Evidential Belief Function Model Research Articles

Comprehensive landslide prediction mapping using bivariate statistical models of Mizoram state of Northeast India

ABSTRACT Landslides in the state of Mizoram result in damage to life and properties annually. The study focuses on landslide susceptibility zones by frequency ratio (FR), evidential belief function (EBF) and index of entropy (IOE) models. A total of 1,486 landslide points were used to build a relationship between 16 factors and landslide occurrences. The results reveal 14.44%, 19.64% and 3.55% of the area as very high susceptible zones in FR, EBF and IOE models, respectively. The AUC results support the adoption of the EBF model in land use planning and decision-making processes to enhance natural resource management and mitigate landslide risks in Mizoram.

Determining land subsidence potential using the evidential belief function model: A case study for the Bardaskan Aquifer, Iran

AbstractIn this study, we employed the evidential belief function model (EBF) to evaluate the potential for land subsidence in the primary aquifer of Bardaskan. Through field visits, we recorded GPS coordinates for 174 land subsidence points. Factors considered in assessing land subsidence potential included well density, groundwater extraction rate, geological characteristics, proximity to faults, vegetation cover, distance from the river, slope, and land use. To develop and validate the model, 70% of the recorded points were randomly selected for training and implementation, while the remaining 30% were reserved for model validation. The number and percentage of land subsidence points in the different classes of the corresponding layers were determined by integrating the training points with influential variables maps such as distance from the river, distance from the fault, land use, and extraction volume. The EBF model rate was calculated for different layer classes. For modeling, all rates of the EBF model in each cell were summated, and the ‎potential of land subsidence was calculated.‎ Finally, the map of land subsidence potential based on the EBF model was determined with GIS. The results showed that most of the subsidence points were located in alluvial sediment of the Holocene period, in areas with high groundwater harvesting, a distance of at least 3000 m from a river, a distance of at least 6000 m from a fault, low‐density rangelands, slopes of at least 0%–2%, and farmlands and gardens. A receiver operating characteristic curve analysis of the EBF model showed that it could accurately predict land subsidence in 87.5% of cases using 30% of the validation data. This suggests that the model can be used for practical applications.

GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India

Predicting landslides is becoming a crucial global challenge for sustainable development in mountainous areas. This research compares the landslide susceptibility maps (LSMs) prepared from five GIS-based data-driven bivariate statistical models, namely, (a) Frequency Ratio (FR), (b) Index of Entropy (IOE), (c) Statistical Index (SI), (d) Modified Information Value Model (MIV) and (e) Evidential Belief Function (EBF). These five models were tested in the high landslides-prone humid sub-tropical type Upper Tista basin of the Darjeeling-Sikkim Himalaya by integrating the GIS and remote sensing. The landslide inventory map consisting of 477 landslide locations was prepared, and about 70% of all landslide data was utilized for training the model, and 30% was used to validate it after training. A total of fourteen landslide triggering parameters (elevation, slope, aspect, curvature, roughness, stream power index, TWI, distance to stream, distance to road, NDVI, LULC, rainfall, modified fournier index, and lithology) were taken into consideration for preparing the LSMs. The multicollinearity statistics revealed no collinearity problem among the fourteen causative factors used in this study. Based on the FR, MIV, IOE, SI, and EBF approaches, 12.00%, 21.46%, 28.53%, 31.42%, and 14.17% areas, respectively, identified in the high and very high landslide-prone zones. The research also revealed that the IOE model has the highest training accuracy of 95.80%, followed by SI (92.60%), MIV (92.20%), FR (91.50%), and EBF (89.90%) models. Consistent with the actual distribution of landslides, the very high, high, and medium hazardous zones stretch along the Tista River and major roads. The suggested landslide susceptibility models have enough accuracy for usage in landslide mitigation and long-term land use planning in the study area. Decision-makers and local planners may utilise the study's findings. The techniques for determining landslide susceptibility can also be employed in other Himalayan regions to manage and evaluate landslide hazards.

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.

A Review on Landslide Susceptibility Mapping in Malaysia: Recent Trend and Approaches

The accelerating economic growth has assisted rapid urban development and expansion of construction sites into the landslide-vulnerable zones in Malaysia. Thus landslide susceptibility mapping has now become an important part of project designing work for landslide zone areas. There are several models that are used for susceptibility mapping, especially in the peninsular region. Every model has its own set of selected computing variables and characteristics to generate a map. To date, there is no single method applicable to assess and predict all landslides, as there are variations of geomorphological conditions set by the nature. This paper has reviewed recent research publications on landslide susceptibility mapping in Malaysia. Results show that there are 16 models that are being used to describe landslide risk mapping and among them, the Fuzzy model, Neural Network combined with Fuzzy logic, evidential belief function model, probability analysis (e.g. Weights-of-Evidence, and regression), and Support Vector Machine models are proved to be effective even in the areas with limited information. It is observed that most of the susceptible models use curvature, slope angles, distance from drainage, altitude, slope gradient, road distance, aspects as variable factors, and prolonged rainfall as the prime triggering factors. Furthermore, it is observed that the maximum number of research has been conducted in Cameron Highlands (28%) and Penang (20%), because of their high frequencies of landslide occurring and vulnerabilities. Sabah and Sarawak are covered by a negligible number of susceptibility research. Further, a comparison study between the selected models presents the limitations of each model and their benefits and some suggestions are also made based on the author's recommendations works.

A Comparative Study of Forest Fire Mapping Using GIS-Based Data Mining Approaches in Western Iran

Mapping fire risk accurately is essential for the planning and protection of forests. This study aims to map fire risk (probability of ignition) in Marivan County of Kurdistan province, Iran, using the data mining approaches of the evidential belief function (EBF) and weight of evidence (WOE) models, with an emphasis placed on climatic variables. Firstly, 284 fire incidents in the region were randomly divided into two groups, including the training group (70%, 199 points) and the validation group (30%, 85 points). Given the previous studies and conditions of the region, the variables of slope percentage, slope direction, altitude, distance from rivers, distance from roads, distance from settlements, land use, slope curvature, rainfall, and maximum annual temperature were considered for zoning fire risk. Then, forest fire risk maps were prepared using the EBF and WOE models. The performance of each model was examined using the Relative Operating Characteristic (ROC) curve. The results showed that WOE and EBF are effective tools for mapping forest fire risks in the study area. However, the WOE model shows a slightly higher Area Under the Curve value (0.896) compared to that of the EBF model (0.886), indicating a slightly better performance. The results of this study can provide valuable information for preventing forest fires in the study area.

Landslide susceptibility mapping using GIS-based bivariate models in the Rif chain (northernmost Morocco)

The coastline between Tetouan and Bou Ahmed in the northernmost Rif of Morocco and its hinterland has become immensely hazardous due to frequent triggering of diversified landslides from last two decades. This paper describes the potential application of a set of multisource data and the GIS platform for zoning and identifying anomalous areas prone to landsliding and its associated landslide hazards. For this purpose, Information value (IV), Statistical index SI (Wi), Weighting factors (WF) and Evidential belief function (EBF) models have been used in this study. Eleven conditioning factors such as elevation, slope, aspect, curvature, shaded/relief, proximity to streams, proximity to faults, proximity to roads, land use, lithology, annual rainfall and an inventory of 905 unstable spots were used to develop the spatial database for landslides susceptibility mapping (LSM). The factors have been used after a test of multi-collinearity. The Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) methods were used for validation of the LSM. The AUC results showed good prediction accuracy for all models with a prediction rate of 78% (IV), 77% (SI), 73% (WF) and 70% (EBF) respectively. However, the results indicated that comparatively, the IV model followed by WI model is more precise and accurate for landslides susceptibility mapping than other models. According to the presented models, about 64% of the study area is located in high to very high landslide susceptible zone. The findings presented in this study are imperatively valuable especially wherein large development projects and land use planning activities are going on.

Assessment of groundwater vulnerability in an urban area: a comparative study based on DRASTIC, EBF, and LR models.

The groundwater vulnerability assessment is known as a useful tool for predicting and prevention of groundwater pollution. This study targets the DRASTIC, evidential belief function (EBF), and logistic regression (LR) models to assess vulnerability in Kabul aquifers, Afghanistan Country. The growth of urban sprawl, groundwater overexploitation, and lack of suitable municipal sewage systems as anthropogenic sources have been the main potential to increase groundwater contaminants such as nitrate in the study area. The vulnerability map has been developed based on various effective factors including altitude, slope (percentage rise), aspect, curvature, land-use type, drainage density, distance from river, annual mean precipitation, net recharge, geology/lithology units, the impact of the vadose zone, aquifer media, depth to water (unsaturated zone), saturated zone, drawdown, and hydraulic conductivity. To identify groundwater pollution, the spatial variation of nitrate concentration data in 2018 was considered indication of groundwater pollution. Based on descriptive statistics, the value of 2.65mg/l (the median of the pixel values of nitrate map) was selected as a threshold to differentiate the occurrence and non-occurrence of pollution. The groundwater quality data were selected and randomly divided into two datasets for training and validation, including 70% and 30%, respectively. The success-rate and prediction-rate curves were computed based on the receiver operating characteristic (ROC) curve and the area under the curve (AUC) to estimate the efficiency of models. The ROC-AUC of success rates for EBF, LR, and DRASTIC models were estimated to be 67%, 66%, and 52%, respectively. Moreover, the ROC-AUC of the prediction rates of the EBF, LR, and DRASTIC models were obtained 61%, 63%, and 55%, respectively. Based on correlation between mean nitrate concentration and the mean vulnerability indexes in each model, the EBF model is the most compatible with the current developed vulnerability zones as the role of mankind in changing the environment in real conditions in comparison to LR and DRASTIC models.

Factors affecting topographic thresholds in gully erosion occurrence and its management using predictive machine learning models

Soil degradation induced by gully erosion represents a worldwide problem in the many arid and semi-arid countries, such as Iran. This study assessed: (1) the importance of variables that control gully erosion using the Boruta algorithm, (2) the relationship among causative variables and gullied locations using the evidential belief function model (EBF), and (3) gully erosion development using the algorithms of boosted regression tree (BRT) and support vector machine (SVM). Based on the results of the Boruta algorithm, slope, land use, lithology, plan curvature, and elevation were the most important factors controlling gully erosion. The results of the EBF model showed the predominance of gully erosion on rangeland and loess-marl deposition. The predominance of gullied locations on the concave positions, with the slope of 5°–20° in the vicinity of drainage lines, illustrates a preferential topographic zone and, therefore, a terrain threshold for gullying. The correlation of gullied locations with rangelands and weak soils in concave positions demonstrates that the interactions among soil characteristics, topography, and land use stimulate a low topographic threshold for gullies development. These relationships are consistent with the threshold concept that a given soil, land use, and climate within a given landscape encourage a given drainage area and a critical soil surface slope that are necessary for gully incision. Furthermore, the BRF-SVM had the highest efficiency and the lowest root mean square error, followed by BRT for predicting gully development, compared with LN-SVM algorithm. The application of two machine learning methods for predicting the gully head cut susceptibility in northern Iran showed that the maps generated by these algorithms could provide an appropriate strategy for geo-conservation and restoration efforts in gullying-prone areas.

Landslides behavior spatial modeling by using evidential belief function model, Promethean II model, and index of entropy in Tabriz, Iran

Due to the increasing construction of clay and marl hills in most areas of Tabriz (Iran), its characteristics in terms of resistance, and its tendency to liquefy during earthquakes, this city is at risk of landslides. This paper studies the landslide vulnerability of Tabriz using visual and statistical evidence. The evaluation of landslide susceptibility was performed using the evidential belief function model (EBF) and the index of entropy. The environmental impact assessment of landslides was carried out using the Promethean II model in three environmental, economic, and social phases. Finally, a landslide strategy plan for decision-makers was developed. The results of the analysis using the EBF model showed that 89.81% of the total area of Tabriz is located in a direct landslide vulnerability zone. The output of the receiver operating characteristics (ROC) curve showed 83.3% accuracy of the EBF model. The impact assessment showed that for the environment, the geological criterion had an output weight of 0.396; for the economy, the road criterion had a weight of 0.477; and for the society, the commercial criterion had a weight of a 0.452; all were the most affected by landslides. The results of monitoring studies of the largest landslides in Tabriz in 1957, 1984, and 2020 showed approximately 41.65 m of land sliding over a period of 63 years. To our knowledge, this study is the first in the world to predict the environmental impact assessment and provide a strategy plan for Tabriz.

A GIS-based groundwater pollution potential using DRASTIC, modified DRASTIC, and bivariate statistical models.

The objective of the current study is groundwater vulnerability assessment using DRASTIC, modified DRASTIC, and three statistical bivariate models (frequency ratio (FR), evidential belief function (EBF), and weights-of-evidence (WOE)) for Sari-Behshahr plain, Iran. A total of 218 wells were sampled for nitrate concentration measurement in 2015. Datasets were generated using results from 109 wells having nitrate concentrations greater than 50 mg/L. The nitrate data were divided into two groups of 70% (76 locations as training dataset) for modeling and 30% (33 locations as a testing dataset) for model validation. Finally, five groundwater potential pollution (GPP) maps were produced by the training dataset and then evaluated using the testing dataset and receiver operating characteristic (ROC) method. Results of the ROC method showed that the WOE model had the highest predictive power, followed by EBF, FR, modified DRASTIC, and DRASTIC models. Results of the maps obtained revealed that high and very high pollution potential covered the southern part of the study areas, where big cities are located. Results of the present study can be replicated in other locations for identifying groundwater contaminant prone areas.

Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping

The survival of humanity is dependent on the survival of forests and the ecosystems they support, yet annually wildfires destroy millions of hectares of global forestry. Wildfires take place under specific conditions and in certain regions, which can be studied through appropriate techniques. A variety of statistical modeling methods have been assessed by researchers; however, ensemble modeling of wildfire susceptibility has not been undertaken. We hypothesize that ensemble modeling of wildfire susceptibility is better than a single modeling technique. This study models the occurrence of wildfire in the Brisbane Catchment of Australia, which is an annual event, using the index of entropy (IoE), evidential belief function (EBF), and logistic regression (LR) ensemble techniques. As a secondary goal of this research, the spatial distribution of the wildfire risk from different aspects such as urbanization and ecosystem was evaluated. The highest accuracy (88.51%) was achieved using the ensemble EBF and LR model. The outcomes of this study may be helpful to particular groups such as planners to avoid susceptible and risky regions in their planning; model builders to replace the traditional individual methods with ensemble algorithms; and geospatial users to enhance their knowledge of geographic information system (GIS) applications.

An improved approach for predicting the groundwater potentiality in the low desert lands; El-Marashda area, Northwest Qena City, Egypt

The harsh environment of Egypt makes the potentiality assessment of the groundwater of high concern where the groundwater considered as the foremost water supply for irrigation and domestic purposes. In the present work, the frequency ratio (FR), analytical hierarchy process (AHP), and evidential belief function (EBF) models were integrated with Geographic Information System (GIS) and Ternary Image (TI) model as a new integrated approach for evaluating groundwater potentiality. The accuracy of our model was compared applying the area under the curve (AUC). The prediction rates of AUC that acquired for AHP, FR, EBF, and TI were about 72%, 77%, 67% and 79.13%, respectively. Consequently, the Ternary image potential zoning map classified well the Northwest Qena city area depending on the water occurrences. The models validation output demonstrates that both AHP and TI proved an efficiency for groundwater potentials zones. Our new integrated approach can be implemented in the similar harsh areas for water resources potentiality prediction in Africa and other alike areas.

Performance Evaluation and Comparison of Bivariate Statistical-Based Artificial Intelligence Algorithms for Spatial Prediction of Landslides

The purpose of this study is to compare nine models, composed of certainty factors (CFs), weights of evidence (WoE), evidential belief function (EBF) and two machine learning models, namely random forest (RF) and support vector machine (SVM). In the first step, fifteen landslide conditioning factors were selected to prepare thematic maps, including slope aspect, slope angle, elevation, stream power index (SPI), sediment transport index (STI), topographic wetness index (TWI), plan curvature, profile curvature, land use, normalized difference vegetation index (NDVI), soil, lithology, rainfall, distance to rivers and distance to roads. In the second step, 152 landslides were randomly divided into two groups at a ratio of 70/30 as the training and validation datasets. In the third step, the weights of the CF, WoE and EBF models for conditioning factor were calculated separately, and the weights were used to generate the landslide susceptibility maps. The weights of each bivariate model were substituted into the RF and SVM models, respectively, and six integrated models and landslide susceptibility maps were obtained. In the fourth step, the receiver operating characteristic (ROC) curve and related parameters were used for verification and comparison, and then the success rate curve and the prediction rate curves were used for re-analysis. The comprehensive results showed that the hybrid model is superior to the bivariate model, and all nine models have excellent performance. The WoE–RF model has the highest predictive ability (AUC_T: 0.9993, AUC_P: 0.8968). The landslide susceptibility maps produced in this study can be used to manage landslide hazard and risk in Linyou County and other similar areas.

Urban Inundation Hazard Potential using Evidential Belief Function model (EBF) (Case study: Emam Ali town, Mashhad city)

Urban Inundation Hazard Potential using Evidential Belief Function model (EBF) (Case study: Emam Ali town, Mashhad city)

Urban flood susceptibility zonation mapping using evidential belief function, frequency ratio and fuzzy gamma operator models in GIS: a case study of Greater Mumbai, Maharashtra, India

Greater Mumbai is one of the major sufferers of urban flood in India. In the present study, flood susceptibility zonation (FSZ) mapping of Greater Mumbai were performed using Geographical Information Systems (GIS) based models, evidential belief function (EBF), frequency ratio (FR), fuzzy gamma operator (FGO). For FSZ mapping, 12 conditioning factors were considered which included elevation, slope, plan curvature, topographic wetness index, stream power index, built-up index, geology, soil type, land use land cover, modified Fournier index, proximity to natural drainage and proximity to sewers and storm water drainage. The flood inventory database was divided into training and test data. The factor maps were integrated using GIS. The FSZ map was validated using area under curve (AUC) method. The AUC values of prediction rate using test data for FR, FGO, and EBF models were 0.7400, 0.7231 and 0.7268 respectively. The accuracy assessment of all three models shows fair prediction accuracy.

Hybrid Computational Intelligence Models for Improvement Gully Erosion Assessment

Gullying is a type of soil erosion that currently represents a major threat at the societal scale and will likely increase in the future. In Iran, soil erosion, and specifically gullying, is already causing significant distress to local economies by affecting agricultural productivity and infrastructure. Recognizing this threat has recently led the Iranian geomorphology community to focus on the problem across the whole country. This study is in line with other efforts where the optimal method to map gully-prone areas is sought by testing state-of-the-art machine learning tools. In this study, we compare the performance of three machine learning algorithms, namely Fisher’s linear discriminant analysis (FLDA), logistic model tree (LMT) and naïve Bayes tree (NBTree). We also introduce three novel ensemble models by combining the aforementioned base classifiers to the Random SubSpace (RS) meta-classifier namely RS-FLDA, RS-LMT and RS-NBTree. The area under the receiver operating characteristic (AUROC), true skill statistics (TSS) and kappa criteria are used for calibration (goodness-of-fit) and validation (prediction accuracy) datasets to compare the performance of the different algorithms. In addition to susceptibility mapping, we also study the association between gully erosion and a set of morphometric, hydrologic and thematic properties by adopting the evidential belief function (EBF). The results indicate that hydrology-related factors contribute the most to gully formation, which is also confirmed by the susceptibility patterns displayed by the RS-NBTree ensemble. The RS-NBTree is the model that outperforms the other five models, as indicated by the prediction accuracy (area under curve (AUC) = 0.898, Kappa = 0.748 and TSS = 0.697), and goodness-of-fit (AUC = 0.780, Kappa = 0.682 and TSS = 0.618). The analyses are performed with the same gully presence/absence balanced modeling design. Therefore, the differences in performance are dependent on the algorithm architecture. Overall, the EBF model can detect strong and reasonable dependencies towards gully-prone conditions. The RS-NBTree ensemble model performed significantly better than the others, suggesting greater flexibility towards unknown data, which may support the applications of these methods in transferable susceptibility models in areas that are potentially erodible but currently lack gully data.

Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India

Flooding is the overflow of water from stream, river, lake and sea that occurs all over the world and has disastrous effects on human society and environment. Frequent severe flood event in eastern India cause of death and damages every year so, the development of flood susceptibility method is needed for identifying the flood vulnerability areas to reduce the damages. Techniques of Remote Sensing (RS) and Geographical Information System (GIS) can help to flood susceptibility modeling by the accrued and analyzing huge amount of data in short time. The main objectives of this study are to determine the effectiveness of Evidence Belief Function (EBF), binomial Logistic Regression (LR) and ensemble of EBF and LR (EBF-LR) model with RS and GIS techniques for flood susceptibility mapping and spatial prediction of flood-susceptible areas in the Koiya river basin of West Bengal, India. Eight flood conditioning factors; Land use and land cover (LULC), soil, rainfall, normalized differences vegetation index (NDVI), distance to river, elevation, topographic wetness index (TWI) and stream power index (SPI) have been used, and total 264 historical flooding points were mapped, and randomly divided in to training (70%) and validating (30%) dataset. Flood susceptibility map has been generated by applying EBF, LR and ensemble EBF-LR method with the help of training and eight causative factors dataset. The maps have been divided in to six classes; extremely low, very low, low, moderate, high and very high. The receiver operating characteristic (ROC) curve has been used to accuracy assessment of the susceptibility map, and the area under curve (AUC) disclosures 87.9%, 85.2% and 84.1% prediction rate for the EBF-LR, EBF and LR model, respectively. This study is helpful to flood management program, dissection makers and planning in local administrative level.

Non-path dependent urban growth potential mapping using a data-driven evidential belief function

Improper urbanization and its environmental impacts have imposed many problems to humanity. Recently, numerous studies have been conducted using different methods to understand and manage spatial and temporal changes in urbanization. In this study, the capability of the data-driven evidential belief function model as a non-path dependent urban growth potential mapping method was evaluated. Using this approach, the conventional trend-based urban growth prediction procedure is transformed into a data integration task through which the potential locations for urban sprawl in response to multiple environmental and anthropogenic variables could be determined. Therefore, true knowledge about urban growth conditioning factors and their quantitative relationships with built-up areas can be obtained. The multivariate-based logistic regression model as a well-known urban growth modelling method was employed to check the efficiency and validity of the proposed evidential belief function model. Furthermore, a hybrid approach based on the logistic regression model results coupled with the data-driven evidential belief function model was developed. The validation results using the relative operating characteristic method indicated that the evidential belief function, logistic regression, and the hybrid methods’ accuracy were 91.81, 84.72, and 92.34%, respectively. Therefore, it can be concluded that while the proposed evidential belief function and hybrid methods for non-path dependent urban growth potential mapping yielded approximately equal results, both of them outperformed the logistic regression model which is an indication of their reliability and accuracy. The proposed evidential belief function and hybrid methods are best suited for integration in different environmental and socio-economic scenarios enhancing the models for urban allocation tasks. In this way, local communities and policy-makers can make smarter decisions.

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

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.