Integrating expert knowledge with statistical analysis for landslide susceptibility assessment at regional scale

Abstract: In this paper, an integration landslide susceptibility model by combining expert-based and bivariate statistical analysis (Landslide Susceptibility Index—LSI) approaches is presented. Factors related with the occurrence of landslides—such as elevation, slope angle, slope aspect, lithology, land cover, Mean Annual Precipitation (MAP) and Peak Ground Acceleration (PGA)—were analyzed within a GIS environment. This integrated model produced a landslide susceptibility map which categorized the study area according to the probability level of landslide occurrence. The accuracy of the final map was evaluated by Receiver Operating Characteristics (ROC) analysis depending on an independent (validation) dataset of landslide events. The prediction ability was found to be 76% revealing that the integration of statistical analysis with human expertise can provide an acceptable landslide susceptibility assessment at regional scale

Geo-spatial Technology for Landslide Hazard Zonation and Prediction

Similar to other geo hazards, landslides cannot be avoided in mountainous terrain. It is the most common natural hazard in the mountain regions and can result in enormous damage to both property and life every year. Better understanding of the hazard will help people to live in harmony with the pristine nature. Since India has 15% of its land area prone to landslides, preparation of landslide susceptibility zonation (LSZ) maps for these areas is of utmost importance. These susceptibility zonation maps will give the areas that are prone to landslides and the safe areas, which in-turn help the administrators for safer planning and future development activities. There are various methods for the preparation of LSZ maps such as based on Fuzzy logic, Artificial Neural Network, Discriminant Analysis, Direct Mapping, Regression Analysis, Neuro-Fuzzy approach and other techniques. These different approaches apply different rating system and the weights, which are area and factors dependent. Therefore, these weights and ratings play a vital role in the preparation of susceptibility maps using any of the approach. However, one technique that gives very high accuracy in certain might not be applicable to other parts of the world due to change in various factors, weights and ratings. Hence, only one method cannot be suggested to be applied in any other terrain. Therefore, an understanding of these approaches, factors and weights needs to be enhanced so that their execution in Geographic Information System (GIS) environment could give better results and yield actual ground like scenarios for landslide susceptibility mapping. Hence, the available and applicable approaches are discussed in this chapter along with detailed account of the literature survey in the areas of LSZ mapping. Also a case study of Garhwal area where Support Vector Machine (SVM) technique is used for preparing LSZ is also given. These LSZ maps will also be an important input for preparing the risk assessment of LSZ

Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya

Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling-Narayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75 %) were randomly selected for building landslide susceptibility models, while the remaining 80 (25 %) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16 %. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57 % of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80 % accuracy (i.e. 89.15 % for IOE model, 89.10 % for LR model and 87.21 % for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling-Narayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.ArticleNATURAL HAZARDS. 65(1):135-165 (2013)journal articl

Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh

Landslides are a common hazard in the highly urbanized hilly areas in Chittagong Metropolitan Area (CMA), Bangladesh. The main cause of the landslides is torrential rain in short period of time. This area experiences several landslides each year, resulting in casualties, property damage, and economic loss. Therefore, the primary objective of this research is to produce the Landslide Susceptibility Maps for CMA so that appropriate landslide disaster risk reduction strategies can be developed. In this research, three different Geographic Information System-based Multi-Criteria Decision Analysis methods—the Artificial Hierarchy Process (AHP), Weighted Linear Combination (WLC), and Ordered Weighted Average (OWA)—were applied to scientifically assess the landslide susceptible areas in CMA. Nine different thematic layers or landslide causative factors were considered. Then, seven different landslide susceptible scenarios were generated based on the three weighted overlay techniques. Later, the performances of the methods were validated using the area under the relative operating characteristic curves. The accuracies of the landslide susceptibility maps produced by the AHP, WLC_1, WLC_2, WLC_3, OWA_1, OWA_2, and OWA_3 methods were found as 89.80, 83.90, 91.10, 88.50, 90.40, 95.10, and 87.10 %, respectively. The verification results showed satisfactory agreement between the susceptibility maps produced and the existing data on the 20 historical landslide locations

Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India

CRediT authorship contribution statement: Dr. Aman Arora and Dr. Alireza Arabameri have conceptualized the study, prepared the dataset, and optimized the models. Dr. Manish Pandey has helped in writing the manuscript. Prof. Masood A. Siddiqui, Prof. U.K. Shukla, Prof. Dieu Tien Bui, Dr. Varun Narayan Mishra, and Dr. Anshuman Bhardwaj have helped in improving the manuscript at different stages of this work.Peer reviewedPostprin

Procjena planiranja mreža šumskih putova u osjetljivim klizištima pomoću GIS baziranih multikriterijskih pristupa odlučivanju na Ihsangazi vododjelnici, Sjeverna Turska

Forest roads are one of the fundamental infrastructures in carrying out forestry activities and services. According to FAO, approximately 20 percent of the world’s forest lands are covered mountain forests. Since forests are generally located also in mountainous areas with steep slope in Turkey, difficulties experienced in these mountainous conditions render the provision of services difficult while increasing the costs. The aim of this study is to evaluate forest road planning alternatives which are to be developed in landslide sensitive mountainous areas based on the Landslide Susceptibility Mapping (LSM). For this purpose, a total of 12 models were generated with different multi-criteria decision making (MCDM) approaches including Modified Analytical Hierarchy Process (M-AHP), Fuzzy Inference System (FIS), and Logistic Regression (LR). As a result of the study, the best model was Model 3 obtained with LR approach (area under the curve (AUC)=76.6%) value followed by LR-Model 4 (AUC=75.7%) and FIS-Model 4 (AUC=73.4%). Model 3 (AUC=71%) was the most successful M-AHP approach. Consequently, the application of these methods will provide an advantage in making more accurate and more rational decisions during road network planning in landslide sensitive forest areas.Šumske ceste jedna su od temeljnih infrastruktura u obavljanju šumarskih djelatnosti i usluga. Budući da su šume općenito smještene u planinskim područjima sa strmim nagibom u Turskoj, teškoće koje se događaju u ovim planinskim uvjetima povećavaju troškove. Cilj ove studije je procijeniti alternative planiranja šumskih cesta koje će se razvijati u planinskim područjima koja se nalaze na osjetljivim klizištima, na  temelju mapiranja mapa osjetljivosti na terenu (LSM). U tu svrhu generirano je ukupno 12 modela s različitim pristupima višestrukog odlučivanja (MCDM), uključujući Modificirani analitički hijerarhijski proces (M-AHP), Fuzz sustav (FIS) i logističku regresiju (LR). Kao rezultat studije, najbolji model bio je Model 3 dobiven uz LR pristup (područje ispod krivulje (AUC) = 76,6%), a zatim LR-Model 4 (AUC = 75,7%) i FIS-Model 4 (AUC = 73.4%). Model 3 (AUC = 71%) bio je najuspješniji M-AHP pristup. Slijedom toga, primjena ovih metoda pružit će prednost u donošenju točnijih i racionalnih odluka tijekom planiranja cestovne mreže u osjetljivim šumskim područjima

Procjena podložnosti bujučnim poplavama - studija slučaja - sliv rijeke Ukrine (BiH)

Torrential floods are the most frequent natural catastrophic events in the Republic of Srpska (B&H). The main objective of this study is susceptibility assessment to torrential floods in Ukrina River Basin using Index Based Method (IBM) and Flash Flood Potential Method (FFPI), which operates entirely in a GIS environment. The definition and identification of influencing factors for torrential floods was the first step in the process of developing the Torrential Flood Susceptibility Model (TFSM). According to the results of these models, 54.00% and 40.86% of the Ukrina Basin area is in the categories of strong and very strong susceptibility to torrential floods. The second task was to identify the torrential basins and create the Register and the Cadastre of Torrential Basins in the Ukrina River Basin. After detailed field survey and analyses, 154 torrential basins have been identified, occupying 551.37 km 2 of the Ukrina Basin. According to the validation indicators of the Torrential Flood Susceptibility Model, 138 torrential basins are in the category of strong and very strong susceptibility according to Index Based Method, while 112 torrential basins are in the same category of susceptibility according to Flash Flood Potential Index Method, which are very good results of the validation. This paper presents the significant step towards better understanding of the phenomenon of torrential floods in the Republic of Srpska (B&H). The data presented in this paper are also significant to practical issues such as integral water management projects, spatial planning, sustainable land planning and protection of soil, forest ecosystems and environmental protection, sediment management, agriculture and other human activities.Bujične poplave su jedne od najčešćih prirodnih katastrofa koje su zastupljene u Republici Srpskoj, odnosno u Bosni i Hercegovini. Glavni cilj ovog istraživanja je bila procjena podložnostiodređenih prostora na pojavu i razvoj bujičnih poplava u slivu rijeke Ukrine. U radu su korišćene Indeksno bazirana metoda (IBM) i "Flash Flood"potencijal metoda (FFPI), koje seu potpunosti sprovode u GIS okruženju. Prvi korak u izradi modela podložnostina pojavu i razvoj bujičnih poplava (TFSM) bio je definisanje i identifikovanje faktora koji utiču na njihovo pojavljivanje. Na osnovu dobijenih rezultata obe korišćene metode, oko 54% (IBM), odnosno 41% (FFPI) površine sliva rijeke Ukrine spada u kategorije jake i veoma jake podložnostina pojavu i razvoj bujičnih poplava (slivova). Drugi važan zadatak je bio identifikovanje bujičnih vodotoka i njima pripadajućih slivova i kreiranje registra i katastra bujičnih vodotoka u slivu rijeke Ukrine. Nakon detaljno sprovedenih terenskih istraživanja i analize prikupljenih podataka, izdvojeno je 154 bujičnih slivova koji se prostiru na površini od 551,37 km2, što čini 36,79% sliva rijeke Ukrine. Prema pokazateljima validacije dobijenih modela podložnostina pojavu i razvoj bujičnih poplava, 138 (90%) bujičnih slivova spada u kategoriju jakei veoma jakeosetljivosti prema IBM metodi, dok je prema FFPI metodi 112 (73%) bujičnih slivova u istoj kategoriji. Ovaj rad predstavlja značajan iskorak ka boljem razumijevanju nastanka bujičnih poplava u Republici Srpskoj (Bosni i Hercegovini). Rezultati predstavljeni u ovom radu veoma su značajni za mnoga praktična pitanja, poput projekata integralnog upravljanja vodnim resursima, prostornog planiranja, održivog planiranja korišćenja zemljišta i zaštite tla, šumskih ekosistema i zaštite životne sredine, upravljanja riječnim nanosom, poljoprivredne proizvodnje i drugih ljudskih aktivnosti