Evacuation shelter suitability modeling under combined geo-hydrological hazards in Western Ghat region, India

2021 ◽  
Author(s):  
Somnath Bera ◽  
Kaushal Raj Gnyawali
Keyword(s):  
Random Forest ◽  
Entropy Method ◽  
Western Ghat ◽  
The Road ◽  
Flood Susceptibility ◽  
Landslide Hazards ◽  
Susceptibility Maps ◽  
Cluster Area ◽  
Hydrological Hazards ◽  
Market Distance

<p>Geo-hydrological hazards like floods and landslides are common in mountain regions. During a disaster, evacuation shelters become a primary need of people. We develop a model to find suitable locations for emergency shelters in flood and landslide strikes in a rural mountain setting of the Western Ghat region, India. Firstly, susceptibility maps for flood and landslide hazards are prepared using a machine learning (Random forest) algorithm. Then location suitability modeling is done in GIS using the entropy method. The following entropy evolution factors are considered- flood susceptibility, landslide susceptibility, land use, distance from the road, distance from the hospital, distance from the market, distance from the fire station, distance from safe water sources, and the population of settlement cluster area. Model constraining factors like steep slope, high landslide, flood susceptible area, and protected area are accounted for using a cost matrix. The model is compared with community-based suitability mapping and evacuation centers during the past disaster of 2005. The study will contribute towards better disaster-resilient planning of rural mountainous settlements.</p><p><strong>Keywords</strong>: Evacuation shelter, landslide, flood, random forest, entropy method, GIS</p>

A Comparative Assessment of Multi-criteria Decision Analysis for Flood Susceptibility Modeling 

2021 ◽  
Author(s):  
Ehsan Shahiri Tabarestani ◽  
Hossein Afzalimehr
Keyword(s):  
Characteristic Curve ◽  
Drainage Density ◽  
Weight Of Evidence ◽  
Mazandaran Province ◽  
Conditioning Factors ◽  
Flood Susceptibility ◽  
Susceptibility Modeling ◽  
Susceptibility Maps ◽  
Reliable Model ◽  
First Time

Abstract Floods are one of the most damaging natural disasters throughout the world. The purpose of this study is to develop a reliable model for identification of flood susceptible areas. Three Multi-criteria decision-making techniques, namely Analytical Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Attributive Border Approximation Area Comparison (MABAC) methods combined with weight of evidence (WOE) were used in Mazandaran Province, Iran. MABAC method is applied to determine the flood susceptibility in this study, for the first time. At first, 160 flood locations were identified in the study area, of which 112 (70%) locations were selected randomly for modeling, and the remaining 48 (30%) locations were used for validation. Using Geographic Information System (GIS) with eight conditioning factors including rainfall, distance from rivers, slope, soil, geology, elevation, drainage density, and land use, the flood susceptibility maps were prepared. The results showed that the area under receiver operating characteristic curve (AUROC) for the test data of AHP-WOE, TOPSIS-WOE-AHP, and MABAC-WOE-AHP methods were 75.3%, 91.6%, and 86.1%, respectively, which indicate the reasonable accuracy of models. High accuracy of the proposed new model (MABAC) clarifies its applicability for preventive measures.

Fractal analysis of rain gauges’ inhomogeneous distribution and the associated hydrological impacts: the case study of Muriaé River basin

2021 ◽  
Author(s):  
Priscila Celebrini de O. Campos ◽  
Igor Paz ◽  
Maria Esther Soares Marques ◽  
Ioulia Tchiguirinskaia ◽  
Daniel Schertzer
Keyword(s):  
Fractal Dimension ◽  
Spatial Analysis ◽  
Fractal Analysis ◽  
Probability Of Detection ◽  
Susceptibility Map ◽  
Rain Gauges ◽  
Flood Susceptibility ◽  
Susceptibility Maps ◽  
Cross Tabulation ◽  
The Cross

<p>The urban population growth requires an improvement in the resilient behavior of these areas to extreme weather events, especially heavy rainfall. In this context, well-developed urban planning should address the problems of infrastructure, sanitation, and installation of communities, primarily related to insufficiently gauged locations. The main objectives of this study were to analyze the impacts of in-situ rain gauges’ distribution associated with the elaboration of a spatial diagnosis of the occurrence of floods in the municipality of Itaperuna, Rio de Janeiro – Brazil. The methodology consisted of the spatial analysis of rain gauges’ distribution with the help of the fractal dimension concept and investigation of flood susceptibility maps prepared by the municipality based on transitory factors (which consider precipitation in the modeling) and on permanent factors (natural flood susceptibility). Both maps were validated by the cross-tabulation method, crossing each predictive map with the recorded data of flood spots measured during a major rainfall event. The results pointed that the fractal analysis of the rain gauges’ distribution presented a scaling break behavior with a low fractal dimension at the small-scale range, mostly concerned in (semi-)urban catchments, highlighting the incapacity of the local instrumentation to capture the spatial rainfall variability. Thereafter, the cross-tabulation validation method indicated that the flood susceptibility map based on transitory factors presented an unsatisfactory probability of detection of floods when compared to the map based on permanent factors. These results allowed us to take into account the hydrological uncertainties concerning the insufficient gauge network and the impacts of the sparse distribution on the choice and elaboration of flood susceptibility maps that use rainfall data as input. Finally, we performed a spatial analysis to estimate the population and habitations that can be affected by floods using the flood susceptibility map based on permanent factors.</p>

scholarly journals Flash Flood Susceptibility Assessment Based on Geodetector, Certainty Factor, and Logistic Regression Analyses in Fujian Province, China

2020 ◽  
Vol 9 (12) ◽  
pp. 748
Author(s):  
Yifan Cao ◽  
Hongliang Jia ◽  
Junnan Xiong ◽  
Weiming Cheng ◽  
Kun Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Flash Flood ◽  
Pearson Correlation ◽  
Flash Floods ◽  
Ensemble Model ◽  
Susceptibility Assessment ◽  
Certainty Factor ◽  
Fujian Province ◽  
Flood Susceptibility ◽  
Susceptibility Maps

Flash floods are one of the most frequent natural disasters in Fujian Province, China, and they seriously threaten the safety of infrastructure, natural ecosystems, and human life. Thus, recognition of possible flash flood locations and exploitation of more precise flash flood susceptibility maps are crucial to appropriate flash flood management in Fujian. Based on this objective, in this study, we developed a new method of flash flood susceptibility assessment. First, we utilized double standards, including the Pearson correlation coefficient (PCC) and Geodetector to screen the assessment indicator. Second, in order to consider the weight of each classification of indicator and the weights of the indicators simultaneously, we used the ensemble model of the certainty factor (CF) and logistic regression (LR) to establish a frame for the flash flood susceptibility assessment. Ultimately, we used this ensemble model (CF-LR), the standalone CF model, and the standalone LR model to prepare flash flood susceptibility maps for Fujian Province and compared their prediction performance. The results revealed the following. (1) Land use, topographic relief, and 24 h precipitation (H24_100) within a 100-year return period were the three main factors causing flash floods in Fujian Province. (2) The area under the curve (AUC) results showed that the CF-LR model had the best precision in terms of both the success rate (0.860) and the prediction rate (0.882). (3) The assessment results of all three models showed that between 22.27% and 29.35% of the study area have high and very high susceptibility levels, and these areas are mainly located in the east, south, and southeast coastal areas, and the north and west low mountain areas. The results of this study provide a scientific basis and support for flash flood prevention in Fujian Province. The proposed susceptibility assessment framework may also be helpful for other natural disaster susceptibility analyses.

scholarly journals Dynamics and conceptual model of the Rossena castle landslide (Northern Apennines, Italy)

2005 ◽  
Vol 5 (6) ◽  
pp. 903-909 ◽  
Author(s):  
A. Chelli ◽  
G. Mandrone ◽  
A. Ruffini ◽  
G. Truffelli
Keyword(s):  
Middle Ages ◽  
Northern Apennines ◽  
Point Of View ◽  
Historical Event ◽  
The Road ◽  
Landslide Hazards ◽  
Historical Monuments ◽  
Henry Iv ◽  
Emilia Romagna Region ◽  
The Village

Abstract. In the Northern Apennines there are many historical villages and castles, which are of great value and represent a cultural heritage of great importance. Their presence within a territory greatly affected by landslide hazards creates, in many circumstances, the need to solve problems of land management and to act for the preservation of historical monuments. This paper describe an interesting landslide, failed during the night of 28 February 2004, that involved the village of Rossena: the failure damaged the village (Fig. 1), the road and the fields down to the stream but, fortunately, the castle just upslope the village was not involved at all. The 10th century massive castle of Rossena stands on the top of a cliff at about 500 m a.s.l., on the border between the provinces of Parma and Reggio Emilia, and it is surrounded by a small ancient village. The castle of Rossena is the best preserved stronghold of the Longobard times, enlarged and reinforced in the tenth century and partially rebuilt by Bonifacio, the father of Matilda of Canossa (the Vice-Queen of Italy and probably the most important woman in the Middle Ages) as a defensive structure guarding the Enza Valley. In addition, at Conossa, very close to Rossena, there was the meeting between Pope Gregory VII and the Emperor of Germany Henry IV, during the historical event known as "fight for the investitures". For these reasons, the area of Rossena is one of the most relevant from a historical point of view in the entire western part of the Emilia Romagna Region and it also has a high value as a geosite (Coratza et al., 2004).  

scholarly journals Novel Entropy and Rotation Forest-Based Credal Decision Tree Classifier for Landslide Susceptibility Modeling

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 106 ◽  
Author(s):  
Qingfeng He ◽  
Zhihao Xu ◽  
Shaojun Li ◽  
Renwei Li ◽  
Shuai Zhang ◽  
...  
Keyword(s):  
Decision Tree ◽  
Landslide Susceptibility ◽  
Entropy Method ◽  
Decision Tree Classifier ◽  
Rotation Forest ◽  
Conditioning Factors ◽  
Susceptibility Modeling ◽  
Landslide Hazards ◽  
Tree Classifier ◽  
Hybrid Intelligence

Landslides are a major geological hazard worldwide. Landslide susceptibility assessments are useful to mitigate human casualties, loss of property, and damage to natural resources, ecosystems, and infrastructures. This study aims to evaluate landslide susceptibility using a novel hybrid intelligence approach with the rotation forest-based credal decision tree (RF-CDT) classifier. First, 152 landslide locations and 15 landslide conditioning factors were collected from the study area. Then, these conditioning factors were assigned values using an entropy method and subsequently optimized using correlation attribute evaluation (CAE). Finally, the performance of the proposed hybrid model was validated using the receiver operating characteristic (ROC) curve and compared with two well-known ensemble models, bagging (bag-CDT) and MultiBoostAB (MB-CDT). Results show that the proposed RF-CDT model had better performance than the single CDT model and hybrid bag-CDT and MB-CDT models. The findings in the present study overall confirm that a combination of the meta model with a decision tree classifier could enhance the prediction power of the single landslide model. The resulting susceptibility maps could be effective for enforcement of land management regulations to reduce landslide hazards in the study area and other similar areas in the world.

scholarly journals Assessing the susceptibility of schools to flood events in Iran

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Saleh Yousefi ◽  
Hamid Reza Pourghasemi ◽  
Sayed Naeim Emami ◽  
Omid Rahmati ◽  
Shahla Tavangar ◽  
...  
Keyword(s):  
Flood Risk ◽  
Flood Hazard ◽  
Rural Students ◽  
Flood Hazards ◽  
Analytic Hierarchy ◽  
Flood Susceptibility ◽  
Susceptibility Maps ◽  
High Flood ◽  
Analytic Hierarchy Process Method ◽  
Very High

Abstract Catastrophic floods cause deaths, injuries, and property damages in communities around the world. The losses can be worse among those who are more vulnerable to exposure and this can be enhanced by communities’ vulnerabilities. People in undeveloped and developing countries, like Iran, are more vulnerable and may be more exposed to flood hazards. In this study we investigate the vulnerabilities of 1622 schools to flood hazard in Chaharmahal and Bakhtiari Province, Iran. We used four machine learning models to produce flood susceptibility maps. The analytic hierarchy process method was enhanced with distance from schools to create a school-focused flood-risk map. The results indicate that 492 rural schools and 147 urban schools are in very high-risk locations. Furthermore, 54% of rural students and 8% of urban students study schools in locations of very high flood risk. The situation should be examined very closely and mitigating actions are urgently needed.

scholarly journals The Idea of Using Bayesian Networks in Forecasting Impact of Traffic-Induced Vibrations Transmitted through the Ground on Residential Buildings

Geosciences ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. 339 ◽  
Author(s):  
Agata Siemaszko ◽  
Anna Jakubczyk-Gałczyńska ◽  
Robert Jankowski
Keyword(s):  
Bayesian Networks ◽  
Residential Buildings ◽  
Surface Condition ◽  
Experimental Tests ◽  
Entropy Method ◽  
Information Value ◽  
Decision Support Model ◽  
The Road ◽  
Specialized Equipment ◽  
The Impact

Traffic–induced vibrations may constitute a considerable load to buildings. In this paper, vibrations transmitted through the ground caused by wheeled vehicles are considered. This phenomenon may cause cracking of plaster, cracks in load-bearing elements or even, in extreme cases, collapse of the whole structure. Measurements of vibrations of real structures are costly and laborious. Therefore, the aim of the present paper is to propose a method of using Bayesian networks combined with implementation of geoscience for assessment of impact of traffic–induced vibrations on residential buildings. Firstly, the experimental tests were performed on different buildings using specialized equipment taking into account five factors: Distance from the building to the edge of the road, condition of road surface, condition of the building, the absorption of soil and the type of vehicle. Then, probabilistic analyses applying Bayesian networks were conducted and two methods of assessing the information value (EVSI method and entropy method) were compared. Finally, the developed diagnostic–decision support model was tested, so as to verify the most important parameter, affecting the possibility of structural vibrations to occur. The results of the study clearly showed that the use of Bayesian networks was a very effective approach to assess the impact of traffic-induced vibrations. The developed algorithm could be successfully applied both to existing and planned buildings, for which the source of vibration is already present or may appear in the future.

scholarly journals Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms

2020 ◽  
Vol 12 (21) ◽  
pp. 3568
Author(s):  
Shahab S. Band ◽  
Saeid Janizadeh ◽  
Subodh Chandra Pal ◽  
Asish Saha ◽  
Rabin Chakrabortty ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Flash Flood ◽  
Flood Hazard ◽  
Machine Learning Algorithms ◽  
Mitigation Strategies ◽  
Boosted Regression Tree ◽  
Flash Flooding ◽  
Flood Susceptibility ◽  
Susceptibility Modeling

Flash flooding is considered one of the most dynamic natural disasters for which measures need to be taken to minimize economic damages, adverse effects, and consequences by mapping flood susceptibility. Identifying areas prone to flash flooding is a crucial step in flash flood hazard management. In the present study, the Kalvan watershed in Markazi Province, Iran, was chosen to evaluate the flash flood susceptibility modeling. Thus, to detect flash flood-prone zones in this study area, five machine learning (ML) algorithms were tested. These included boosted regression tree (BRT), random forest (RF), parallel random forest (PRF), regularized random forest (RRF), and extremely randomized trees (ERT). Fifteen climatic and geo-environmental variables were used as inputs of the flash flood susceptibility models. The results showed that ERT was the most optimal model with an area under curve (AUC) value of 0.82. The rest of the models’ AUC values, i.e., RRF, PRF, RF, and BRT, were 0.80, 0.79, 0.78, and 0.75, respectively. In the ERT model, the areal coverage for very high to moderate flash flood susceptible area was 582.56 km2 (28.33%), and the rest of the portion was associated with very low to low susceptibility zones. It is concluded that topographical and hydrological parameters, e.g., altitude, slope, rainfall, and the river’s distance, were the most effective parameters. The results of this study will play a vital role in the planning and implementation of flood mitigation strategies in the region.

scholarly journals 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

2019 ◽  
Vol 11 (13) ◽  
pp. 1589 ◽  
Author(s):  
Duie Tien Bui ◽  
Khabat Khosravi ◽  
Himan Shahabi ◽  
Prasad Daggupati ◽  
Jan F. Adamowski ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Vegetation Index ◽  
Slope Angle ◽  
Training Data ◽  
Multivariate Logistic Regression Model ◽  
Success Rates ◽  
Topographic Wetness Index ◽  
Validation Data ◽  
Flood Susceptibility ◽  
Susceptibility Maps

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.

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