1. Introduction
A natural disaster is a major adverse event resulting from the natural processes of the Earth. These can include floods, hurricanes, tornadoes, volcanic eruptions, earthquakes, tsunamis, etc. A natural disaster can cause loss of life and damage to property, which typically results in economic damage, the severity of which depends on the affected population’s resilience, or ability to recover, and also on the available infrastructure. Among natural disasters, floods are considered to be one of the most devastating [
1], and an accurate assessment of its risks is hampered by a lack of data and knowledge about flood losses at different scales [
2]. During a heavy rainfall event, the amount of flow discharge in a river increases rapidly and the water level will rise above its normal bed, covering the flood plain and surrounding areas [
3]. Life-threatening water overflow in residential areas is a common occurrence in Iran after earthquakes (
http://www3.irna.ir/fa/NewsPrint.aspx?ID=214943), and catastrophic flooding events happen annually in the Mazandaran, Guilan, and Golestan Provinces in Northern Iran. Due to the high flood-frequency in this region, to prevent loss of life and property damage, areas with a high risk of flooding must be recognized according to flood susceptibility maps [
4]. The primary difference between flood susceptibility and flood inundation maps is that flood susceptibility maps (FSM) only show the areas that have a high potential for flooding, while flood inundation maps can identify flood-prone areas based on different flood depths [
5,
6].
On a global scale, floods are the most destructive natural disasters, causing the highest number of deaths and damage; in fact, Opolot [
7] noted that almost 99 million people around the world were affected by floods between 2000 and 2008. In the most recent decade, the occurrence of repeated flood events in the northern part of Iran reached its historical maximum, with the rate and extent of damage increasing every year. Examples of recent floods in the Mazandaran Province and its cities include Noshahr City in 1995, 2003, and 2012, Neka City in 1999, Behshar City in 2013, and Sari City also in 2013. The flood in Neka City caused more than
$1,000,000 worth of damage to agricultural lands including wheat, barley, and rice fields. The arable lands of about 28 villages, with an area of 80 ha, were also destroyed in the Chahardangeh District, Sari, while 150 ha of agricultural lands were destroyed in Klijan Restagh in Sari City. Hence, identification and evaluation of sensitive areas are essential in order to prevent and mitigate flood damages and losses [
4]. Hydrologists have used several models to prepare flooding maps, but many of these models are data intensive or difficult to calibrate; however, some of the models are still required to understand the physical processes occurring within the catchment [
8].
In recent years, many statistical and probabilistic models have been tested to prepare flood susceptibility maps [
9,
10]. Geographic Information System (GIS) has been used as an effective tool for spatial analysis and data manipulation due to its ability to handle large amounts of spatial data [
11]. Specifically, the combination of statistical and probabilistic models with Remote Sensing (RS) and GIS has been widely used by different researchers [
1,
12]. Additionally, some scientists and researchers have studied natural disasters, specifically floods and FSM, with the help of RS and GIS, using different models such as Decision-Tree (DT) [
6,
13], Support Vector Machine (SVM) [
14,
15], Frequency Ratio (FR) [
16,
17], Evidential Belief Function (EBF) [
18,
19,
20], EBF-AHP (Analytical Hierarchy Process) [
21], Logistic Regression (LR) [
22], Shannon’s entropy and weights-of-evidence [
23], Artificial Neural Networks (ANN) [
23], AHP [
23,
24], Random Forest [
3,
23], and Adaptive Neuro-Fuzzy Inference System (ANFIS) [
25,
26]. Recently, Khosravi et al. [
27] compared the prediction power of the data mining algorithms of Naïve Bayes and Naïve Bayes Tree with three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS, and SAW). Their finding show that although MCDM models could predict flood-prone areas, the data mining algorithms had a higher prediction power than MCDMs since MCDMs rely on expert opinion. Arabameri et al. [
28] applied an EBF model to the generation of flood susceptibility maps and compared the results with FR, TOPSIS, and VIKOR models, concluding that the EBF model performed best.
Recently, hybrid machine learning methods have been applied to studies relating to the spatial prediction of natural hazards such as landslides [
12,
20,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45,
46,
47,
48,
49], wildfires [
50], sinkholes [
51], droughts [
52], gully erosion [
53,
54], and groundwater [
55,
56] and land/ground subsidence [
12]. An advantage of the ensemble algorithms is that they have a higher goodness-of-fit and prediction accuracy than individual or single-based methods/algorithms.
Khosravi et al. [
57] stated that (1) every model has its advantages and disadvantages, (2) model performance depends on the data, accuracy and model structure, and (3) there is no universal guideline specifying which model should be applied in any given scenario; therefore, several models should be applied and the best of them used for further analysis. According to the literature, many machine learning and data mining algorithms have recently been applied in the field of natural hazards assessment, however, there is no consensus among researchers with regard to which model is best. Some research shows that bivariate statistical models demonstrated better predictive power than both machine learning and data mining algorithms [
23]; this is because machine learning and data mining algorithms are more complex and require an expert to perform accurate simulations. Therefore, bivariate models, which are very simple to run with similar or sometimes superior predictive power, can be used as adequate substitutes.
At present, the EBF method is rarely applied for flood analysis, though it has been used for other categories of natural disasters such as landslide susceptibility assessment [
21,
33,
58], and land subsidence [
12,
59], as well as having been used to predict groundwater potential zones [
19,
60].
The main purpose of this research is to evaluate the possibilities of using the EBF method to generate flood susceptibility maps and to assess the strengths and weaknesses of this method, as EBF has rarely been used for floods but has shown high accuracy in previous studies involving natural hazards mapping. The specific objectives of the current study include, (i) determining the most significant factors in each model, (ii) application of a bivariate statistical model, EBF, to produce new ensemble models, in combination with a statistical model, LR, in order to generate a more accurate flood susceptibility map, (iii) detection of the flood-prone areas within the study region for improved management during flood occurrence. In general, river flooding is a common natural disaster in the southern Caspian Sea, especially in the Haraz catchment; the results of the current study will be useful for land-use planning and management for future flood mitigation in the Haraz Watershed.
5. Discussion
Analysis of elevation maps, as well as the position of flood points, revealed that flooding usually occurs at lower elevations. It also indicated that the frequency of recorded flooding events decreased as elevation class increased, as floods generally occur in flat, lower elevation and lower slope areas where water can coalesce. Additionally, as TWI shows wetness, meaning areas with a high TWI have saturated soil, the flood potential is higher with greater TWI values. In particular, the river and its surrounding areas maintain higher flood susceptibility than any other region, and the flood risk was shown to be reduced by increasing the distance from the river. Furthermore, rainfall depth and EBF values showed an inverse relationship; this may be because rainfall often increases at higher altitudes, although the risk of flooding significantly decreases. Overall, these results are in accordance with the results of Khosravi et al. [
6,
16] and Tien Bui et al. [
63].
The relationship between the flood locations and lithological map depicted that least amount of flooding occurred in the areas with high permeability formations (31.062%). These formations had a high potential for saturating processes and hence allowed more infiltration than the low (37.568%) and moderate (31.369%) permeability formations with all other factors constant. The regions with volcanic rocks and low permeability allow more runoff to be transferred into streams, creating conditions where overbank can occur [
23,
97]. On the other hand, streams both collect water from runoff generated upland adjacent to the streams and from rainfall falling directly on them. Therefore, streams have been more susceptible to flooding in the Haraz catchment.
In terms of land use, villages close to the Haraz River, agricultural areas, such as citrus gardens, and areas with low topographic gradients are more susceptible to flooding. The results showed that most points recorded in the rangeland are close to registered residential areas and gardens; therefore, a single flood event may turn into a fatal natural hazard, causing catastrophic financial damage as well as claiming many human lives. Such devastation has been one of the leading causes of death and economic distress in the Haraz catchment, according to annual reports by authorities.
According to Park [
82], the main limitation of the EBF method is that if a flood event did not happen in a represented class, the Bel and Dis results would be equal to zero, meaning that by Equation (10) the Unc or uncertainty values would be equal to 1. Walley [
98] stated that if the models were provided with complete information about the study area, Pls-Bel would be expected to be equal to zero (in this case Bel is called Bayesian belief function), confirming that the two maps of Bel and Pls should have similar results [
18]; which they did in this study. Furthermore, the results of this study were also consistent with other research [
19,
21,
99,
100]. According to the results of Nampak et al. [
19], the main advantage of the Dempster–Shafer theory is that the application of an EBF model not only provides predictive maps of the desired area but also provides the predictive degree of uncertainty. The results of the present study were also consistent with Razavi and Malek [
101], as they compared the results of the EBF method with an ensemble of AHP and EBF, finding that that EBF model has a higher prediction power than the ensemble method. Tehrany and Kumar [
102] used an EBF model for flood susceptibility and compared the results with LR and FR models, similarly finding that the EBF model yielded the highest prediction power. Similarly, Arabameri at al. [
28] found that EBF model had the highest prediction power over FR, TOPSIS, and VIKOR models. In contrast, Khosravi et al. [
16], who applied FR and WOE models to a similar case study with similar input data, found that the FR and WOE models had slightly better prediction powers than the EBF model and its ensembles.
The current study showed that a greater number of input variables enhanced the results of modeling, which corresponded to Donati and Turrini [
103], who explained that a higher number of variables would likely result in improved model accuracy. The high accuracy of the EBF model used in this study was in line with Nampak et al. [
19], Pourghasemi and Beheshtirad [
65], and Rahmati et al. [
17]. Additionally, the developed FSMs showed that the areas nearest to the Haraz River with low slope, flat curvature, low altitude, and a high TWI were highly susceptible to flooding, which is in agreement with Tehrany et al. [
13] and Khosravi et al. [
6,
16]. Overall, the results of the current study could be extremely useful in improving flood management and planning within the area in order to prevent and mitigate further damages due to flooding hazards. For example, by avoiding the construction of homes, villas, or industries in susceptible zones, and by employing both structural and non-structural approaches for future flood mitigation, the enduring damage and devastation caused by floods can be significantly reduced.
One of the main limitations of the current study was the use of Google Earth, rather than a field survey, for the identification of non-flooding points, though some prior researchers that had done the same, such as Tehrany et al. [
15,
64], Khosravi et al. [
16] and Khosravi et al. [
27]. Additionally, LIDAR DEM, with its high resolution, would have likely affected the results and prediction power of the models positively; thus, it is recommended that further studies employ LIDAR instead of ASTER GDEM. Further recommendations for future research include a comprehensive comparative study involving an assessment of the accuracy and simplicity of assorted bivariate, multivariate, machine learning, data mining, and multi-criteria decision-making models and their ensembles, for improving flood prediction.
Furthermore, as the current case study was in a region with diverse topography, it is recommended that future researchers examine the models’ prediction power in mountainous areas and flat areas separately, to determine which model is better for which type of topography. The main limitation of the current methodology is that the models used resulted in flood susceptibility maps (prone areas without depth, velocity, and hydraulic details) which differ from the results of 2D flood inundation models [
97]. Therefore, it is recommended that future researchers compare the results of the present study with the 2D results of numerical models such as HEC-RAS. However, it should be noted that in comparison to susceptibility mapping, 1D and 2D hydraulic models are mostly performed on short river sections but not limited to any short reach and can be used for a catchment with a complicated drainage network. It is important to bear in mind that hydraulic models require different input data (e.g., design discharges with different return period, river cross sections, and roughness coefficients in main channel and flood plains) and better accuracy (e.g., high-resolution DEM and detailed topography). Therefore, combining the results of both flood inundation and susceptibility maps may offer a better outcome for decision makers in flood mitigation and management.
6. Conclusions
The main goal of this study was to assess the performance of an EBF model that is rarely used for the development of flood susceptibility maps, in comparison to an LR model and an ensemble LR EBF model, in creating FSMs for the Haraz Catchment, Iran. The LR model was implemented using independent variables that were weighted and reclassified by the EBF model. Based on the coefficients obtained from the LR model, the TWI parameter had the highest weight and thus the greatest impact on flood occurrence. The relationship between factors affecting flooding and the final maps generated by the models indicated that most flooding events occur in areas where the topography is relatively flat. The FSMs created in the current study illustrated that most flooding in the Haraz Catchment occurs in areas directly adjacent to the river, which are often characterized by low slope, and concave or flat curvature. Due to the steep, mountainous areas in the Haraz Catchment, frequent rapid runoff occurs, and water flows down toward the Haraz River, causing flash flooding in areas where topography permits. This is important as most residential and agricultural zones within this catchment are located in flat areas with low slopes, and thus were found to be especially susceptible to flooding.
According to the success-rate and prediction-rate curves, the EBF model exhibited the highest accuracy with a 94.61% success rate and 94.5% prediction rate. Alternatively, the developed EBF from LR model had an accuracy of 66.41%, which was 28.5% lower than the EBF model alone. Based on the expressed curves, the accuracy of the EBF-LR enter and stepwise models were found to be 83.19% and 52.98%, respectively; with the difference between these two models being 30.21%. The lowest accuracy belonged to the EBF-LR (stepwise) model, indicating this method is inappropriate for determining flood-prone areas. Furthermore, the results of the EBF-LR (stepwise) model suggested that altitude, slope, TWI, and distance from the river, may not be sufficient to adequately validate the model for flood susceptibility prediction. This is likely because the occurrence of a natural hazard, like flooding, is very complex and cannot be predicted with high accuracy when limited parameters are being considered in model development.
Furthermore, according to the FSM resulting from the EBF model, 15% of the total area was located in the high and very high susceptibility classes while 62% of the area was located within the low and very low susceptibility classes. Therefore, it is recommended that researchers and stakeholders identify flood-prone areas in additional catchments using the EBF model, which has a high degree of accuracy and relative simplicity. Finally, the results of this research also indicated that the impact of the different classes of the factors was more important for natural hazard assessment and mapping than the impact of the different factors themselves (i.e., the weights of the individual layers).
The developed model can now be used as a tool for decision making by management agencies such as the Department of Water and Natural Resources. The results and insights gained from this modeling effort can provide an improved understanding of flood susceptibility in the region. More generally, the results can help to improve the identification of flood-prone regions in other areas of the world. Overall, flood susceptibility mapping is essential for the prevention and mitigation of damages resulting from future floods, specifically in the northern areas of Iran. In future studies, it is recommended that researchers focus on the flood-prone areas identified in order to develop best management practices and implement structural and non-structural methods for potential damage reduction.