Comparing physically-based with data-driven models for landslide susceptibility: a case study in the Catalan Pyrenees

En este proyecto de investigación, se utilizaron un modelo físico (FSLAM) y cuatro modelos basados en datos (regresión logística, SVC, árbol de clasificación y bosque aleatorio) para mapear la susceptibilidad a los deslizamientos para un área de estudio ubicada en los Pirineos Catalanes. Seguidamente, se compararon los resultados de todos los modelos para determinar cuál funcionó mejor y en qué condiciones. También se discutieron las ventajas y desventajas de cada modelo, así como las limitaciones de sus productos finales.In this research project, a physically-based (FSLAM) and four data-driven models (logistic regression, SVC, classification tree and random forest) were used to map landslide susceptibility for a case study area located in the Catalan Pyrenees. The results for all models were then compared in order to determine which performed best and under which conditions. The advantages and disadvantages of each model were also discussed as well as the limitations of their end products

Features Analysis of the Research and Development Industry in Indonesia

R&D is one of the key drivers of technological progress and contributes to increased productivity and profit growth. Indonesian percentage of Gross Domestic Expenditure on R & R&D (GERD) to GDP in 2018 is one of the Global Competitiveness Index indicators, only reaches 0.28% and is dominated by the government sector, while the industrial sector is only 7.34%. One of the reasons for this small value is that the data collection of R&D on the business sector in Indonesia has not been carried out optimally. A classification model is needed to determine the data collection target so that the results are more optimal. The main objective of this study is to classify R&D industries actors in Indonesia using XGBoost and then analyze the features for R&D industries actors using SHAP. XGBoost is one of the black-box models that is difficult to interpret, and SHAP is one of the interpretation methods. The classification results using XGBoost obtained the accuracy, AUC, and F1-Score values of 79.61%, 0.7646, and 84.44%, respectively. Based on the Shapley value of the SHAP method, it was found that the average growth in R&D expenditure had the highest contribution. The feature's contribution to the estimation will be even higher if the mean of R&D expenditure growth is higher (more than 0). The other one is the ratio of researchers to R&D human resources. If the ratio is more than 75%, it will negatively contribute. Finally, exports and State-Owned Enterprise (BUMN) feature with the smallest contribution.https://dorl.net/dor/20.1001.1.20088302.2022.20.2.4.

A new integrated approach for landslide data balancing and spatial prediction based on generative adversarial networks (GAN)

Landslide susceptibility mapping has significantly progressed with improvements in machine learning techniques. However, the inventory / data imbalance (DI) problem remains one of the challenges in this domain. This problem exists as a good quality landslide inventory map, including a complete record of historical data, is difficult or expensive to collect. As such, this can considerably affect one’s ability to obtain a sufficient inventory or representative samples. This research developed a new approach based on generative adversarial networks (GAN) to correct imbalanced landslide datasets. The proposed method was tested at Chukha Dzongkhag, Bhutan, one of the most frequent landslide prone areas in the Himalayan region. The proposed approach was then compared with the standard methods such as the synthetic minority oversampling technique (SMOTE), dense imbalanced sampling, and sparse sampling (i.e., producing non-landslide samples as many as landslide samples). The comparisons were based on five machine learning models, including artificial neural networks (ANN), random forests (RF), decision trees (DT), k-nearest neighbours (kNN), and the support vector machine (SVM). The model evaluation was carried out based on overall accuracy (OA), Kappa Index, F1-score, and area under receiver operating characteristic curves (AUROC). The spatial database was established with a total of 269 landslides and 10 conditioning factors, including altitude, slope, aspect, total curvature, slope length, lithology, distance from the road, distance from the stream, topographic wetness index (TWI), and sediment transport index (STI). The findings of this study have shown that both GAN and SMOTE data balancing approaches have helped to improve the accuracy of machine learning models. According to AUROC, the GAN method was able to boost the models by reaching the maximum accuracy of ANN (0.918), RF (0.933), DT (0.927), kNN (0.878), and SVM (0.907) when default parameters used. With the optimum parameters, all models performed best with GAN at their highest accuracy of ANN (0.927), RF (0.943), DT (0.923) and kNN (0.889), except SVM obtained the highest accuracy of (0.906) with SMOTE. Our finding suggests that RF balanced with GAN can provide the most reasonable criterion for landslide prediction. This research indicates that landslide data balancing may substantially affect the predictive capabilities of machine learning models. Therefore, the issue of DI in the spatial prediction of landslides should not be ignored. Future studies could explore other generative models for landslide data balancing. By using state-of-the-art GAN, the proposed model can be considered in the areas where the data are limited or imbalanced

Landslide susceptibility modeling: An integrated novel method based on machine learning feature transformation

Landslide susceptibility modeling, an essential approach to mitigate natural disasters, has witnessed considerable improvement following advances in machine learning (ML) techniques. However, in most of the previous studies, the distribution of input data was assumed as being, and treated, as normal or Gaussian; this assumption is not always valid as ML is heavily dependent on the quality of the input data. Therefore, we examine the effectiveness of six feature transformations (minimax normalization (Std-X), logarithmic functions (Log-X), reciprocal function (Rec-X), power functions (Power-X), optimal features (Opt-X), and one-hot encoding (Ohe-X) over the 11conditioning factors (i.e., altitude, slope, aspect, curvature, distance to road, distance to lineament, distance to stream, terrain roughness index (TRI), normalized difference vegetation index (NDVI), land use, and vegetation density). We selected the frequent landslide-prone area in the Cameron Highlands in Malaysia as a case study to test this novel approach. These transformations were then assessed by three benchmark ML methods, namely extreme gradient boosting (XGB), logistic regression (LR), and artificial neural networks (ANN). The 10-fold cross-validation method was used for model evaluations. Our results suggest that using Ohe-X transformation over the ANN model considerably improved performance from 52.244 to 89.398 (37.154% improvement)

Assessment of earthquake-triggered landslides in Central Nepal

Landslides are recurrent in Nepal due to active tectonics, high precipitation, complex topography, geology, and land use practices. Reliable landslide susceptibility maps are crucial for effective disaster management. Ongoing research has improved landslide mapping approaches, while further efforts are needed to assess inventories and enhance susceptibility mapping methods. This thesis aims to evaluate the landslides caused by the Gorkha earthquake in 2015 and develop reliable landslide susceptibility maps using statistical and geospatial techniques. There are four main objectives: (i) proposing clustering-based sampling strategies to increase the efficiency of landslide susceptibility maps over random selection methods, (ii) identifying and delineating effective landslide mapping units, (iii) proposing an innovative framework for comparing inventories and their corresponding susceptibility maps, and (iv) implementing a methodology for landslide-specific susceptibility mapping. Firstly, a comprehensive Gorkha earthquake-induced landslide inventory was initially compiled, and six unsupervised clustering algorithms were employed to generate six distinct training datasets. An additional training dataset was also prepared using a randomised approach. Among the tested algorithms, the Expectation Maximization using the Gaussian Mixture Model (EM/GMM) demonstrated the highest accuracy, confirming the importance of prioritising clustering patterns for training landslide inventory datasets. Secondly, slope units were introduced as an effective mapping unit for assessing landslides, delineating 112,674 slope unit polygons over an approximately 43,000 km2 area in Central Nepal. This is the first instance of generating such comprehensive mapping and making it publicly accessible. Thirdly, a comparison of five post-Gorkha earthquake inventories and susceptibility was conducted, revealing similarities in causative factors and map performance but variations in spatial patterns. Lastly, a rockfall inventory along two significant highways was developed as a landslide-classified inventory, and the rockfall susceptibility was evaluated. A segment-wise map with a 1 to 5 scale indicating low to high susceptibility was published for public use. This thesis proposes new approaches to landslide inventory sampling and earthquake-triggered landslide assessment. It provides publicly accessible databases for Central Nepal's slope unit map and rockfall susceptibility along the major highways. These findings can benefit researchers, planners, and policymakers to enhance risk management practices by advancing landslide assessment, particularly for earthquake-induced landslides in Central Nepal

Classification models for 2,4-D formulations in damaged Enlist crops through the application of FTIR spectroscopy and machine learning algorithms

With new 2,4-Dichlorophenoxyacetic acid (2,4-D) tolerant crops, increases in off-target movement events are expected. New formulations may mitigate these events, but standard lab techniques are ineffective in identifying these 2,4-D formulations. Using Fourier-transform infrared spectroscopy and machine learning algorithms, research was conducted to classify 2,4-D formulations in treated herbicide-tolerant soybeans and cotton and observe the influence of leaf treatment status and collection timing on classification accuracy. Pooled Classification models using k-nearest neighbor classified 2,4-D formulations with over 65% accuracy in cotton and soybean. Tissue collected 14 DAT and 21 DAT for cotton and soybean respectively produced higher accuracies than the pooled model. Tissue directly treated with 2,4-D also performed better than the pooled model. Lastly, models using timing and treatment status as factors resulted in higher accuracies, with cotton 14 DAT New Growth and Treated models and 28 DAT and 21 DAT Treated soybean models achieving the best accuracies

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

The Hindu Kush Himalaya Assessment

This open access volume is the first comprehensive assessment of the Hindu Kush Himalaya (HKH) region. It comprises important scientific research on the social, economic, and environmental pillars of sustainable mountain development and will serve as a basis for evidence-based decision-making to safeguard the environment and advance people’s well-being. The compiled content is based on the collective knowledge of over 300 leading researchers, experts and policymakers, brought together by the Hindu Kush Himalayan Monitoring and Assessment Programme (HIMAP) under the coordination of the International Centre for Integrated Mountain Development (ICIMOD). This assessment was conducted between 2013 and 2017 as the first of a series of monitoring and assessment reports, under the guidance of the HIMAP Steering Committee: Eklabya Sharma (ICIMOD), Atiq Raman (Bangladesh), Yuba Raj Khatiwada (Nepal), Linxiu Zhang (China), Surendra Pratap Singh (India), Tandong Yao (China) and David Molden (ICIMOD and Chair of the HIMAP SC). This First HKH Assessment Report consists of 16 chapters, which comprehensively assess the current state of knowledge of the HKH region, increase the understanding of various drivers of change and their impacts, address critical data gaps and develop a set of evidence-based and actionable policy solutions and recommendations. These are linked to nine mountain priorities for the mountains and people of the HKH consistent with the Sustainable Development Goals. This book is a must-read for policy makers, academics and students interested in this important region and an essentially important resource for contributors to global assessments such as the IPCC reports. ; Constitutes the first comprehensive assessment of the Hindu Kush Himalaya region, providing an authoritative overview of the region Assembles the collective knowledge of over 300 leading researchers, practitioners, experts, and policymakers Combines the current state of knowledge of the Hindu Kush Himalaya region in one volume Offers Open Access to a set of practically oriented policy recommendation

Ecosystem Service and Land-Use Changes in Asia

This book highlights the role of research in Ecosystem Services and Land Use Changes in Asia. The contributions include case studies that explore the impacts of direct and indirect drivers affecting provision of ecosystem services in Asian countries, including China, India, Mongolia, Sri Lanka, and Vietnam. Findings from these empirical studies contribute to developing sustainability in Asia at both local and regional scales

The Hindu Kush Himalaya Assessment

This open access volume is the first comprehensive assessment of the Hindu Kush Himalaya (HKH) region. It comprises important scientific research on the social, economic, and environmental pillars of sustainable mountain development and will serve as a basis for evidence-based decision-making to safeguard the environment and advance people’s well-being. The compiled content is based on the collective knowledge of over 300 leading researchers, experts and policymakers, brought together by the Hindu Kush Himalayan Monitoring and Assessment Programme (HIMAP) under the coordination of the International Centre for Integrated Mountain Development (ICIMOD). This assessment was conducted between 2013 and 2017 as the first of a series of monitoring and assessment reports, under the guidance of the HIMAP Steering Committee: Eklabya Sharma (ICIMOD), Atiq Raman (Bangladesh), Yuba Raj Khatiwada (Nepal), Linxiu Zhang (China), Surendra Pratap Singh (India), Tandong Yao (China) and David Molden (ICIMOD and Chair of the HIMAP SC). This First HKH Assessment Report consists of 16 chapters, which comprehensively assess the current state of knowledge of the HKH region, increase the understanding of various drivers of change and their impacts, address critical data gaps and develop a set of evidence-based and actionable policy solutions and recommendations. These are linked to nine mountain priorities for the mountains and people of the HKH consistent with the Sustainable Development Goals. This book is a must-read for policy makers, academics and students interested in this important region and an essentially important resource for contributors to global assessments such as the IPCC reports. ; Constitutes the first comprehensive assessment of the Hindu Kush Himalaya region, providing an authoritative overview of the region Assembles the collective knowledge of over 300 leading researchers, practitioners, experts, and policymakers Combines the current state of knowledge of the Hindu Kush Himalaya region in one volume Offers Open Access to a set of practically oriented policy recommendation