A method for flood hazard mapping based on basin morphometry: application in two catchments in Greece

Basin morphometric parameters play an important role in hydrological processes, as they largely control a catchment’s hydrologic response. Their analysis becomes even more significant when studying runoff reaction to intense rainfall, especially in the case of ungauged, flash flood prone basins. Unit hydrographs are one of the useful tools for estimating runoff when instrumental data are inadequate. In this work, instantaneous unit hydrographs based on the time-area method have been compiled along the drainage networks of two small rural catchments in Greece, situated approximately 25 km northeast of its capital, Athens. The two catchments drained by ephemeral torrents, namely Rapentosa and Charadros, have been subject to flash flooding during the last decades, which caused extensive damages at the local small towns of Marathon and Vranas. Hydrograph compilation in numerous locations along the catchments’ drainage networks directly reflected the runoff conditions across each basin against a given rainfall. This gave a holistic assessment of their hydrologic response, allowing the detection of areas where peak flow rates were elevated and therefore, there was higher flood potential. The resulting flood hazard zonation showed good correlation with locations of damages induced by past flood events, indicating that the method can successfully predict flood hazard spatial distribution. The whole methodology was based on geographic information software due to its excellent capabilities on storing and processing spatial data.     

Monte Carlo simulation-aided analytical hierarchy process (AHP) for flood susceptibility mapping in Gabes Basin (southeastern Tunisia)

Flash floods are among the most severe hazards which have disastrous environmental, human, and economic impacts. This study is interested in the characterization of flood hazard in Gabes Catchment (southeastern Tunisia), considered as an important step for flood management in the region. Analytical hierarchy process (AHP) and geographic information system are applied to delineate and characterize flood areas. A spatial database was developed based on geological map, digital elevation model, land use, and rainfall data in order to evaluate the different factors susceptible to affect flood analysis. However, the uncertainties that are associated with AHP techniques may significantly impact the results. Flood susceptibility is analyzed as a function of weights using Monte Carlo (MC) simulation and Global sensitivity analysis. AHP and MC–AHP models gave similar results. However, compared to AHP approach, MC–AHP confidence intervals (95%) of the overall scores had small overlaps. Results obtained were validated by remote sensing data for the zones that showed very high flood hazard during the extreme rainfall event of June 2014 that hit the study basin.     

Evaluation of the prediction capability of AHP and F-AHP methods in flood susceptibility mapping of Ernakulam district (India)

Natural Hazards - Floods are one of the frequent natural hazards occurring in Kerala because of the remarkably high annual rate of rainfall. The objective of this study is to prepare the flood...     

A subjective and objective integrated weighting method for landslides susceptibility mapping based on GIS

The purpose of this study is to present a weighting method, integrating subjective weight with objective weight, for landslides susceptibility mapping based on geographical information system (GIS). First, the landslide inventory, aspect, slope, proximity to streams of drainage network, proximity to railway, proximity to road, topography, elevation, lithology, tectonic activity and annual precipitation, including their subclasses, were taken as independent landslide causal factors. Second, objective weights of the causal factors were calculated according to the landslide area density based on entropy weighting method, and key factors were selected according to the rank of the objective weights. Third, trapezoidal fuzzy number weighting approach was used to assess the sub-classes of each key factor. Finally, a case study was carried out in Guizhou province, China. A landslide susceptibility map was created using weighted linear combination model based on GIS. Using a predicted map of probability, the study area was classified into four categories of landslide susceptibility: low, moderate, moderate-high, and high.     

Validation and evaluation of two multivariate statistical models for predictive shallow landslide susceptibility mapping of the Eastern Pyrenees (Spain)

This paper deals with the quality of two multivariate statistical models based on the Geographical Information System for shallow landslide susceptibility assessment in a test area at La Pobla de Lillet (Eastern Pyrenees, Spain). The quality, which was guaranteed by a rigorous methodology based on a suitable diagnosis, validation, and evaluation of the models, ensured a reliable contrast of the final susceptibility maps. This enables us to transfer the best results to the end user. Landslide susceptibility models were carried out by logistic regression and discriminant analysis of the significant conditioning factors related to the characteristics of the slope and the upslope contributing area captured from the digital elevation model and landslide distribution. The explanatory variables were tested (KS test, principal components and one-way and T-test) to select the most statistically significant ones before being introduced into the logistic and discriminant analyses. Accuracy statistics and the receiver operating characteristic curve used for diagnosis and validation showed similar prediction skills and a good fit to the data with more than 85% of unfailed cells properly classified for the two models. The evaluation of the study area and the correlation function (R ² = 0.83) between the models revealed that the discriminant model overestimated the susceptibility of the most stable zones with respect to the logistic model. Different methods of producing susceptibility maps showed marked differences in matching the models. Substantial spatial agreement (Kappa = 0.741) between binary maps produced by the standard cut-off value descended moderately (Kappa = 0.540) as a result of superimposing maps with five susceptibility levels defined by landslide percentage. Despite the fact that the two statistical models are similar in assessing susceptibility in the study area, the implications for hazard and risk management can be different because of the conservative nature of the discriminant model.     

Development of hybrid wavelet packet-statistical models (WP-SM) for landslide susceptibility mapping

Landslides are recognized as one of the most important natural hazards in many areas throughout the world. Producing landslide susceptibility maps have received particular attention from a wide range of scientists. The main objective of this study was to produce landslide susceptibility maps using hybrid wavelet packet-statistical models (WP-SM). In the first step, landslide susceptibility maps were produced using single artificial neural network (ANN), support vector machine (SVM), maximum entropy (MaxEnt), and generalized linear model (GLM). In the next step, the input maps were preprocessed using different mother wavelets in different levels. Then, the hybrid models were developed using the wavelet-based preprocessed maps. Results showed that the wavelet packet transform can be effectively used to produce precise landslide susceptibility maps. It was shown that wavelet packet transform significantly enhanced the ability of the single statistical models. The kappa coefficients were increased from 0.829 to 0.941, 0.846 to 0.978, 0.744 to 0.829, and 0.735 to 0.817 in hybrid ANN, SVM, MaxEnt, and GLM, respectively. The best wavelet transform was performed using bior1.5 with a three-level decomposition. It was also recognized that MaxEnt and GLM produced approximately poor results. However, SVM performed better than the other three models both in single and hybrid forms. ANN also outperformed MaxEnt and GLM models. Spatial distribution of the susceptible area is consistent with the observed landslide distribution pattern particularly in maps obtained from the hybrid models. The produced maps showed that the general pattern of susceptible area intensively followed the pattern of roads and sensitive geological formations.     

Proposed method for groundwater vulnerability mapping in carbonate (karstic) aquifers: the COP method

The ‘COP method’ has been developed for the assessment of intrinsic vulnerability of carbonate aquifers in the frame of the European COST Action 620. This method uses the properties of overlying layers above the water table (O factor), the concentration of flow (C factor) and precipitation (P factor) over the aquifer, as the parameters to assess the intrinsic vulnerability of groundwater. This method considers karst characteristics, such as the presence of swallow holes (C factor) and their catchment areas as well as karstic landforms, as factors which decrease the natural protection provided by overlying layers (O factor). The P factor allows for consideration of the spatial and temporal variability of precipitation, which is considered the transport agent of contamination. Two carbonate aquifers in the South of Spain, Sierra de Líbar (a conduit flow system) and Torremolinos (a diffuse flow system), have been selected for the application and validation of the method and the results have been compared with three methods widely applied in different aquifers around the world (AVI, GOD and DRASTIC). Comparisons with these methods and validation tools (hydrogeological data and tracer test) show the advantages of the COP method in the assessment of vulnerability of karstic groundwaters.     

A Late Pleistocene transgression in Thailand: A marine molluscan fauna from Ban Praksa (Samut Prakan Province)

A Late Pleistocene molluscan fauna sampled at Ban Praksa, near the Chao Phraya River mouth (Lower Central Plain of Bangkok, Thailand) is herein analyzed and paleoecologically characterized, revealing a shallow infralittoral, coarse/hard-bottomed environment. The comparison of the Ban Praksa association with several coeval ones recovered from Phra Pradaeng Formation seems to be evidence of a 10,000 year hiatus between two separate groups of marine faunas, possibly belonging to different interstadial transgressive peaks that occurred during the long-term sea-level regression following the Last Interglacial.     

Comparing predictive capability of statistical and deterministic methods for landslide susceptibility mapping: a case study in the northern Apennines (Reggio Emilia Province, Italy)

Statistical and deterministic methods are widely used in geographic information system based landslide susceptibility mapping. This paper compares the predictive capability of three different models, namely the Weight of Evidence, the Fuzzy Logic and SHALSTAB, for producing shallow earth slide susceptibility maps, to be included as informative layers in land use planning at a local level. The test site is an area of about 450 km² in the northern Apennines of Italy where, in April 2004, rainfall combined with snowmelt triggered hundreds of shallow earth slides that damaged roads and other infrastructure. An inventory of the landslides triggered by the event was obtained from interpretation of aerial photos dating back to May 2004. The pre-existence of mapped landslides was then checked using earlier aerial photo coverage. All the predictive models were run on the same set of geo-environmental causal factors: soil type, soil thickness, land cover, possibility of deep drainage through the bedrock, slope angle, and upslope contributing area. Model performance was assessed using a threshold-independent approach (the ROC plot). Results show that global accuracy is as high as 0.77 for both statistical models, while it is only 0.56 for SHALSTAB. Besides the limited quality of input data over large areas, the relatively poorer performance of the deterministic model maybe also due to the simplified assumptions behind the hydrological component (steady-state slope parallel flow), which can be considered unsuitable for describing the hydrologic behavior of clay slopes, that are widespread in the study area.     

Field based index of flood vulnerability(IFV): A new validation technique for flood susceptible models

The flood hazard management is one of the major challenges in the floodplain regions worldwide. With the rise in population growth and the spread of infrastructural development, the level of risk has increased over time.Therefore, the prediction of flood susceptible area is a key challenge for the adoption of management plans.Flood susceptibility modeling is technically a common work, but it is still a very tough job to validate flood susceptible models in a very rigorous and scientific manner. Therefore, the present work in the Atreyee River Basin of India and Bangladesh was planned to establish artificial neural network(ANN), radial basis function(RBF), random forest(RF) and their ensemble-based flood susceptibility models. The flood susceptible models were constructed based on nine flood conditioning parameters. The flood susceptibility models were validated in a conventional way using the receiver operating curve(ROC). To validate the flood-susceptible models, a two dimensional(2 D) hydraulic flood simulation model was developed. Also, the index of flood vulnerability model was developed and applied for validating the flood susceptible models, which was a very unique way to validate the predictive models. Friedman test and Wilcoxon Signed rank test were employed to compare the generated flood susceptible models. Results showed that 11.95%–12.99% of the entire basin area(10188.4 km2) comes under very high flood-susceptible zones. Accuracy evaluation results have shown that the performance of ensemble flood susceptible models outperforms other standalone machine learning models. The flood simulation model and IFV model were also spatially adjusted with the flood susceptibility models. Therefore, the present study recommended for the ensemble flood susceptibility prediction and IFV based validation along with conventional ways.     

A quantitative approach for improving the BIS (Indian) method of medium-scale landslide susceptibility

In India, the Bureau of Indian Standards (BIS) recommends a heuristic method for medium-scale (1:25,000/1:50,000) landslide susceptibility mapping. This is based on fixed ratings of geofactors, without the inclusion of landslide inventory information. In BIS method, the pre-defined ratings of geofactors are applied over diverse areas, irrespective of the terrain-specific spatial inter-dependence of geofactors and landslide types, which leads to rather moderate prediction. In this paper, we evaluate the effectiveness of the existing BIS method in Darjeeling Himalaya through a quantitative method adapting weights of evidence (WofE) modeling. The quantified spatial associations between specific geofactors for different landslide types and failure mechanisms that were generated, using this method showed improved prediction rates as compared to the BIS method of fixed ratings of geofactors. We therefore recommend adjusting the existing BIS guidelines by inclusions of weights, derived locally through quantitative spatial analysis of landslide inventories and geofactor maps.     

Two-dimensional maps in multi-dimensional worlds: A case of community-based mapping in Northern Thailand

Community-based mapping has become a necessary tool for development work worldwide - its adoption is near hegemonic. Mapping community land, however, can have unforeseen consequences in part due to its tendency to render what are complex configurations of social-ecological relationships into two-dimensional form. I argue that one of the key limitations to commonly practiced community-based mapping is the assumption that the spatial organization of resource use and management is an abstract entity that can be mapped independent of the social relations that produce it. This paper uses a case study from Northern Thailand to show how mapping techniques that fix and simplify fluid and complex associations can inadvertently prescribe changes to how residents manage their land, effectively becoming not only a tool for securing land tenure but also a tool for the spatial re-organization of land-use and management.     

Multi-criteria decision based geospatial mapping of flood susceptibility and temporal hydro-geomorphic changes in the Subarnarekha basin,India

The Subarnarekha River in east India experiences frequent high magnitude flooding in monsoon season.In this study, we present an in-depth analysis of flood hydrology and GIS-based flood susceptibility mapping of the entire catchment. About 40 years of annual peak discharge data, historical cross-sections of different gauging sites, and 12 flood conditioning factors were considered. Our flood susceptibility mapping followed an expert knowledge-based multi-parametric analytical hierarchy process(AHP) and optimized AHP-VIP methods. Peak hydrology data indicated more than 5 times higher discharge contrasted with the mean streamflow of the peak monsoon month in all hydro-monitoring stations that correspond to possible overbank flooding in the shallow semi-alluvial reaches of the Subarnarekha River. Widthdepth ratio revealed continuous changes on the channel cross-sections at decadal scale in all gauging sites. Predicted flood susceptibility map through optimized AHP-VIP method showed a great amount of areas(38%) have a high probability of flooding and demands earnest attention of administrative bodies.The AHP-VIP based flood susceptibility map was theoritically validated through AUC approach and it showed fairly high accuracy(AUC = 0.93). Our study offers an exceptionally cost and time effective solution to the flooding issues in the Subarnarekha basin.     

Landslide susceptibility mapping for Ayvalik (Western Turkey) and its vicinity by multicriteria decision analysis

This paper presents the results of geographical information system (GIS)-based landslide susceptibility mapping in Ayvalık, western Turkey using multi-criteria decision analysis. The methodology followed in the study includes data production, standardization, and analysis stages. A landslide inventory of the study area was compiled from aerial photographs, satellite image interpretations, and detailed field surveys. In total, 45 landslides were recorded and mapped. The areal extent of the landslides is 1.75 km². The identified landslides are mostly shallow-seated, and generally exhibit progressive character. They are mainly classified as rotational, planar, and toppling failures. In all, 51, 45, and 4% of the landslides mapped are rotational, planar, and toppling types, respectively. Morphological, geological, and land-use data were produced using existing topographical and relevant thematic maps in a GIS framework. The considered landslide-conditioning parameters were slope gradient, slope aspect, lithology, weathering state of the rocks, stream power index, topographical wetness index, distance from drainage, lineament density, and land-cover and vegetation density. These landslide parameters were standardized in a common data scale by fuzzy membership functions. Then, the degree to which each parameter contributed to landslides was determined using the analytical hierarchy process method, and the weight values of these parameters were calculated. The weight values obtained were assigned to the corresponding parameters, and then the weighted parameters were combined to produce a landslide susceptibility map. The results obtained from the susceptibility map were evaluated with the landslide location data to assess the reliability of the map. Based on the findings obtained in this study, it was found that 5.19% of the total area was prone to landsliding due to the existence of highly and completely weathered lithologic units and due to the adverse effects of topography and improper land use.     

Evaluation of Sentinel-1 data for flood mapping in the upstream of Sidi Salem dam (Northern Tunisia)

Flood mapping is a powerful asset that allows drawing better strategies to contain possible economic repercussions and to rescue the affected population. This work is directly unfolded after the rainfall events that occurred in the north of the country, in February 2015, during which certain cities located in the vicinity of the Tunisian basin of Medjerda were flooded by the overflow of the Medjerda river, causing important damage to the towns of Jendouba and Bou Salem. The present research illustrates the potentiality of Sentinel-1 sensor in detecting flood areas in the upstream of Medjerda river. The Medjerda is the most important river in Tunisia, with an annual water potential reaching 0.8 billion m³. We compared the signature of flood water in vertical transmit and horizontal received (VH) and vertical transmit and vertical received (VV) polarizations of radar data. The study proves that the segregation of land/water areas with a threshold technique is better observed in VH polarization rather than VV polarization.     

Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: the Melen Gorge (NW Turkey)

In the international literature, although considerable amount of publications on the landslide susceptibility mapping exist, geomorphology as a conditioning factor is still used in limited number of studies. Considering this factor, the purpose of this article paper is to implement the geomorphologic parameters derived by reconstructed topography in landslide susceptibility mapping. According to the method employed in this study, terrain is generalized by the contours passed through the convex slopes of the valleys that were formed by fluvial erosion. Therefore, slope conditions before landsliding can be obtained. The reconstructed morphometric and geomorphologic units are taken into account as a conditioning parameter when assessing landslide susceptibility. Two different data, one of which is obtained from the reconstructed DEM, have been employed to produce two landslide susceptibility maps. The binary logistic regression is used to develop landslide susceptibility maps for the Melen Gorge in the Northwestern part of Turkey. Due to the high correct classification percentages and spatial effectiveness of the maps, the landslide susceptibility map comprised the reconstructed morphometric parameters exhibits a better performance than the other. Five different datasets are selected randomly to apply proper sampling strategy for training. As a consequence of the analyses, the most proper outcomes are obtained from the dataset of the reconstructed topographical parameters and geomorphologic units, and lithological variables that are implemented together. Correct classification percentage and root mean square error (RMSE) values of the validation dataset are calculated as 86.28% and 0.35, respectively. Prediction capacity of the different datasets reveal that the landslide susceptibility map obtained from the reconstructed parameters has a higher prediction capacity than the other. Moreover, the landslide susceptibility map obtained from the reconstructed parameters produces logical results.