Landslide susceptibility mapping using machine learning: A literature survey

Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented.Web of Science1413art. no. 302

Assessment of landslide susceptibility using statistical- and artificial intelligence-based FR-RF integrated model and multiresolution DEMs

© 2019 by the authors. Landslide is one of the most important geomorphological hazards that cause significant ecological and economic losses and results in billions of dollars in financial losses and thousands of casualties per year. The occurrence of landslide in northern Iran (Alborz Mountain Belt) is often due to the geological and climatic conditions and tectonic and human activities. To reduce or control the damage caused by landslides, landslide susceptibility mapping (LSM) and landslide risk assessment are necessary. In this study, the efficiency and integration of frequency ratio (FR) and random forest (RF) in statistical- and artificial intelligence-based models and different digital elevation models (DEMs) with various spatial resolutions were assessed in the field of LSM. The experiment was performed in Sangtarashan watershed, Mazandran Province, Iran. The study area, which extends to 1072.28 km2, is severely affected by landslides, which cause severe economic and ecological losses. An inventory of 129 landslides that occurred in the study area was prepared using various resources, such as historical landslide records, the interpretation of aerial photos and Google Earth images, and extensive field surveys. The inventory was split into training and test sets, which include 70 and 30% of the landslide locations, respectively. Subsequently, 15 topographic, hydrologic, geologic, and environmental landslide conditioning factors were selected as predictor variables of landslide occurrence on the basis of literature review, field works and multicollinearity analysis. Phased array type L-band synthetic aperture radar (PALSAR), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), and SRTM (Shuttle Radar Topography Mission) DEMs were used to extract topographic and hydrologic attributes. The RF model showed that land use/land cover (16.95), normalised difference vegetation index (16.44), distance to road (15.32) and elevation (13.6) were the most important controlling variables. Assessment of model performance by calculating the area under the receiving operating characteristic curve parameter showed that FR-RF integrated model (0.917) achieved higher predictive accuracy than the individual FR (0.865) and RF (0.840) models. Comparison of PALSAR, ASTER, and SRTM DEMs with 12.5, 30 and 90 m spatial resolution, respectively, with the FR-RF integrated model showed that the prediction accuracy of FR-RF-PALSAR (0.917) was higher than FR-RF-ASTER (0.865) and FR-RF-SRTM (0.863). The results of this study could be used by local planners and decision makers for planning development projects and landslide hazard mitigation measures

Machine Learning Predicts Reach-Scale Channel Types From Coarse-Scale Geospatial Data in a Large River Basin

Hydrologic and geomorphic classifications have gained traction in response to the increasing need for basin-wide water resources management. Regardless of the selected classification scheme, an open scientific challenge is how to extend information from limited field sites to classify tens of thousands to millions of channel reaches across a basin. To address this spatial scaling challenge, this study leverages machine learning to predict reach-scale geomorphic channel types using publicly available geospatial data. A bottom-up machine learning approach selects the most accurate and stable model among∼20,000 combinations of 287 coarse geospatial predictors, preprocessing methods, and algorithms in a three-tiered framework to (i) define a tractable problem and reduce predictor noise, (ii) assess model performance in statistical learning, and (iii) assess model performance in prediction. This study also addresses key issues related to the design, interpretation, and diagnosis of machine learning models in hydrologic sciences. In an application to the Sacramento River basin (California, USA), the developed framework selects a Random Forest model to predict 10 channel types previously determined from 290 field surveys over 108,943 two hundred-meter reaches. Performance in statistical learning is reasonable with a 61% median cross-validation accuracy, a sixfold increase over the 10% accuracy of the baseline random model, and the predictions coherently capture the large-scale geomorphic organization of the landscape. Interestingly, in the study area, the persistent roughness of the topography partially controls channel types and the variation in the entropy-based predictive performance is explained by imperfect training information and scale mismatch between labels and predictors

Landslide Identification and Zonation Using the Index of Entropy Technique at Ossey Watershed Area in Bhutan

The landslide is one of the natural disasters which claim human lives and incur huge economic losses, especially in the mountainous area. The main aim of this study is to develop different zones of landslide-prone area using the index of entropy (IOE) at the Ossey watershed area in Bhutan. During the landslide inventory, 164 landslides were identified of which 115 locations were used for the training dataset while the remaining 49 locations were used for the validation dataset. A total of ten causal factors were used for this study including elevation, slope, aspect, slope curvature, stream power index, normalized difference vegetation index (NDVI), distance from the road, distance from the river, lithology, and rainfall. The IOE was used to obtain the relationship between the landslide events and the causal factors. The most influential causal factors were NDVI, slope, and rainfall with the weightage of 0.377, 0.347, and 0.175 respectively as per the IOE. The final landslide susceptibility map was classified into five classes using the geometrical interval classification. The validation was done using the receiver operating characteristic (ROC) curves and the kappa index. The area under the curve (AUC) for the success rate and prediction rate was 0.7821 and 0.8377, respectively. The kappa index using the training dataset and validation dataset were 0.4111 and 0.4898, respectively. The final landslide susceptibility map is accurate enough for the future references by the decision-makers and the engineers

Geo-Information Technology and Its Applications

Geo-information technology has been playing an ever more important role in environmental monitoring, land resource quantification and mapping, geo-disaster damage and risk assessment, urban planning and smart city development. This book focuses on the fundamental and applied research in these domains, aiming to promote exchanges and communications, share the research outcomes of scientists worldwide and to put these achievements better social use. This Special Issue collects fourteen high-quality research papers and is expected to provide a useful reference and technical support for graduate students, scientists, civil engineers and experts of governments to valorize scientific research

Fractal assessment analysis of China's air-HSR network integration

High-speed rail (HSR) has emerged as a significant mode for intercity transport in several countries, particularly China, setting an environment that may promote integration between air and HSR networks. To better measure the current level of integration of China's air-HSR intermodal network and identify implementation issues, this paper establishes a novel assessment framework that considers three primary areas: service capability, network connectivity and transfer potential. The framework is based on a comprehensive literature review of network measurement and assessment methodologies. Then, fractal theory is used to establish an assessment model that associates the fractal dimension to the level of intermodal integration, which can serve as an important complement to traditional weighting methods. The model and framework are applied to the 10 cities in China with the potential for air-HSR integration. The results show that international hub airports, together with their closest HSR station, do not necessarily perform at a higher integration level than regional hubs. The paper also proposes policy and practical recommendations to enhance air-HSR network integration levels from service supply, network coordination and transfer design perspectives

Soil-Water Conservation, Erosion, and Landslide

The predicted climate change is likely to cause extreme storm events and, subsequently, catastrophic disasters, including soil erosion, debris and landslide formation, loss of life, etc. In the decade from 1976, natural disasters affected less than a billion lives. These numbers have surged in the last decade alone. It is said that natural disasters have affected over 3 billion lives, killed on average 750,000 people, and cost more than 600 billion US dollars. Of these numbers, a greater proportion are due to sediment-related disasters, and these numbers are an indication of the amount of work still to be done in the field of soil erosion, conservation, and landslides. Scientists, engineers, and planners are all under immense pressure to develop and improve existing scientific tools to model erosion and landslides and, in the process, better conserve the soil. Therefore, the purpose of this Special Issue is to improve our knowledge on the processes and mechanics of soil erosion and landslides. In turn, these will be crucial in developing the right tools and models for soil and water conservation, disaster mitigation, and early warning systems

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things

Lur labainketen analisirako hurbilketa metodologikoa eskala erregionalean: Datuen bilketa, suszeptibilitate modeloak eta euri prezipitazioen atalaseak. Gipuzkoako Lurralde Historikoan aplikatua (Euskal Herria).

227 p.Lurraldearen zonazioa lur labainketak jasotzeko aukeren arabera, suszeptibilitate mapen bitartez hain zuzen ere, hauek eragindako kalteak arindu eta mehatxua eta arrisku maila ebaluatu ahal izateko oinarrizko pausoa da. Tesi honek bide-orri baten definizioa aurkezten du, hutsetik abiatuta, lurraldearen suszeptibilitate mapen garapenerako eskala erregionalean. Helburua ikuspegi metodologiko eguneratu bat zehaztea da, prozeduraren pauso bakoitzean hartutako erabakia zientifikoki justifikatuak eta onartua izan daitezen. Hainbat esperimentu eta aplikazio gauzatzeko Gipuzkoako Probintzia hautatu da (1980 km2) . Ezaugarri eta izaera desberdineko hainbat aldagai independente, mota eta iturri desberdineko lur labainketa inbentarioak eta metodo ezagun nahiz metodo berritzaileekin batera ikuspegi desberdinak jorratu dira, azkenean lan honek aurkezten dituen ondorioak lortzeko.Emaitzen arabera, inferentzia geomorfologikoaren beharra azpimarratu daiteke estatistikoki gidatutako arauen arabera aldagai independenteen hautaketa egiterako orduan, hala nola, aldagai kategorikoen transformatzea aldagai jarraietan suszeptibilitate-modeloak garatzerako orduan onuragarria dela ere ondorioztatu da. Gainera, lur labainketen suszeptibilitate modeloak kalibratzeko ikuskatutako eremu efektiboaren erabilera positiboa dela frogatu da, lur labainketen inbentarioa landa lanaren bitartez eskuratu den kasuetan. Bestalde, malda unitateen erabilerak lurralde unitate bezala, ohikoak diren pixel unitateak izan beharrean, landa laneko inbentario batek ezarri dezakeen ziurgabetasuna arintzeko ahalmena erakutsi du.Horretaz gain, lur labainketak gertatzeko beharrezko prezipitazio atalasa definitzeko algoritmo baten aplikazioak ikerketa eremu berberean, aurreikuspenak egiteko beharrezkoa den informazioa erakutsi du, alerta goiztiar sistema baterantz aurreratzen joateko dauden aukerak goraipatuz