Earthquake risk assessment using an integrated Fuzzy Analytic Hierarchy Process with Artificial Neural Networks based on GIS: A case study of Sanandaj in Iran

Earthquakes are natural phenomena, which induce natural hazard that seriously threatens urban areas, despite significant advances in retrofitting urban buildings and enhancing the knowledge and ability of experts in natural disaster control. Iran is one of the most seismically active countries in the world. The purpose of this study was to evaluate and analyze the extent of earthquake vulnerability in relation to demographic, environmental, and physical criteria. An earthquake risk assessment (ERA) map was created by using a Fuzzy-Analytic Hierarchy Process coupled with an Artificial Neural Networks (FAHP-ANN) model generating five vulnerability classes. Combining the application of a FAHP-ANN with a geographic information system (GIS) enabled to assign weights to the layers of the earthquake vulnerability criteria. The model was applied to Sanandaj City in Iran, located in the seismically active Sanandaj-Sirjan zone which is frequently affected by devastating earthquakes. The Multilayer Perceptron (MLP) model was implemented in the IDRISI software and 250 points were validated for grades 0 and 1. The validation process revealed that the proposed model can produce an earthquake probability map with an accuracy of 95%. A comparison of the results attained by using a FAHP, AHP and MLP model shows that the hybrid FAHP-ANN model proved flexible and reliable when generating the ERA map. The FAHP-ANN model accurately identified the highest earthquake vulnerability in densely populated areas with dilapidated building infrastructure. The findings of this study are useful for decision makers with a scientific basis to develop earthquake risk management strategies

Toward an integrated disaster management approach: How artificial intelligence can boost disaster management

Technical and methodological enhancement of hazards and disaster research is identified as a critical question in disaster management. Artificial intelligence (AI) applications, such as tracking and mapping, geospatial analysis, remote sensing techniques, robotics, drone technology, machine learning, telecom and network services, accident and hot spot analysis, smart city urban planning, transportation planning, and environmental impact analysis, are the technological components of societal change, having significant implications for research on the societal response to hazards and disasters. Social science researchers have used various technologies and methods to examine hazards and disasters through disciplinary, multidisciplinary, and interdisciplinary lenses. They have employed both quantitative and qualitative data collection and data analysis strategies. This study provides an overview of the current applications of AI in disaster management during its four phases and how AI is vital to all disaster management phases, leading to a faster, more concise, equipped response. Integrating a geographic information system (GIS) and remote sensing (RS) into disaster management enables higher planning, analysis, situational awareness, and recovery operations. GIS and RS are commonly recognized as key support tools for disaster management. Visualization capabilities, satellite images, and artificial intelligence analysis can assist governments in making quick decisions after natural disasters

Automation of Post-Earthquake Civil Infrastructure Reconnaissance

Traditionally post-earthquake structural engineering reconnaissance consists of a team of experts who are deployed to the field to record and capture earthquake damage data, which is later uploaded into online repositories. Despite many advances to these data archives in recent years, the entries in online repositories often have limited metadata which make it difficult and time consuming to extract specific damage evidence that can be used for meaningful analysis. This report outlines the author’s contributions to overcoming these challenges via the development of a neural network that automatically filters and classifies post-earthquake civil infrastructure damage data after a seismic event. In addition, this document summarizes the author’s progress on utilizing neural network output to rapidly generate geospatial maps from reconnaissance data

End-to-End Intelligent Framework for Rockfall Detection

Rockfall detection is a crucial procedure in the field of geology, which helps to reduce the associated risks. Currently, geologists identify rockfall events almost manually utilizing point cloud and imagery data obtained from different caption devices such as Terrestrial Laser Scanner or digital cameras. Multi-temporal comparison of the point clouds obtained with these techniques requires a tedious visual inspection to identify rockfall events which implies inaccuracies that depend on several factors such as human expertise and the sensibility of the sensors. This paper addresses this issue and provides an intelligent framework for rockfall event detection for any individual working in the intersection of the geology domain and decision support systems. The development of such an analysis framework poses significant research challenges and justifies intensive experimental analysis. In particular, we propose an intelligent system that utilizes multiple machine learning algorithms to detect rockfall clusters of point cloud data. Due to the extremely imbalanced nature of the problem, a plethora of state-of-the-art resampling techniques accompanied by multiple models and feature selection procedures are being investigated. Various machine learning pipeline combinations have been benchmarked and compared applying well-known metrics to be incorporated into our system. Specifically, we developed statistical and machine learning techniques and applied them to analyze point cloud data extracted from Terrestrial Laser Scanner in two distinct case studies, involving different geological contexts: the basaltic cliff of Castellfollit de la Roca and the conglomerate Montserrat Massif, both located in Spain. Our experimental data suggest that some of the above-mentioned machine learning pipelines can be utilized to detect rockfall incidents on mountain walls, with experimentally proven accuracy

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

Landslide Susceptibility Analysis on the Vicinity of Bogotá-Villavicencio Road (Eastern Cordillera of the Colombian Andes)

Landslide occurrence in Colombia is very frequent due to its geographical location in the Andean mountain range, with a very pronounced orography, a significant geological complexity and an outstanding climatic variability. More specifically, the study area around the Bogotá-Villavicencio road in the central sector of the Eastern Cordillera is one of the regions with the highest concentration of phenomena, which makes its study a priority. An inventory and detailed analysis of 2506 landslides has been carried out, in which five basic typologies have been differentiated: avalanches, debris flows, slides, earth flows and creeping areas. Debris avalanches and debris flows occur mainly in metamorphic materials (phyllites, schists and quartz-sandstones), areas with sparse vegetation, steep slopes and lower sections of hillslopes; meanwhile, slides, earth flows and creep occur in Cretaceous lutites, crop/grass lands, medium and low slopes and lower-middle sections of the hillslopes. Based on this analysis, landslide susceptibility models have been made for the different typologies and with different methods (matrix, discriminant analysis, random forest and neural networks) and input factors. The results are generally quite good, with average AUC-ROC values above 0.7–0.8, and the machine learning methods are the most appropriate, especially random forest, with a selected number of factors (between 6 and 8). The degree of fit (DF) usually shows relative errors lower than 5% and success higher than 90%. Finally, an integrated landslide susceptibility map (LSM) has been made for shallower and deeper types of movements. All the LSM show a clear zonation as a consequence of the geological control of the susceptibility

Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides

The robustness of landslide prediction models has become a major focus of researchers worldwide. We developed two novel hybrid predictive models that combine the self-organizing, deep-learning group method of data handling (GMDH) with two swarm intelligence optimization algorithms, i.e., cuckoo search algorithm (CSA) and whale optimization algorithm (WOA) for spatially explicit prediction of landslide susceptibility. Eleven landslide-causing factors and 334 historic landslides in a 31,340 km2 landslide-prone area in Iran were used to produce geospatial training and validation datasets. The GMDH model was employed to develop a basic predictive model that was then restructured and its parameters were optimized using the CSA and WOA algorithms, yielding the novel hybrid GMDH-CSA and GMDH-WOA models. The hybrid models were validated and compared to the standalone GMDH model by calculating the area under the receiver operating characteristic (AUC) curve and root mean square error (RMSE). The results demonstrated that the hybrid models overcame the computational shortcomings of the basic GMDH model and significantly improved landslide susceptibility prediction (GMDH-CSA, AUC = 0.909 and RMSE = 0.089; GMDH-WOA, AUC = 0.902 and RMSE = 0.129; standalone GMDH, AUC = 0.791 and RMSE = 0.226). Further, the hybrid models were more robust than the standalone GMDH model, showing consistently excellent performance when the training and validation datasets were changed. Overall, the swarm intelligence-optimized models, but not the standalone model, identified the best trade-offs among objectives, accuracy, and robustness

Riverside Landslide Susceptibility Overview: Leveraging Artificial Neural Networks and Machine Learning in Accordance with the United Nations (UN) Sustainable Development Goals

Riverside landslides present a significant geohazard globally, posing threats to infrastructure and human lives. In line with the United Nations’ Sustainable Development Goals (SDGs), which aim to address global challenges, professionals in the field have developed diverse methodologies to analyze, assess, and predict the occurrence of landslides, including quantitative, qualitative, and semi-quantitative approaches. With the advent of computer programs, quantitative techniques have gained prominence, with computational intelligence and knowledge-based methods like artificial neural networks (ANNs) achieving remarkable success in landslide susceptibility assessments. This article offers a comprehensive review of the literature concerning the utilization of ANNs for landslide susceptibility assessment, focusing specifically on riverside areas, in alignment with the SDGs. Through a systematic search and analysis of various references, it has become evident that ANNs have emerged as the preferred method for these assessments, surpassing traditional approaches. The application of ANNs aligns with the SDGs, particularly Goal 11: Sustainable Cities and Communities, which emphasizes the importance of inclusive, safe, resilient, and sustainable urban environments. By effectively assessing riverside landslide susceptibility using ANNs, communities can better manage risks and enhance the resilience of cities and communities to geohazards. While the number of ANN-based studies in landslide susceptibility modeling has grown in recent years, the overarching objective remains consistent: researchers strive to develop more accurate and detailed procedures. By leveraging the power of ANNs and incorporating relevant SDGs, this survey focuses on the most commonly employed neural network methods for riverside landslide susceptibility mapping, contributing to the overall SDG agenda of promoting sustainable development, resilience, and disaster risk reduction. Through the integration of ANNs in riverside landslide susceptibility assessments, in line with the SDGs, this review aims to advance our knowledge and understanding of this field. By providing insights into the effectiveness of ANNs and their alignment with the SDGs, this research contributes to the development of improved risk management strategies, sustainable urban planning, and resilient communities in the face of riverside landslides

A Novel Method for Landslide Displacement Prediction by Integrating Advanced Computational Intelligence Algorithms

Landslide displacement prediction is considered as an essential component for developing early warning systems. The modelling of conventional forecast methods requires enormous monitoring data that limit its application. To conduct accurate displacement prediction with limited data, a novel method is proposed and applied by integrating three computational intelligence algorithms namely: the wavelet transform (WT), the artificial bees colony (ABC), and the kernel-based extreme learning machine (KELM). At first, the total displacement was decomposed into several sub-sequences with different frequencies using the WT. Next each sub-sequence was predicted separately by the KELM whose parameters were optimized by the ABC. Finally the predicted total displacement was obtained by adding all the predicted sub-sequences. The Shuping landslide in the Three Gorges Reservoir area in China was taken as a case study. The performance of the new method was compared with the WT-ELM, ABC-KELM, ELM, and the support vector machine (SVM) methods. Results show that the prediction accuracy can be improved by decomposing the total displacement into sub-sequences with various frequencies and by predicting them separately. The ABC-KELM algorithm shows the highest prediction capacity followed by the ELM and SVM. Overall, the proposed method achieved excellent performance both in terms of accuracy and stability