field and project rating curve of morning glory spillways. According to the results of numerical model, relative error of numerical modelling equals 6.4% for calculating discharge rate of the spillways. Numerical modelling error is 7.6% for determining depth parameter of the flow in spillway crest in comparison with experimental results.

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Ebrahim Nohani

Papers

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

Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models

The Effect of Nanoparticles on Geotechnical Properties of Clay

Retracted: Numerical Simulation of the Flow Pattern on Morning Glory Spillways

Numerical Modeling of Flow Field in Morning Glory Spillways and Determining Rating Curve at Different Flow Rates