GIS-based Frequency Ratio Research Articles

Flood Susceptibility Mapping Using GIS-Based Frequency Ratio and Shannon’s Entropy Index Bivariate Statistical Models: A Case Study of Chandrapur District, India

Flood Susceptibility Mapping Using GIS-Based Frequency Ratio and Shannon’s Entropy Index Bivariate Statistical Models: A Case Study of Chandrapur District, India

GIS-based frequency ratio model for flood susceptibility zonation in the state of Meghalaya, Northeast India

GIS-based frequency ratio model for flood susceptibility zonation in the state of Meghalaya, Northeast India

Assessing landslide susceptibility in Lake Abya catchment, Rift Valley, Ethiopia: A GIS-based frequency ratio analysis

Ethiopia's varied landscape, significant rainfall, and diverse geological characteristics pose risks of landslides. The specific research area spans 40 km2 within the Lake Abaya catchment area in the Rift Valley of Ethiopia. This investigation aimed to map landslide susceptibility using remote sensing information, GIS technology, and frequency ratio analysis. It evaluated multiple factors influencing landslide susceptibility. The process involved meticulous mapping of thematic layers, utilizing GIS techniques and diverse data sources, including primary data, satellite imagery, and secondary sources. A combination of Google Earth image analysis and field surveys was used to map landslide susceptibility in inaccessible areas. It was determined that 138 landslide sites existed. Of these, 30% (41 points) were assigned to the test of the model and another 30% to the training of the model, for a total of 97 points. The landslide susceptibility was classified into five categories based on frequency ratio analysis of the landslide susceptibility index (LSI): very low, low, moderate, high, and very high. The northeastern sector of the study area demonstrated a comparatively diminished susceptibility to landslides, ranging from low to moderate, whereas the central and southern regions showcased markedly elevated vulnerability. An evaluation of the model's accuracy using the area under the curve (AUC) method based on test inventory landslide data produced encouraging results: 84.8% accuracy on the success rate curve and 78.8% accuracy on the prediction rate curve. Based on the frequency ratio model, a susceptibility map is derived to represent susceptibility levels accurately.

Mapping Groundwater Prospective Areas Using Remote Sensing and GIS-Based Data Driven Frequency Ratio Techniques and Detecting Land Cover Changes in the Yellow River Basin, China

Groundwater is an essential resource that meets all of humanity’s daily water demands, supports industrial development, influences agricultural output, and maintains ecological equilibrium. Remote sensing data can predict the location of potential water resources. The current study was conducted in China’s Yellow River region, Ningxia Hui Autonomous Region (NHAR). Through the use of a GIS-based frequency ratio machine learning technique, nine layers of evidence influenced by remote sensing data were generated and integrated. The layers used are soil characteristics, aspect, and roughness index of the terrain, drainage density, elevation, lineament density, depressions, rainfall, and distance to the river from the location. Six groundwater prospective zones (GWPZs) were found to have very low (13%), low (30%), moderate (25%), high (16%), very high (11%), and extreme potentiality (5.26%) values. According to well data used to validate the GWPZs map, approximately 40% of the wells are consistent to very high to excellent zones. Information about groundwater productivity was gathered from 150 well locations. Using well data that had not been used for model training, the resulting GWPZs maps were validated using area-under-the-curve (AUC) analysis. FR models have an accuracy rating of 0.759. Landsat data were used to characterize the study area’s changes in land cover. The spatiotemporal differences in land cover are detected and quantified using multi-temporal images which revealed changes in water, agricultural, and anthropogenic activities. Overall, combining different data sets through a GIS can reveal the promising areas of water resources that aid planners and managers.

Using Remote Sensing and GIS-Based Frequency Ratio Technique for Revealing Groundwater Prospective Areas at Wadi Al Hamdh Watershed, Saudi Arabia

For socioeconomic development in arid regions, there is an increasing need for groundwater resources due to rapid population expansion. It is necessary to apply innovative approaches for managing the sustainability of groundwater resources. Thus, remote sensing, geologic, climatic, and hydrologic data are integrated through the GIS-based frequency ratio and overlay analysis for assessing the spatial distribution of groundwater potential zones (GWPZs) of Wadi Al Hamdh, Saudi Arabia. Twelve factors controlling groundwater’s existence and infiltration were identified, normalized using the frequency ratio technique and combined through GIS techniques. To accomplish this, 313 well locations in the study area were used for training (70%) and 137 well locations were utilized for validation (30%). Using receiver operating characteristic (ROC) curves and field data, the model predictions were validated and showed very good performance (AUC: 0.893). The five zones on the GWPZs map correspond to 2.24, 5.81, 13.39, 53.90, and 24.65% of the entire area. These are: excellent, good, moderate, low, and very low perspectivity. As a good example, the applied approaches provided very good results that are significant to planning and sustainable development as well as managing groundwater resources in arid regions.

Mapping Potential Water Resource Areas Using GIS-Based Frequency Ratio and Evidential Belief Function

Groundwater is a critical freshwater resource that is necessary for sustaining life. Thus, targeting prospective groundwater zones is crucial for the extraction, use, and management of water resources. In this study, we combined the remote sensing, GIS-based frequency ratio (FR), and evidential belief function (EBF) techniques into a model to delineate and quantify prospective groundwater zones. To accomplish this, we processed Shuttle Radar Topography Mission (SRTM), Landsat-8 Operational Land Imager (OLI), Sentinel-2, and rainfall data to reveal the geomorphic, hydrologic, and structural elements and climatic conditions of the study area, which is downstream of the Yellow River basin, China. We processed, quantified, and combined twelve factors (the elevation, slope, aspect, drainage density, lineament density, distance to rivers, NDVI, TWI, SPI, TRI, land use/cover, and rainfall intensity) that control the groundwater infiltration and occurrence using the GIS-based FR and EBF models to produce groundwater potential zones (GWPZs). We used the natural breaks classifier to categorize the groundwater likelihood at each location as very low, low, moderate, high, or very high. The FR model exhibited a better performance than the EBF model, as evidenced by the area under the curve (AUC) assessment of the groundwater potential predictions (FR AUCs of 0.707 and 0.734, and EBF AUCs of 0.665 and 0.690). Combining the FR and EBF models into the FR–EBF model increased the accuracy (AUC = 0.716 and 0.747), and it increased the areas of very high and moderate potentiality to 1.97% of the entire area, instead of the 0.39 and 0.78% of the FR and EBF models, respectively. The integration of remote sensing and GIS-data-driven techniques is crucial for the mapping of groundwater prospective zones.

GIS-Based Frequency Ratio and Analytic Hierarchy Process for Forest Fire Susceptibility Mapping in the Western Region of Syria

Forest fires are among the most major causes of global ecosystem degradation. The integration of spatial information from various sources using statistical analyses in the GIS environment is an original tool in managing the spread of forest fires, which is one of the most significant natural hazards in the western region of Syria. Moreover, the western region of Syria is characterized by a significant lack of data to assess forest fire susceptibility as one of the most significant consequences of the current war. This study aimed to conduct a performance comparison of frequency ratio (FR) and analytic hierarchy process (AHP) techniques in delineating the spatial distribution of forest fire susceptibility in the Al-Draikich region, located in the western region of Syria. An inventory map of historical forest fire events was produced by spatially digitizing 32 fire incidents during the summers of 2019, 2020, and 2021. The forest fire events were divided into a training dataset with 70% (22 events) and a test dataset with 30% (10 events). Subsequently, FR and AHP techniques were used to associate the training data set with the 13 driving factors: slope, aspect, curvature, elevation, Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Topographic Wetness Index (TWI), rainfall, temperature, wind speed, TWI, and distance to settlements, rivers and roads. The accuracy of the maps resulting from the modeling process was checked using the validation dataset and receiver operating characteristics (ROC) curves with the area under the curve (AUC). The FR method with AUC = 0.864 achieved the highest value compared to the AHP method with AUC = 0.838. The outcomes of this assessment provide constructive spatial insights for adopting forest management strategies in the study area, especially in light of the consequences of the current war.

GIS-based frequency ratio and Shannon's entropy techniques for flood vulnerability assessment in Patna district, Central Bihar, India

GIS-based frequency ratio and Shannon's entropy techniques for flood vulnerability assessment in Patna district, Central Bihar, India

GIS-Based Susceptibility Mapping of the Fentale–Metehara Blisters and Blister Caves in the Main Ethiopian Rift: Implications for Their Conservation

The biggest and most spectacular blister caves in the world are found in the Fentale–Metehara area, in the Main Ethiopian Rift, where we inventoried 639 blisters and blister caves in an area of 80 sq. km straddling between the southern foot of Fentale volcano and the northern shore of Lake Beseka. The blisters were formed by a unique geological process involving up-doming of fluid-rich pyroclastic flows due to pressure of gases entrapped in the progressing flow, and subsequent breaching of some of the hollow domes. These unique geological heritages have been threatened, in some cases already destroyed, by various man-made and natural factors such as the unprecedented expansion of Lake Beseka, extensive infrastructure development (roads, railways, settlements and urbanization, and large-scale irrigation schemes), and younger fissural lava flows. In order to systematically determine the degree of threat quantitatively and qualitatively and evaluate the probability of destruction of any of the blisters and blister caves, we applied GIS-based frequency ratio model and non-hierarchical cluster analysis techniques and produced a susceptibility map of the area. The susceptibility map, validated using the degree of fitness technique, indicates that most of the blister field falls within very high to high susceptibility of destruction, implying that the destruction of most of these spectacular features is imminent particularly due to expansion of Lake Beseka and the Metehara town. This calls for concerted effort by the local authorities to devise mechanisms to protect the threatened blisters and blister caves along with mitigating the impacts of the expanding Lake Beseka. The susceptibility map will be helpful to guide and prioritize conservation efforts.

Landslide susceptibility mapping using statistical methods in Uatzau catchment area, northwestern Ethiopia

Uatzau basin in northwestern Ethiopia is one of the most landslide-prone regions, which characterized by frequent high landslide occurrences causing damages in farmlands, non-cultivated lands, properties, and loss of life. Preparing a Landslide susceptibility mapping is imperative to manage the landslide hazard and reduce damages of properties and loss of lives. GIS-based frequency ratio, information value, and certainty factor methods were applied. The landslide inventory map was prepared from detailed fieldwork and Google Earth imagery interpretation. Thus, 514 landslides were mapped, and out of which 359 (70%) of landslides were randomly selected keeping their spatial distribution to build landslide susceptibility models, while the remaining 155 (30%) of the landslides were used to model validation. In this study, six factors, including lithology, land use/cover, distance to stream, slope gradient, slope aspect, and slope curvature were evaluated. The effects of the landslide factor of slope instability were determined by comparing with landslide inventory raster using the GIS environment. The landslide susceptibility maps of the Uatzau area were categorized into very low, low, moderate, high and very high susceptibility classes. The landslide susceptibility maps of the three models validated by the ROC curve. The results for the area under the curve (AUC) are 88.83% for the frequency ratio model, 87.03% for certainty factor, and 84.83% of information value models, which are indicating very good accuracy in the identification of landslide susceptibility zones of a region. From these resulted maps, it is possible to recommend, the statistical methods (Frequency Ratio, Information Value, and Certainty Factor Methods) are adequate to landslide susceptibility mapping. The landslide susceptibility maps can be used for regional land use planning and landslide hazard mitigation purposes.

Phytochemical Analysis and Habitat Suitability Mapping of Glycyrrhiza glabra L. Collected in the Hatay Region of Turkey.

The growth and quality of licorice depend on various environmental factors, including the local climate and soil properties; therefore, its cultivation is often unsuccessful. The current study investigated the key factors that affect the contents of bioactive compounds of Glycyrrhiza glabra L. root and estimated suitable growth zones from collection sites in the Hatay region of Turkey. The contents of three bioactive compounds (glycyrrhizic acid, glabridin, and liquiritin), soil factors (pH, soil bearing capacity, and moisture content), and geographical information (slope, aspect, curvature, elevation, and hillshade) were measured. Meteorological data (temperature and precipitation) were also obtained. An analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA) were performed on the data. The soil bearing capacity, moisture content, slope, aspect, curvature, and elevation of the study area showed statistically significant effects on the glycyrrhizic acid and liquiritin contents. A habitat suitability zone map was generated using a GIS-based frequency ratio (FR) model with spatial correlations to the soil, topographical, and meteorological data. The final map categorized the study area into four zones: very high (15.14%), high (31.50%), moderate (40.25%), and low suitability (13.11%). High suitability zones are recommended for further investigation and future cultivation of G. glabra.

GIS-based gully erosion susceptibility modeling, adapting bivariate statistical method and AHP approach in Gombe town and environs Northeast Nigeria

Gully erosion is a major environmental problem in Gombe town, a large area of land is becoming unsuitable for human settlement, hence the need for a gully erosion susceptibility map of the study area. To generate a gully inventory map, a detailed field exercise was carried out, during this investigation one hundred gullies were identified and studied extensively within the study area of about 550 km2. In addition to the mapped gullies, Google EarthPro with high-resolution imagery was used to locate the spatial extents of fifty (50) more gullies. Ten gully erosion predisposing factors were carefully selected considering the information obtained from literature, and multiple field survey of the study area, the factors include elevation, slope angle, curvature, aspect, topographic wetness index (TWI), soil texture, geology, drainage buffer, road buffer and landuse. In this study, a GIS-based Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models were employed to predict areas prone to gully erosion in Gombe town and environs. The result obtained from FR shows that drainage, soil texture, and slope have the highest correlation with gully occurrence, while the AHP model revealed that drainage buffer, soil texture, geology have a high correlation with the formation of a gully. Gully erosion susceptibility maps (GESM) were produced and reclassified into very high, high, moderate, and low zones. The overall accuracies of both models were tested utilizing area under the curve (AUC) values and gully density distribution.FR and AHP model have AUC values of 0.73 and 0.72 respectively, the outcome indicates that both models have high prediction accuracy. The gully erosion density distribution values revealed that gullies are concentrated in the very high susceptibility class and it decreases towards the low class, therefore the GESM produced using these models in this study area is reliable and can be used for land management and future planning.

Landslide susceptibility zonation mapping using GIS-based frequency ratio model with multi-class spatial data-sets in the Adwa-Adigrat mountain chains, northern Ethiopia

Different organizations reported that natural hazards related disasters killed hundreds of people and damaged infrastructures estimated millions of dollars every year worldwide. The purpose of this research was to generate landslide susceptibility zonation (LSZ) map for the first time and understand the main landside conditioning factors in the Adwa-Adigrat mountain chain (about 2540 km2). At initial stage, landslide inventory map was prepared using classical fieldwork, stereo aerial photographic analysis and interpretations (API) and Google Earth™ (GE) image interpretations. A total of 546 landslides were identified, located and delineated. Rock fall was the dominant type of landslide (170), while rock slide was the smallest (only 28 rock slides). Nearly 7.6% of the total study area is found to be affected by past and active landslides. Five spatial data-sets or landslide conditioning factors: lithology, land use and land cover, distance from streams, distance from lineament/faults, slope aspect and slope angle maps were prepared and analyzed. Frequency ratio (FR) model was used to generate LSZ map. The generated LSZ map was classified into very low, low, moderate, high and very high susceptibility zones. The validation and verification of the LSZ map was carried out using the accuracy statistics approach and the area under the curve (AUC) methods. Both methods confirmed the reliability and good fit of the FR method for the present study area. It was observed that 73.8% of the past landslides fell in the high and very high susceptibility zones. In mountain chains, field studies for landslide inventory and LSZ mapping is important. The produced LSZ map can help to predict and mitigate landslide related damages and risks, and future land use planning.

GIS-Based Frequency Ratio Method for Identification of Potential Landslide Susceptible Area in the Siwalik Zone of Chatara-Barahakshetra Section, Nepal

The hilly regions of Nepal are potential for land sliding in rainy season. Lying between two major thrusts: Main Frontal Thrust (MFT) and Main Boundary Thrust (MBT), the rocks of Siwalik zone are very weak and fragile. Shallow and deep landslides are very common in the Siwalik zone during heavy and continuous rainfall. The landslide in the busy road and agglomerate settlements are destroying the life and properties every year in rainy season. This study aims to develop a landslide susceptibility map of Chatara-Barahakshetra area, Siwalik zones of eastern Nepal by the means of frequency ratio model. The paper utilized the remote sensing and GIS to develop a landslide susceptibility map. Total of 382 landslide polygons were mapped from Google earth and by field verification. The validation results showed that the success rate curve with 72.55 percentage of the area lying under the curve and the prediction rate curve with 71.73 percentage of the area lying under the curve indicating that prediction ability of the Frequency Ratio model. These landslide susceptibility maps can be used as a planning tool by prioritizing areas for controlling the landslide effects. More than 71% success rate indicate that frequency ratio model is suitable model for the landslide susceptibility in the study area.

GIS-based frequency ratio and logistic regression modelling for landslide susceptibility mapping of Debre Sina area in central Ethiopia

Ethiopia has a mountainous landscape which can be divided into the Northwestern and Southeastern plateaus by the Main Ethiopian Rift and Afar Depression.Debre Sina area is located in Central Ethiopia along the escarpment where landslide problem is frequent due to steep slope, complex geology, rift tectonics, heavy rainfall and seismicity. In order to tackle this problem, preparing a landslide susceptibility map is very important. For this, GIS-based frequency ratio (FR) and logistic regression (LR) models have been applied using landslide inventory and the nine landslide factors (i.e. lithology, land use, distance from river & fault, slope, aspect, elevation, curvature and annual rainfall). Database construction, weighting each factor classes or factors, preparing susceptibility map and validation were the major steps to be undertaken. Both models require a rasterized landslide inventory and landslide factor maps. The former was classified into training and validation landslides. Using FR model,weights for each factor classes were calculated and assigned so that all the weighted factor maps can be added to produce a landslide susceptibility map. In the case of LR model, the entire study area is firstly divided into landslide and non-landslide areas using the training landslides. Then, these areas are changed into landslide and non-landslide points so as to extract the FR maps of the nine landslide factors. Then a linear relationship is established between training landslides and landslide factors in SPSS. Based on this relationship, the final landslide susceptibility map is prepared using LR equation. The success-rate and prediction-rate of FR model were 74.8% and 73.5%, while in case of LR model these were 75.7% and 74.5% respectively. A close similarity in the prediction and validation rates showed that the model is acceptable. Accuracy of LR model is slightly better in predicting the landslide susceptibility of the area compared to FR model.

Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models

Mountain areas in the southern western corner of the Kingdom of Saudi Arabia frequently suffer from various types of landslides due to rain storms and anthropogenic activities. To resolve the problem related to landslides, landslide susceptibility map is important as a quick and safe mitigation measure and to help making strategic planning by identifying the most vulnerable areas. This paper summarizes findings of landslide susceptibility analysis at Al-Hasher area, Jizan, KSA, using two statistical models: frequency ratio and index of entropy models with the aid of GIS tools and remote sensing data. The landslide locations (inventory map) were identified in the study area using historical records, interpretation of high-resolution satellite images that include Geo-Eye in 2.5 m and Quickbird in 0.6m resolution, topographic maps of 1:10,000 scale, and multiple field investigations. A total of 207 landslides (80% out of 257 detected landslides) were randomly selected for model training, and the remaining 50 landslides (19%) were used for the model validation. Ten landslide conditioning factors including slope angle, slope-aspect, altitude, curvature, lithology, distance to lineaments, normalized difference vegetation index (NDVI), distance to roads, precipitation, and distance to streams, were extracted from spatial database. Using these conditioning factors and landslide locations, landslide susceptibility and weights of each factor were analyzed by using frequency ratio and index of entropy models. Our findings showed that the existing landslides of high and very high susceptibility classes cover nearly 80.4% and 79.1% of the susceptibility maps produced by frequency ratio and index of entropy models respectively. For verification, receiver operating characteristic (ROC) curves were drawn and the areas under the curve (AUC) were calculated for success and prediction rates. For success rate the results revealed that for the index of entropy model (AUC = 77.9%) is slightly lower than frequency ratio model (AUC = 78.8%). For the prediction rate, it was found that the index of entropy model (AUC = 74.9%) is slightly lower than the frequency ratio model (AUC = 76.7%). The landslide susceptibility maps produced from this study could help decision makers, planners, engineers, and urban areas developers to make suitable decisions.

GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran

This study presents a landslide susceptibility assessment for the Caspian forest using frequency ratio and index of entropy models within geographical information system. First, the landslide locations were identified in the study area from interpretation of aerial photographs and multiple field surveys. 72 cases (70 %) out of 103 detected landslides were randomly selected for modeling, and the remaining 31 (30 %) cases were used for the model validation. The landslide-conditioning factors, including slope degree, slope aspect, altitude, lithology, rainfall, distance to faults, distance to streams, plan curvature, topographic wetness index, stream power index, sediment transport index, normalized difference vegetation index (NDVI), forest plant community, crown density, and timber volume, were extracted from the spatial database. Using these factors, landslide susceptibility and weights of each factor were analyzed by frequency ratio and index of entropy models. Results showed that the high and very high susceptibility classes cover nearly 50 % of the study area. For verification, the receiver operating characteristic (ROC) curves were drawn and the areas under the curve (AUC) calculated. The verification results revealed that the index of entropy model (AUC = 75.59 %) is slightly better in prediction than frequency ratio model (AUC = 72.68 %). The interpretation of the susceptibility map indicated that NDVI, altitude, and rainfall play major roles in landslide occurrence and distribution in the study area. The landslide susceptibility maps produced from this study could assist planners and engineers for reorganizing and planning of future road construction and timber harvesting operations.

Crustacean habitat potential mapping in a tidal flat using remote sensing and GIS

To make a macrofaunal (crustacean) habitat potential map, the spatial distribution of ecological variables in the Hwangdo tidal flat, Korea, was explored. Spatial variables were mapped using remote sensing and a geographic information system (GIS) combined with field observations. A frequency ratio (FR) and logistic regression (LR) model were employed to map the macrofauna potential area for the Ilyoplax dentimerosa, a crustacean species. Spatial variables affecting the tidal macrofauna distribution were selected based on abundance and biomass and used within a spatial database derived from remotely sensed data of various types of sensors. The spatial variables included the intertidal digital elevation model (DEM), slope, distance from a tidal channel, tidal channel density, surface sediment facies, spectral reflectance of the near infrared (NIR) bands and the tidal exposure duration. The relation between the I. dentimerosa and each spatial variable was calculated using the FR and LR. The species was randomly divided into a training set (70%) to analyse habitat potential using FR and LR and a test set (30%) to validate the predicted habitat potential map. The relations were overlaid to produce a habitat potential map with the species potential index (SPI) value for each pixel. The potential habitat maps were compared with the surveyed habitat locations such as validation data set. The comparison results showed that the LR model (accuracy is 85.28%) is better in prediction than the FR (accuracy is 78.96%) model. The performance of models gave satisfactory accuracies. The LR provides the quantitative influence of variables on a potential habitat of species; otherwise, the FR shows the quantitative influence of a class in each variable. The combination of a GIS-based frequency ratio and logistic regression models and remote sensing with field observations is an effective method to determine locations favorable for macrofaunal species occurrences in a tidal flat.

Spatial relationships between surface sedimentary facies distribution and topography using remotely sensed data: Example from the Ganghwa tidal flat, Korea

Spatial relationships between surface sedimentary facies distribution and topography, including channel networks and an intertidal digital elevation model (DEM), were estimated for the Ganghwa tidal flat, Korea. Tidal channels were extracted from high spatial resolution satellite data and used to derive maps of channel density and distance from channels. The intertidal DEM was generated by a waterline method using Landsat Enhanced Thematic Mapper Plus (ETM+) images, and the map of surface sedimentary facies distribution was produced from grain-size data on sediment samples. A GIS-based frequency ratio model was applied to quantify the relationships between the surface sediment facies and topographic factors. The result indicated that the fine-grained mud flat facies occurs mainly in areas of complex tidal channels and in elevated areas that have a high spectral reflectance in the Landsat ETM+ image. The coarse-grained sand flat facies is concentrated in areas of sparse tidal channels, and the mixed flat facies is distributed where tidal channels have an intermediate level of complexity. The results demonstrate a significant spatial relationship between the distribution of surface sediment in a tidal flat and topographic features. We conclude that tidal channels should be considered an important factor in mapping surface sedimentary facies in tidal flat environments, and that patterns of tidal topography are a useful adjunct to spectral reflectance in classifying sedimentary facies from remotely sensed data with high spatial resolution.