Geospatial Approaches to Landslide Susceptibility Mapping: A Study of Rangamati Sadar Upazila, Bangladesh

The study area in northwestern Ethiopia is one of the most landslide-prone regions, which is characterized by frequent high landslide occurrences. To predict future landslide occurrence, preparing a landslide susceptibility mapping is imperative to manage the landslide hazard and reduce damages of properties and loss of lives. Geographic information system (GIS)-based frequency ratio (FR), information value (IV), certainty factor (CF), and logistic regression (LR) methods were applied. The landslide inventory map is prepared from historical records and Google Earth imagery interpretation. Thus, 717 landslides were mapped, of which 502 (70%) landslides were used to build landslide susceptibility models, and the remaining 215 (30%) landslides were used to model validation. Eleven factors such as lithology, land use/cover, distance to drainage, distance to lineament, normalized difference vegetation index, drainage density, rainfall, soil type, slope, aspect, and curvature were evaluated and their relationship with landslide occurrence was analyzed using the GIS tool. Then, landslide susceptibility maps of the study area are categorized into very low, low, moderate, high, and very high susceptibility classes. The four models were validated by the area under the curve (AUC) and landslide density. The results for the AUC are 93.9% for the CF model, which is better than 93.2% using IV, 92.7% using the FR model, and 87.9% using the LR model. Moreover, the statistical significance test between the models was performed using LR analysis by SPSS software. The result showed that the LR and CF models have higher statistical significance than the FR and IV methods. Although all statistical models indicated higher prediction accuracy, based on their statistical significance analysis result (Table 5), the LR model is relatively better followed by the CF model for regional land use planning, landslide hazard mitigation, and prevention purposes.

Database construction for landslide factors (slope, aspect, profile curvature, plan curvature, lithology, land use, distance from lineament & distance from river) and landslide inventory map is an important step in landslide susceptibility modelling. Using the frequency ratio model, the weights for each factor classes were calculated and assigned in GIS so as to add these factors and produce landslide susceptibility index maps based on mathematical combination theory. However, before combining them, their independence among each other should be ascertained. For this, the correlation matrix of logistic regression was applied and this showed that most of the correlations between factors were either absent or very insignificant suggesting that all landslide factors are independent. From a set of eight landslide factors, a total of 247 landslide susceptibility map combinations can be generated. However, for simplification, only 28 landslide susceptibility maps were chosen. Then the best landslide susceptibility map was selected based on high prediction accuracy. But, when there is similarity in the prediction accuracies of different combinations, the landslide susceptibility index difference values can be used as another selection criterion. Hence, the susceptibility map from a combination of all landslide factors except distance from river was found to be the best one. Among the 28 representative combinations, landslide susceptibility maps with the same prediction accuracy of 87.7% have been found in spite of their dissimilarity in their difference values. The combination, with a limited number of landslide factors and the highest prediction accuracy of 87.7%, was found from a combination of slope, lithology, land use and distance from lineament. In order to validate the prediction model, landslides were overlaid over the landslide susceptibility map and the number of landslides that fall into each susceptibility class was calculated. From this analysis 0.39%, 1.84%, 9.1%, 32.04% and 56.63% of the landslides fall in the very low, low, medium, high and very high landslide susceptibility classes respectively. Since 88.67% of the landslides fall in the high and very high susceptibility classes, the landslide susceptibility map can be considered reliable to predict future landslides.

Constitutionality of the Chittagong Hill Tracts Peace Accord 1997: Is the Peace Process Valid?

Intense rainfall events often produce a great number of shallow landslides events, which in many cases can hits large areas or an entire regional territory. These slope instabilities cause damage to many roads, buildings, and infrastructures and often human loss. In these conditions, it is useful to refine shallow landslides susceptibility maps at regional scale progressively more reliable and efficacy. To take the highlighted goal it is opportune to promote the use of a circular approach that can considers knowledge (data, methods, models, solutions, etc.) constantly upgraded. To achieve this aims we propose a method that introduces structurally in a possible circular approach (progressive better results with constantly upgraded knowledge) the use of a comprehensive geo-database of shallow landslide events and related implemented through a collection and analysis of numerous sources, including published inventory maps, scientific literature, technical reports and newspapers, integrated by a multi-temporal interpretation of remote sensing images and several field surveys. The method is applied referring to the Calabria region, which is largely affected by this landslide category. The refined geo-database realized includes 22,028 shallow landslides, occurred between 1951 and 2017. The relationship between spatial pattern of the shallow landslides and the analyzed predisposing factors (lithological units, fault density, land use, drainage density, slope gradient, TWI, SPI and LS) showed that the high values of slope gradient, LS factor and drainage density, coupled to low values of TWI, displayed a strong control on the shallow landslide occurrence. The efficacy of the geo-database realization proves their usefulness in order to estimate and validate shallow landslide susceptibility map, which was optimally obtained applied a simple bivariate statistical method. The susceptibility map was classified into five classes and about 26% of the study area falls in high and very high susceptible classes and most of the shallow landslides mapped (76%) occur in the same classes. The AUC value of the prediction rate curve was 0.81, indicating a good prediction capability of the susceptibility map. The interaction between shallow landslide susceptibility map and road network map highlighted that the 20% of the roadways of the region area falls in high and very high susceptible areas, whereas was observed that the high (58.4%) and very high (65.6%) susceptibility classes are mainly distributed within cover materials from weathered crystalline rocks. The results obtained in this study indicate that the proposed method can concur to promote a circular approach and support with efficacy a progressive refinement of regional shallow landslide susceptibility map, from 2008 to now, that may be useful tool for national and/or local authorities to manage land use and civil protection planning, and for hazard and risk assessment from regional to slope scale.

The Chittagong Hill Tracts (CHT) region in Bangladesh is a conflict-torn area. This paper aims to identify the determinants of entrepreneurship at the household level in the CHT region and contrast these with the determinants of entrepreneurship in the non-conflict mainland region. The analysis is based on the Household Income and Expenditure Survey 2005 data set (N=10,080). Using descriptive statistics and probit regression results, it is found that the probability that a household will own a business in the CHT region is 11 percentlower in comparison to that of a household elsewhere in Bangladesh. Moreover, starting a business in the CHT region requires more capital, and violent conflict and geo-cultural characteristics are particularly discouraging for entrepreneurship.

PurposeThe Sendai framework for disaster risk reduction (DRR) 2015–2030 offers guidelines to reduce disaster losses and further delivers a wake-up call to be conscious of disasters. Its four priorities hinge on science, technology and innovations as critical elements necessary to support the understanding of disasters and the alternatives to countermeasures. However, the changing dynamics of current and new risks highlight the need for existing approaches to keep pace with these changes. This is further relevant as the timeline for the framework enters its mid-point since its inception. Hence, this study reflects on the aspirations of the Sendai framework for DRR through a review of activities conducted in the past years under science, technology and innovations.Design/methodology/approachMultidimensional secondary datasets are collected and reviewed to give a general insight into the DRR activities of governments and other related agencies over the past years with case examples. The results are then discussed in the context of new global risks and technological advancement.FindingsIt becomes evident that GIS and remote sensing embedded technologies are spearheading innovations for DRR across many countries. However, the severity of the Covid-19 pandemic has accelerated innovations that use artificial intelligence-based technologies in diverse ways and has thus become important to risk management. These notwithstanding, the incorporation of science, technology and innovations in DRR faces many challenges. To mitigate some of the challenges, the study proposes reforms to the scope and application of science and technology for DRR, as well as suggests a new framework for risk reduction that harnesses stakeholder collaborations and resource mobilizations.Research limitations/implicationsThe approach and proposals made in this study are made in reference to known workable processes and procedures with proven successes. However, contextual differences may affect the suggested approaches.Originality/valueThe study provides alternatives to risk reduction approaches that hinge on practically tested procedures that harness inclusivity attributes deemed significant to the Sendai framework for DRR 2015–2030.

Uatzau basin in northwestern Ethiopia is one of the most landslide-prone regions, which characterized by frequent high landslide occurrences causing damages in farmlands, non-cultivated lands, properties, and loss of life. Preparing a Landslide susceptibility mapping is imperative to manage the landslide hazard and reduce damages of properties and loss of lives. GIS-based frequency ratio, information value, and certainty factor methods were applied. The landslide inventory map was prepared from detailed fieldwork and Google Earth imagery interpretation. Thus, 514 landslides were mapped, and out of which 359 (70%) of landslides were randomly selected keeping their spatial distribution to build landslide susceptibility models, while the remaining 155 (30%) of the landslides were used to model validation. In this study, six factors, including lithology, land use/cover, distance to stream, slope gradient, slope aspect, and slope curvature were evaluated. The effects of the landslide factor of slope instability were determined by comparing with landslide inventory raster using the GIS environment. The landslide susceptibility maps of the Uatzau area were categorized into very low, low, moderate, high and very high susceptibility classes. The landslide susceptibility maps of the three models validated by the ROC curve. The results for the area under the curve (AUC) are 88.83% for the frequency ratio model, 87.03% for certainty factor, and 84.83% of information value models, which are indicating very good accuracy in the identification of landslide susceptibility zones of a region. From these resulted maps, it is possible to recommend, the statistical methods (Frequency Ratio, Information Value, and Certainty Factor Methods) are adequate to landslide susceptibility mapping. The landslide susceptibility maps can be used for regional land use planning and landslide hazard mitigation purposes.

در این پژوهش با استفاده از زمین لغزش های ثبت‌شده در منطقه و 11 پارامتر طبیعی (سنگ-شناسی، فاصله از گسل، فاصله از رودخانه، شاخص حمل رسوب (STI)، شاخص توان آبراهه (SPI)، بارش، شاخص رطوبت توپوگرافیک (TWI)، درجه شیب، جهت شیب، کاربری زمین و تراکم پوشش گیاهی (NDVI) نقشه حساسیت زمین لغزش برای حوضه سیاهرود استان گیلان تهیه گردیده است. جهت انجام این کار از تئوری بیزین استفاده‌شده است. با استفاده از احتمالات تئوری بیزین ارتباط بین پارامترها و مناطق لغزشی (دو سوم مناطق لغزشی) تعیین شد و وزن هر طبقه از پارامترها به دست آمد. اجرای مدل و اعمال وزن لایه ها با استفاده از نرم افزار Arcmap صورت گرفت و درنهایت نقشه حساسیت زمین‌لغزش در پنج کلاس حساسیت به دست آمد. با توجه به نقشه به‌دست‌آمده و نیز وزن کلاس های هر یک از پارامترها، کلاس تراس های آبرفتی قدیمی و مخروط افکنه های مرتفع در لایه سازند، مرتع متوسط در بین کلاس های کاربری زمین، جهات شمالی و شمال غربی، شیب های 20-5 درجه و نیز فاصله 100-0 متر از رودخانه بیشترین وزن و تأثیر را در وقوع زمین‌لغزش های منطقه دارند. دقت نقشه حساسیت زمین‌لغزش با استفاده از یک سوم (30 نقطه لغزشی) مناطق لغزشی مورد ارزیابی قرار گرفت. نتیجه ارزیابی نشان داد که مدل با قابلیت پیش بینی 3/83 درصد زمین لغزش ها در کلاس خطر زیاد و خیلی زیاد، دقت قابل قبولی در ارزیابی و تهیه نقشه حساسیت زمین‌لغزش دارد.

Landslides pose a significant risk to the Chittagong Hill Tracts (CHT) of Bangladesh, causing severe socio-economic and environmental impacts that threaten the achievement of sustainable land management. In this study, a landslide susceptibility map for the CHT region was created using two machine learning models: Random Forest (RF) and Maximum Entropy (MaxEnt). A total of 15 landslide conditioning factors were considered, including elevation, slope, rainfall, soil texture, and land cover. A landslide inventory dataset comprising 730 landslide events was used for model training and validation. The results indicate that both models successfully classify landslide prone areas, with MaxEnt identifying 79.12% and RF identifying 78% of the study area as high to very high susceptibility zones. Performance evaluation using the Area Under the Curve (AUC) metric revealed that RF (AUC = 0.93) outperformed compared to MaxEnt (AUC = 0.86), demonstrating superior predictive accuracy. RF also exhibited higher overall accuracy (98%) and precision (99%) compared to MaxEnt (87% and 89%, respectively). Maximum rainfall and elevation were the most influential factors in both models for landslide susppectibilty. These findings provide a critical insight into disaster risk management and policy making in the CHT and directly support SDG 11 (Sustainable Cities and Communities) by improving urban resilience, SDG 13 (Climate Action) by improving adaptation strategies and SDG 15 (Life on the Land) by promoting sustainable land management. By integrating scientific modelling into a global sustainability agenda, the study contributes to the development of risk-informed policies and early warning systems to protect vulnerable communities in the CHT region.

An ongoing change in legislation means decision-makers in Aotearoa New Zealand need to incorporate 'mātauranga' (Māori knowledge/knowledge system) in central and local government legislation and strategy. This paper develops a 'te ao Māori' (Māori worldview) disaster risk reduction (DRR) framework for non-Māori decision-makers to guide them through this process. This 'interface framework' will function as a Rosetta Stone between the 'two worlds'. It intends to help central and local officials trained in Western knowledge-based disciplines by translating standard DRR concepts into a te ao Māori DRR framework. It draws on previous work examining Māori DRR thinking to create a novel framework that can help these stakeholders when they are converting higher-level theoretical insights from mātauranga Māori into more practical 'on the ground' applications. This type of interface is essential: while Indigenous knowledge's utility is increasingly recognised nationally and internationally, a gap remains between this acknowledgement and its practical and applied integration into emergency management legislation and strategy.

At the first gathering of its kind on the role of science in implementing the Sendai Framework for Disaster Risk Reduction 2015–2030, over 750 scientists, policymakers, business people, and practitioners met in Geneva from January 27–29, 2016. The UNISDR Science and Technology Conference on the Implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030 featured experts from some of the world’s most disaster-prone countries. The conference brought together a diversity of science and technology community from all geographical regions, international partners and scientific disciplines, and a wide variety of other stakeholders including policymakers and nongovernmental organizations to discuss the barriers and opportunities to reducing disaster risk and loss in the coming 15 years. Attendees contributed to a lively and dynamic debate, including on social media using the hashtag #science4sendai and reporting blogs from many of the organizations represented. The Sendai Framework (UNISDR 2015a), agreed at the Third UN World Conference on Disaster Risk Reduction held in Sendai, Japan in March 2016, was adopted by the UN General Assembly on 3 June 2015. It places unprecedented emphasis on the role of science and technology in disaster risk reduction and calls for a strengthening of networks, platforms, and research institutions, a refocus on research into disaster risk patterns, and examining causes and effects. Its goal is to ‘‘prevent new and reduce existing disaster risk through the implementation of integrated and inclusive economic, structural, legal, social, health, cultural, educational, environmental, technological, political and institutional measures that prevent and reduce hazard exposure and vulnerability to disaster, increase preparedness for response and recovery, and thus strengthen resilience’’ (UNISDR 2015a, Paragraph 17). The organizing committee of the conference, which included members of the UNISDR’s Scientific and Technical Advisory Group (STAG), brought the wide range of stakeholders together to launch the UNISDR Science and Technology Partnership and the UNISDR Science and Technology Road Map to 2030 (UNISDR 2015b). The Road Map presents the expected outcomes under each of the four priorities for action outlined in the Sendai Framework, and proposes key actions that the UNISDR Science and Technology Partnership can undertake to fulfil the expected outcomes and to achieve the goal of the Sendai Framework. It also highlights ways for monitoring progress and reviewing needs. Both the Road Map and the Partnership were supported by the conference participants as promising ways forward to 2030. The conference discussions were arranged into four work streams that looked at opportunities to work & Virginia Murray virginia.murray@phe.gov.uk

Application of data-driven evidential belief functions to landslide susceptibility mapping in Jinbu, Korea

This study investigates the effect of inclusive development policies and initiatives in reducing armed group rivalry and fostering long-term peace in Bangladesh's Chittagong Hill Tracts (CHT) region. The study adopts a qualitative research methodology, utilizing data from key stakeholder interviews, focus group discussions, and document analysis. According to the study's findings, inclusive development policies and programs have the ability to address the underlying reasons of armed group rivalry, boost socioeconomic growth, and generate more social cohesion and reconciliation in the region. Inclusive development techniques that prioritize local communities' needs and ambitions can foster trust and legitimacy among them, increase their feeling of ownership and participation in development processes, and provide an enabling climate for long-term peace. The study also identifies barriers to implementing inclusive development policies and initiatives in the CHT region, such as a lack of political will and commitment, insufficient fund, and low ability and coordination among government agencies and civil society organizations. More political will, coordination, capacity-building, and community participation are required to implement successful development policies and programs that address the core causes of conflict and generate long-term peace in the CHT region.

This research work deals with the landslide susceptibility assessment using Analytic hierarchy process (AHP) and information value (IV) methods along a highway road section in Constantine region, NE Algeria. The landslide inventory map which has a total of 29 single landslide locations was created based on historical information, aerial photo interpretation, remote sensing images, and extensive field surveys. The different landslide influencing geoenvironmental factors considered for this study are lithology, slope gradient, slope aspect, distance from faults, land use, distance from streams, and geotechnical parameters. A thematic layer map is generated for every geoenvironmental factor using Geographic Information System (GIS); the lithological units and the distance from faults maps were extracted from the geological database of the region. The slope gradient, slope aspect, and distance from streams were calculated from the Digital Elevation Model (DEM). Contemporary land use map was derived from satellite images and field study. Concerning the geotechnical parameters maps, they were determined making use of the geotechnical data from laboratory tests. The analysis of the relationships between the landslide-related factors and the landslide events was then carried out in GIS environment. The AUC plot showed that the susceptibility maps had a success rate of 77 and 66% for IV and AHP models, respectively. For that purpose, the IV model is better in predicting the occurrence of landslides than AHP one. Therefore, the information value method could be used as a landslide susceptibility mapping zonation method along other sections of the A1 highway.

The Chittagong Hill Tracts (CHTs) region of Bangladesh, covering a considerable portion of ‘hill forest type’ of the country, is rich in biological diversity; in terms of flora, fauna and ethnicity. A number of aboriginal and tribal communities enrich the cultural heritage of the region. Thanchi upazilla (sub-district) of Bandarban district in the CHTs is the remotest forested area where some tribal groups still lead their subsistence life depending fully on natural resources. This exploratory study was conducted to document indigenous knowledge (IK) employed by the Mro tribe in their everyday activities, highlighting traditional utilization of forests and other natural resources. A total of 36 farms were assessed using different participatory appraisals through semi-structured questionnaire. The respondents were peasants who live on the hilltops in a pristine environment, inside the high ranges of hills and dense forest almost totally beyond the eye-sight of the outer civilized society. They developed IK of their own in practising shifting cultivation (Jhum) and other land use systems along with the utilization of natural resources. In most cases, such IK has become key factors in the sound management of their forest resources with sustainable utilization of biodiversity. But most of the wealth of their IK is being threatened by the settlement of the non-tribal people in the CHTs region. The life style and ethno-forestry perception regulated by IK governing the daily activities of the ethnic communities need to be explored in order to conserve them and to assess the possibilities for conserving the forest resources by utilizing such traditional indigenous concepts.