Frequency Ratio Method Research Articles

RETRACTED: Landslide susceptibility mapping at central Zab basin, Iran: A comparison between analytical hierarchy process, frequency ratio and logistic regression models

Abstract The purpose of this study is to compare the landslide susceptibility mapping models of logistic regression (LR), analytical hierarchy process (AHP) and frequency ratio (FR) applied in the central Zab basin (West Azerbaijan—Iran). Eight factors were used for landslide susceptibility mapping including slope, aspect, land cover, precipitation, lithology and the distance to roads, drainage, and faults that affect the occurrence of landslides. To get more precision, speed and facility in our analysis, all descriptive and spatial information was entered into GIS system. Satellite images (Landsat ETM + and SPOT 5) are also used to prepare for land use and landslide-inventory mapping respectively. Landslide events as used as dependant variable and data layers as independent variable, making use of the correlation between these two variables in landslide susceptibility. The three models are validated using the relative landslide density index (R-index) and the receiver operating characteristic (ROC) curves. The predictive capability of each model was determined from the area under the relative operating characteristic curve and the areas under the curves obtained using the LR, AHP, and FR methods are 0.8941, 0.8115, and 0.8634, respectively. These results indicate that the LR and FR models are relatively good estimators of landslide susceptibility in the study area. The interpretations of the susceptibility map reveal that precipitation, lithology and slope played major roles in landslide occurrence and distribution in the central Zab basin. In general, all three models were reasonably accurate. The resultant maps would be useful for regional spatial planning as well as for land cover planning.

Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS

Flood is one of the natural hazards which occur all over the world and it is critical to be controlled through proper management. Severe flood events in Kelantan, Malaysia cause damage to both life and property every year, and therefore the development of flood model to recognize the susceptible areas in watersheds is important for decision makers. Remote sensing (RS) and geographic information system (GIS) techniques could be useful in hydrological studies while they are able to fulfill all the requirements for comprehensive, rapid and accurate analysis. The aim of the current research is to compare the prediction performances of two different approaches such as rule-based decision tree (DT) and combination of frequency ratio (FR) and logistic regression (LR) statistical methods for flood susceptibility mapping at Kelantan, Malaysia. DT is based on the rules which are created precisely and strongly by considering all the characteristics of the variables which can enhance the performance of the flood susceptibility mapping. On the other hand, LR as multivariate statistical analysis (MSA) has some weak points. For that reason, FR was used to analyze the impact of classes of each variable on flood occurrence and overcome the weakness of LR. At first, flood inventory map with a total of 155 flood locations was extracted from various sources over the part of the Kelantan. Then the flood inventory data was randomly divided into a testing dataset 70% (115 flood locations) for training the models and the remaining 30% (40 flood locations) was used for validation purpose. The spatial database includes digital elevation model (DEM), curvature, geology, river, stream power index (SPI), rainfall, land use/cover (LULC), soil type, topographic wetness index (TWI) and slope. For validation both success and prediction rate curves were performed. The validation results showed that, area under the curve for the results of DT and integrated method of FR and LR was 87% and 90% for success rate and 82% and 83% for prediction rate respectively.

Assessment of the susceptibility degree to landslides within Ciulucurilor Hills by Method of Frequency Rate

Landslide susceptibility map for Ciulucurilor Hills was carried out using frequency ratio method. Its calculation is performed by the ratio of the percentage of landslide areas for each factor taken into account (lithology, elevation, drainage depth, slope, exposition, land use, distance from river and drainage network density) and the percentage of the total area corresponding to each factor. Very low susceptibility land occupies 10.1% of the region surface and is the most common in the south-west and the Big Ciuluc and the Middle Ciuluc river valleys. There is 25.8% of the built surface on this area. Low susceptibility land occupies 40.3% of the territory, being the largest class of susceptibility. It holds 56.2% of the arable land and 47.6% of the built surface. The average susceptibility is recorded on 22.9% of the territory and has a more compact distribution in Soloneţ basin. This land holds 38.8% of the pasture surface and 28.5% of the perennial plantations. The land with high and very high susceptibility occupies 26.7% of the territory, with a high frequency in the south-west of the region, on interfluve between the Big Ciuluc and the Middle Ciuluc rivers. These classes have 81.9% of forest surface and only 12.6% of the arable land. The length of the roads located on the land with high and very high susceptibility is 164.5 km

Hazard mapping of Sindhu Khola watershed, Sindupalchowk, Nepal

The aim of present study is to produce landslide susceptibility map of Sindhu Khola watershed, Sindhupalchok, Kavre, Nepal, using Geographic Information System (GIS) by means of frequency ratio. The ArcView was used as primary software for the management of spatial data. Landslide locations were identified during the field visit. There are altogether seven different factors such as topographic slope, aspect, distance from drainage and so on, and their respective relations with the occurrence of landslides in the study area were analyzed. These factors were used to produce landslide susceptibility map using frequency ratio method. The frequency ratio suggests that distance near to drainage and slope angle higher than 30° are main factor contributing landslide occurrences. To assess the performance of the produced susceptibility map, the area under curve (AUC) is drawn. The AUC value of the produced landslide susceptibility map has been obtained as 79.65%. According to the results of the AUC evaluation, the produced map has exhibited a satisfactory performance.

Spatio-temporal assessment of soil erosion at Kuala Lumpur metropolitan city using remote sensing data and GIS

The main purpose of this paper is to assess and predict soil erosion and its dynamism observation at Kuala Lumpur metropolitan city using universal soil loss equation (USLE). To acquire USLE factors, multi-source datasets such as rainfall data of the past 20 years, DEM map, SPOT5 image and geological map of the study area were used to calculate spatial soil erosion of Kuala Lumpur metropolitan city. Results indicated that the highest spatial coverage (61.9%) occurred in very low and the lowest spatial coverage (3.8%) in severe soil loss class. Furthermore, a landslide inventory map of the same area was overlaid on the resultant soil erosion layer to demonstrate their correlation. A frequency ratio (FR) method was applied to determine the relationship between occurred landslides and each soil erosion class. The results indicated that the highest and the lowest FR equal to 0.8 and 2.1 and observed in very low and severe soil loss classes, respectively. Temporal assessment of soil erosion indicated that there is a decrement of soil loss content in severe class from 2000 to 2010. The result of the prognosis analysis showed that the spatial coverage of the study area in severe soil loss class equals to 48%. This result proved the significance of land use/land cover to curb soil erosion. The resultant maps may help the planners and managers to take proper decision for the conservation of soil as a noteworthy natural resource.

Statistical assessment of vegetation dynamics within protected areas using remote sensing data

This study aimed to test the effectiveness of protected areas to preserve vegetation. The first step was to identify vegetation suitable areas, designed as areas with optimal morphological terrain features for a good photosynthetic activity. These areas were defined according to the following landscape factors: slope, altitude, aspect and land use. Enhanced vegetation index (EVI) was chosen as vegetation dynamics indicator. This method is based on a statistical approach using remote sensing data in a geographic information system (GIS) environment. The correlation between EVI and landscape factor was evaluated using the frequency ratio method. Classes of landscape factors that show good correlation with a high EVI were combined to obtain vegetation suitable areas. Once identified, these areas and their vegetation dynamics were analysed by comparing the results obtained whenever these areas are included or not included in protected areas. A second EVI dataset was used to verify the accuracy in identifying vegetation suitable areas and the influence of each landscape factor considered in their identification. This validation process showed that vegetation suitable areas are significant in identifying areas with good photosynthetic activity. The effects analysis showed a positive influence of all landscape factors in determining suitability. This methodology, applied to central regions of Italy, shows that the vegetation suitable areas located inside protected areas are <em>greener</em> than those outside protected areas. This suggests that the protective measures established by the institution of the parks have proved to be effective, at least as far as the status of vegetation development is concerned.

Statistical assessment of vegetation dynamics within protected areas using remote sensing data

This study aimed to test the effectiveness of protected areas to preserve vegetation. The first step was to identify vegetation suitable areas, designed as areas with optimal morphological terrain features for a good photosynthetic activity. These areas were defined according to the following landscape factors: slope, altitude, aspect and land use. Enhanced vegetation index (EVI) was chosen as vegetation dynamics indicator. This method is based on a statistical approach using remote sensing data in a geographic information system (GIS) environment. The correlation between EVI and landscape factor was evaluated using the frequency ratio method. Classes of landscape factors that show good correlation with a high EVI were combined to obtain vegetation suitable areas. Once identified, these areas and their vegetation dynamics were analysed by comparing the results obtained whenever these areas are included or not included in protected areas. A second EVI dataset was used to verify the accuracy in identifying vegetation suitable areas and the influence of each landscape factor considered in their identification. This validation process showed that vegetation suitable areas are significant in identifying areas with good photosynthetic activity. The effects analysis showed a positive influence of all landscape factors in determining suitability. This methodology, applied to central regions of Italy, shows that the vegetation suitable areas located inside protected areas are greener than those outside protected areas. This suggests that the protective measures established by the institution of the parks have proved to be effective, at least as far as the status of vegetation development is concerned.

A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey

Abstract This study evaluated and compared landslide susceptibility maps produced with three different methods, frequency ratio, weights of evidence, and logistic regression, by using validation datasets. The field surveys performed as part of this investigation mapped the locations of 90 landslides that had been identified in the Sultan Mountains of south-western Turkey. The landslide influence parameters used for this study are geology, relative permeability, land use/land cover, precipitation, elevation, slope, aspect, total curvature, plan curvature, profile curvature, wetness index, stream power index, sediment transportation capacity index, distance to drainage, distance to fault, drainage density, fault density, and spring density maps. The relationships between landslide distributions and these parameters were analysed using the three methods, and the results of these methods were then used to calculate the landslide susceptibility of the entire study area. The accuracy of the final landslide susceptibility maps was evaluated based on the landslides observed during the fieldwork, and the accuracy of the models was evaluated by calculating each model’s relative operating characteristic curve. The predictive capability of each model was determined from the area under the relative operating characteristic curve and the areas under the curves obtained using the frequency ratio, logistic regression, and weights of evidence methods are 0.976, 0.952, and 0.937, respectively. These results indicate that the frequency ratio and weights of evidence models are relatively good estimators of landslide susceptibility in the study area. Specifically, the results of the correlation analysis show a high correlation between the frequency ratio and weights of evidence results, and the frequency ratio and logistic regression methods exhibit correlation coefficients of 0.771 and 0.727, respectively. The frequency ratio model is simple, and its input, calculation and output processes are easily understood. The interpretations of the susceptibility map reveal that geology, slope steepness, slope aspect, and elevation played major roles in landslide occurrence and distribution in the Sultan Mountains. The landslide susceptibility maps produced from this study could therefore assist planners and engineers during development and land-use planning.

Evaluation of the consistency of landslide susceptibility mapping: a case study from the Kankai watershed in east Nepal

GIS-based landslide susceptibility maps for the Kankai watershed in east Nepal are developed using the frequency ratio method and the multiple linear regression technique. The maps are derived from comparing observed landslides with possible causative factors: slope angle, slope aspect, slope curvature, relative relief, distance from drainage, land use, geology, distance from faults and mean annual rainfall. The consistency of the maps is evaluated using landslide density analysis, success rate analysis and spatially agreed area approach. The first two analyses produce almost identical quantitative results, whereas the last approach is able to reveal spatial differences between the maps and also to improve predictions in the agreed high landslide-susceptible area.

An orbital floating time scale of the Hauterivian/Barremian GSSP from a magnetic susceptibility signal (Río Argos, Spain)

An orbital floating time scale of the Hauterivian–Barremian transition (Early Cretaceous) is proposed using high-resolution magnetic susceptibility measurements. Orbital tuning was performed on the Río Argos section (southeast Spain), the candidate for a Global boundary Stratotype Section and Point (GSSP) for the Hauterivian–Barremian transition. Spectral analyses of MS variations, coupled with the frequency ratio method, allow the recognition of precession, obliquity and eccentricity frequency bands. Orbitally-tuned magnetic susceptibility provides minimum durations for ammonite biozones. The durations of well-constrained ammonite zones are assessed at 0.78myr for Pseudothurmannia ohmi (Late Hauterivian) and 0.57myr for Taveraidiscus hugii (Early Barremian). These results are consistent with previous estimates from the other reference section (Angles, southeast France) and tend to show that the Río Argos section displays a complete succession for this time interval. They differ significantly from those proposed in the Geologic Time Scale 2008 and may help to improve the next compilation. The Faraoni Oceanic Anoxic Event, a key Early Cretaceous oceanographic perturbation occurring at the P. ohmi/P. catulloi subzone boundary has a duration estimated at 0.10–0.15myr, which is similar to previous assessments.

Landslide susceptibility mapping based on frequency ratio and logistic regression models

The aim of this study is to apply and compare a probability model, frequency ratio and statistical model, and a logistic regression to Sajaroud area, Northern Iran using geographic information system. Landslide locations of the study area were detected from interpretation of aerial photographs and field surveys. Landslide-related factors such as elevation, slope gradient, slope aspect, slope curvature, rainfall, distance to fault, distance to drainage, distance to road, land use, and geology were calculated from the topographic and geology map and LANDSAT ETM satellite imagery. The spatial relationships between the landslide location and each landslide-related factor were analyzed and then landslide susceptibility maps were produced using the frequency ratio and forward stepwise logistic regression methods. Finally, the maps were tested and compared using known landslide locations, and success rates were calculated. Predicted accuracy values for frequency ratio (79.48%) and logistic regression models showed that the map obtained from frequency ratio model is more accurate than the logistic regression (77.4%) model. The models used in this study have shown a great deal of importance for watershed management and land use planning.

Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio and analytical hierarchy methods in Rize province (NE Turkey)

The northeast part of Turkey is prone to landslides because of the climatic conditions, as well as geologic and geomorphologic characteristics of the region. Especially, frequent landslides in the Rize province often result in significant damage to people and property. Therefore, in order to mitigate the damage from landslides and help the planners in selecting suitable locations for implementing development projects, especially in large areas, it is necessary to scientifically assess susceptible areas. In this study, the frequency ratio method and the analytical hierarchy process (AHP) were used to produce susceptibility maps. Especially, AHP gives best results because of allowing better structuring of various components, including both objective and subjective aspects and comparing them by a logical and thorough method, which involves a matrix-based pairwise comparison of the contribution of different factors for landslide. For this purpose, lithology, slope angle, slope aspect, land cover, distance to stream, drainage density, and distance to road were considered as landslide causal factors for the study area. The processing of multi-geodata sets was carried out in a raster GIS environment. Lithology was derived from the geological database and additional field studies; slope angle, slope aspect, distance to stream, distance to road and drainage density were invented from digital elevation models; land cover was produced from remote sensing imagery. In the end of study, the results of the analysis were verified using actual landslide location data. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.

MASS ESTIMATION OF IMPACTING OBJECTS AGAINST A STRUCTURE USING AN ARTIFICIAL NEURAL NETWORK WITHOUT CONSIDERATION OF BACKGROUND NOISE

It is critically important to identify unexpected loose parts in a nuclear reactor pressure vessel, since they may collide with and cause damage to internal structures. Mass estimation can provide key information regarding the kind as well as the location of loose parts. This study proposes a mass estimation method based on an artificial neural network (ANN), which can overcome several unresolved issues involved in other conventional methods. In the ANN model, input parameters are the discrete cosine transform (DCT) coefficients of the auto-power spectrum density (APSD) of the measured impact acceleration signal. The performance of the proposed method is then evaluated through application to a large-sized plate and a 1/8-scaled mockup of a reactor pressure vessel. The results are compared with those obtained using a conventional method, the frequency ratio (FR) method. It is shown that the proposed method is capable of estimating the impact mass with 30% lower relative error than the FR method, thus improving the estimation performance.

Improvement of stress testing performance using Barkhausen noise sensor

This paper focuses on the occurrence of magnetic barkhausen noise (MBN) when the ferromagnetic material is under an alternating magnetic field in a localized zone. It encompasses the formation of a magnetizer with alternating magnetization function of low frequency and the characteristics of concentrated line of magnetic induction. Using molecular intercalation technology to control the distribution mode of the metal ions and anions, located inside and between the layered double hydroxides (LDHs) laminate respectively, the highly ordered NiZnFe 2O 4 spinel type ferrite which receives the weak MBN signal is synthesized. With the aid of hardware circuit, the interfering noise of the MBN signal is eliminated through research involving low noise amplication, band-pass filtering and frequency-selective amplication. The highly selective, instant, fixed point, high-speed and portable MBN stress testing sensor has also been successfully researched. Additionally, the feasibility of applying the magnetizing frequency ratio method and the high-intensity magnetizing field method to engineering stress testing and heat treatment state is also studied.

Production of landslide susceptibility map of Samsun (Turkey) City Center by using frequency ratio method

Due to the increased need for housing, Samsun province center had been quickly opened for construction in the last 25 years; because of the rapid population growth, construction facilities have progressed towards the southern hillside of the city. Large amount of landslides had been observed and classified in this region by the relevant institutions. To prevent life and property losses, it is required to evaluate past landslides and thus, to determine the probable landslide areas in the construction regions. Therefore, this study aims to create a landslide susceptibility map of Samsun province center. Frequency ratio method which is widely used in the literature has been used to create the landslide susceptibility map. Some characteristic properties of the region such as the geological formation, altitude, slope, aspect, curvature, plan and profile curvature, distance from roads and streams have been considered for the landslide susceptibility evaluation. Key words: GIS, Landslide, susceptibility map, frequency ratio method.

Frequency ratio analysis of mass movements in the Xiangxi catchment, Three Gorges Reservoir area, China

In 2003, the Three Gorges Project (TGP, China), currently the world's largest hydro- electric power plant by total capacity, went into operation. Due to large-scale impoundment of the Yangtze River and its tributaries and also due to resettlement, extensive environmental impacts like land use change and increase of geohazards are associated with the TGP. Within the Yangtze Project, we investigate these effects for the Xiangxi (香溪) catchment which is part of the Three Gorges Reser- voir. The aim of this study is to evaluate the susceptibility for mass movement within the Xiangxi River backwater area using geographic information system (GIS). We used existing mass movements and the conditioning factors (geology, elevation, slope, curvature, land use, and land use change) for analyzing mass movement susceptibility. Mass movements and geology were mapped in the field to establish a mass movement inventory and a geological map. Land use and digital elevation model (DEM) were ob- tained from remote-sensing data. We determined the relation between mass movements and the condi- tioning factors by using the frequency ratio method and found strong relation between mass move-

Prediction and comparison of urban growth by land suitability index mapping using GIS and RS in South Korea

This study compares land suitability index (LSI) maps created using a geographic information system (GIS) with frequency ratio (FR), analytical hierarchy process (AHP), logistic regression (LR), and artificial neural network (ANN) approaches to forecasting urban land-use changes. Various social, political, topographic, and geographic factors were used as predictors of land-use change, including elevation, slope, aspect, distance from roads and urban areas, road ratio, land use, environmental score, and legal restrictions. Then, LSI maps were created using FR, AHP, LR, and ANN approaches, and significance and correlation were examined among the models using relative operating characteristic (ROC), overall accuracy, and kappa analyses. The ROC analyses gave results of 0.940, 0.937, 0.922, and 0.891 for the LR, FR, AHP, and ANN LSI maps, respectively. The highest correlation was found between the LR and AHP LSI maps (0.816911), and the lowest correlation was between the ANN and FR LSI maps (0.759701). The ANN approach produced the highest overall accuracy at 92.3%, followed by 91.74% for FR, 89.12% for AHP, and 88.93% for LR. In the kappa analysis, the highest K ˆ statistic was 45.38% for FR, followed by 40.84% for ANN, 30 representing the city area, the ANN method had a relatively high value of 71.71%, and the FR, LR, and AHP methods had similar accuracies of 57.68, 55.05, and 54.31%, respectively. These results indicate that the FR, AHP, LR, and ANN approaches produced similar LSI maps for Korea.

The planetary system host HR 8799: on its λ Bootis nature

HR\,8799 is a $\lambda$ Bootis, $\gamma$ Doradus star hosting a planetary system and a debris disk with two rings. This makes this system a very interesting target for asteroseismic studies. This work is devoted to the determination of the internal metallicity of this star, linked with its $\lambda$ Bootis nature (i.e., solar surface abundances of light elements, and subsolar surface abundances of heavy elements), taking advantage of its $\gamma$ Doradus pulsations. This is the most accurate way to obtain this information, and this is the first time such a study is performed for a planetary-system-host star. We have used the equilibrium code CESAM and the non-adiabatic pulsational code GraCo. We have applied the Frequency Ratio Method (FRM) and the Time Dependent Convection theory (TDC) to estimate the mode identification, the Brunt-Va\"is\"al\"a frequency integral and the mode instability, making the selection of the possible models. When the non-seismological constraints (i.e its position in the HR diagram) are used, the solar abundance models are discarded. This result contradicts one of the main hypothesis for explaining the $\lambda$ Bootis nature, namely the accretion/diffusion of gas by a star with solar abundance. Therefore, according to these results, a revision of this hypothesis is needed. The inclusion of accurate internal chemical mixing processes seems to be necessary to explain the peculiar abundances observed in the surface of stars with internal subsolar metallicities. The use of the asteroseismological constraints, like those provided by the FRM or the instability analysis, provides a very accurate determination of the physical characteristics of HR 8799. However, a dependence of the results on the inclination angle $i$ still remains. The determination of this angle, more accurate multicolour photometric observations, and high resolution spectroscopy can definitively fix the mass and metallicity of this star.

Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling

Data collection for landslide susceptibility modeling is often an inhibitive activity. This is one reason why for quite some time landslides have been described and modelled on the basis of spatially distributed values of landslide-related attributes. This paper presents landslide susceptibility analysis in the Klang Valley area, Malaysia, using back-propagation artificial neural network model. A landslide inventory map with a total of 398 landslide locations was constructed using the data from various sources. Out of 398 landslide locations, 318 (80%) of the data taken before the year 2004 was used for training the neural network model and the remaining 80 (20%) locations (post-2004 events) were used for the accuracy assessment purpose. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Eleven landslide occurrence related factors were selected as: slope angle, slope aspect, curvature, altitude, distance to roads, distance to rivers, lithology, distance to faults, soil type, landcover and the normalized difference vegetation index value. For calculating the weight of the relative importance of each factor to the landslide occurrence, an artificial neural network method was developed. Each thematic layer's weight was determined by the back-propagation training method and landslide susceptibility indices (LSI) were calculated using the trained back-propagation weights. To assess the factor effects, the weights were calculated three times, using all 11 factors in the first case, then recalculating after removal of those 4 factors that had the smallest weights, and thirdly after removal of the remaining 3 least influential factors. The effect of weights in landslide susceptibility was verified using the landslide location data. It is revealed that all factors have relatively positive effects on the landslide susceptibility maps in the study. The validation results showed sufficient agreement between the computed susceptibility maps and the existing data on landslide areas. The distribution of landslide susceptibility zones derived from ANN shows similar trends as those obtained by applying in GIS-based susceptibility procedures by the same authors (using the frequency ratio and logistic regression method) and indicates that ANN results are better than the earlier method. Among the three cases, the best accuracy (94%) was obtained in the case of the 7 factors weight, whereas 11 factors based weight showed the worst accuracy (91%).

Application of frequency ratio and logistic regression to active rock glacier occurrence in the Andes of San Juan, Argentina

This study employs statistical modeling techniques and geomorphological mapping to analyze the distribution of active rock glaciers in relation to altitude, aspect, slope, lithology and solar radiation using optical remote sensing techniques with GIS. The study area includes a portion of the Dry Andes of the Cordillera Frontal of San Juan around 30°S latitude, where few geomorphological studies have been conducted. Over 155 rock glaciers have been identified, and 85 are considered active. The relationship between the variables and the rock glaciers distribution was analyzed using the frequency ratio method and logistic regression models. The analytical results show that elevations > 3824 m a.s.l., a south-facing or east-facing aspect, areas with relatively low solar radiation, and slope between 2° and 20° favor the existence of the rock glaciers, and demonstrate that lithology and slope exert major influences.