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In this research, we used the integration of frequency ratio and adaptive neuro-fuzzy modeling (ANFIS) to predict landslide susceptibility along forest road networks in the Hyrcanian Forest, northern Iran. We began our study by first mapping landslide locations during an extensive field survey. In addition, we then selected landslide-conditioning factors, such as slope, aspect, altitude, rainfall, geology, soil, road age, and slip position from the available Geographic Information System (GIS) data. Following this, we developed Adaptive Neuro-Fuzzy Inference System (ANFIS) models with two different membership functions (MFs) in order to generate landslide susceptibility maps. We applied a frequency ratio model to the landslide susceptibility mapping and compared the results with the probabilistic ANFIS model. Finally, we calculated map accuracy by evaluating receiver-operating characteristics (ROC). The validation results yielded 70.7% accuracy using the triangular MF model, 67.8% accuracy using the Gaussian MF model, and 68.8% accuracy using the frequency ratio model. Our results indicated that the ANFIS is an effective tool for regional landslide susceptibility assessment, and the maps produced in the study area can be used for natural hazard management in the landslide-prone area of the Hyrcanian region.
Nastaran Zare; Seyed Ata Ollah Hosseini; Mohammad Kazem Hafizi; Akbar Najafi; Baris Majnounian; Marten Geertsema. A Comparison of an Adaptive Neuro-Fuzzy and Frequency Ratio Model to Landslide-Susceptibility Mapping along Forest Road Networks. Forests 2021, 12, 1087 .
AMA StyleNastaran Zare, Seyed Ata Ollah Hosseini, Mohammad Kazem Hafizi, Akbar Najafi, Baris Majnounian, Marten Geertsema. A Comparison of an Adaptive Neuro-Fuzzy and Frequency Ratio Model to Landslide-Susceptibility Mapping along Forest Road Networks. Forests. 2021; 12 (8):1087.
Chicago/Turabian StyleNastaran Zare; Seyed Ata Ollah Hosseini; Mohammad Kazem Hafizi; Akbar Najafi; Baris Majnounian; Marten Geertsema. 2021. "A Comparison of an Adaptive Neuro-Fuzzy and Frequency Ratio Model to Landslide-Susceptibility Mapping along Forest Road Networks." Forests 12, no. 8: 1087.
Earth's climate is warming and will continue to warm as the century progresses. High mountains and high latitudes are experiencing the greatest warming of all regions on Earth and also are some of the most sensitive areas to climate change, in part because ecosystems and natural processes in these areas are intimately linked to the cryosphere. Evidence is mounting that warming will further reduce permafrost and snow and ice cover in high mountains, which in turn will destabilize many slopes, alter sediment delivery to streams, and change subalpine and alpine ecosystems. This paper contributes to the continuing discussion of impacts of climate change on mountain environments by comparing and discussing processes and trends in the mountains of western Canada and the European Alps. We highlight the effects of physiography and climate on physical processes occurring in the two regions. Processes of interest include landslides and debris flows induced by glacier debuttressing, alpine permafrost thaw, changes in rainfall regime, formation and sudden drainage of glacier- and moraine-dammed lakes, ice avalanches, glacier surges, and large-scale sediment transfers due to rapid deglacierization. Our analysis points out the value of integrating observations and data from different areas of the world to better understand these processes and their impacts.
Marta Chiarle; Marten Geertsema; Giovanni Mortara; John J. Clague. Relations between climate change and mass movement: Perspectives from the Canadian Cordillera and the European Alps. Global and Planetary Change 2021, 103499 .
AMA StyleMarta Chiarle, Marten Geertsema, Giovanni Mortara, John J. Clague. Relations between climate change and mass movement: Perspectives from the Canadian Cordillera and the European Alps. Global and Planetary Change. 2021; ():103499.
Chicago/Turabian StyleMarta Chiarle; Marten Geertsema; Giovanni Mortara; John J. Clague. 2021. "Relations between climate change and mass movement: Perspectives from the Canadian Cordillera and the European Alps." Global and Planetary Change , no. : 103499.
With the development of remote sensing algorithms and increased access to satellite data, generating up-to-date, accurate land use/land cover (LULC) maps has become increasingly feasible for evaluating and managing changes in land cover as created by changes to ecosystem and land use. The main objective of our study is to evaluate the performance of Support Vector Machine (SVM), Artificial Neural Network (ANN), Maximum Likelihood Classification (MLC), Minimum Distance (MD), and Mahalanobis (MH) algorithms and compare them in order to generate a LULC map using data from Sentinel 2 and Landsat 8 satellites. Further, we also investigate the effect of a penalty parameter on SVM results. Our study uses different kernel functions and hidden layers for SVM and ANN algorithms, respectively. We generated the training and validation datasets from Google Earth images and GPS data prior to pre-processing satellite data. In the next phase, we classified the images using training data and algorithms. Ultimately, to evaluate outcomes, we used the validation data to generate a confusion matrix of the classified images. Our results showed that with optimal tuning parameters, the SVM classifier yielded the highest overall accuracy (OA) of 94%, performing better for both satellite data compared to other methods. In addition, for our scenes, Sentinel 2 date was slightly more accurate compared to Landsat 8. The parametric algorithms MD and MLC provided the lowest accuracy of 80.85% and 74.68% for the data from Sentinel 2 and Landsat 8. In contrast, our evaluation using the SVM tuning parameters showed that the linear kernel with the penalty parameter 150 for Sentinel 2 and the penalty parameter 200 for Landsat 8 yielded the highest accuracies. Further, ANN classification showed that increasing the hidden layers drastically reduces classification accuracy for both datasets, reducing zero for three hidden layers.
Laleh Ghayour; Aminreza Neshat; Sina Paryani; Himan Shahabi; Ataollah Shirzadi; Wei Chen; Nadhir Al-Ansari; Marten Geertsema; Mehdi Pourmehdi Amiri; Mehdi Gholamnia; Jie Dou; Anuar Ahmad. Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms. Remote Sensing 2021, 13, 1349 .
AMA StyleLaleh Ghayour, Aminreza Neshat, Sina Paryani, Himan Shahabi, Ataollah Shirzadi, Wei Chen, Nadhir Al-Ansari, Marten Geertsema, Mehdi Pourmehdi Amiri, Mehdi Gholamnia, Jie Dou, Anuar Ahmad. Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms. Remote Sensing. 2021; 13 (7):1349.
Chicago/Turabian StyleLaleh Ghayour; Aminreza Neshat; Sina Paryani; Himan Shahabi; Ataollah Shirzadi; Wei Chen; Nadhir Al-Ansari; Marten Geertsema; Mehdi Pourmehdi Amiri; Mehdi Gholamnia; Jie Dou; Anuar Ahmad. 2021. "Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms." Remote Sensing 13, no. 7: 1349.
On 28 November 2020, about 18 Mm3 of quartz diorite detached from a steep rock face at the head of Elliot Creek in the southern Coast Mountains of British Columbia. The rock mass fragmented as it descended 1000 m and flowed across a debris-covered glacier. The rock avalanche was recorded on local and distant seismometers, with long-period amplitudes equivalent to a M 4.9 earthquake. Local seismic stations detected several earthquakes of magnitude <2.4 over the minutes and hours preceding the slide, though no causative relationship is yet suggested. More than half of the rock debris entered a 0.6 km2 lake, where it generated a huge displacement wave that overtopped the moraine at the far end of the lake. Water that left the lake was channelized along Elliot Creek, deeply scouring the valley fill over a distance of 10 km before depositing debris on a 2 km2 fan in the Southgate River valley. Debris temporarily dammed the river, and turbid water continued down the Southgate River to Bute Inlet, where it produced a 70 km turbidity current and altered turbidity and water chemistry in the inlet for weeks. The landslide followed a century of rapid glacier retreat and thinning that exposed a growing lake basin. The outburst flood extended the damage of the landslide far beyond the limit of the landslide, destroying forest and impacting salmon spawning and rearing habitat. We expect more cascading impacts from landslides in the glacierized mountains of British Columbia as glaciers continue to retreat, exposing water bodies below steep slopes while simultaneously removing buttressing support.
Marten Geertsema; Brian Menounos; Dan Shugar; Tom Millard; Brent Ward; Göran Ekstrom; John Clague; Patrick Lynett; Pierre Friele; Andrew Schaeffer; Jennifer Jackson; Bretwood Higman; Chunli Dai; Camille Brillon; Derek Heathfield; Gemma Bullard; Ian Giesbrecht; Katie Hughes. A landslide-generated tsunami and outburst flood at Elliot Creek, coastal British Columbia . 2021, 1 .
AMA StyleMarten Geertsema, Brian Menounos, Dan Shugar, Tom Millard, Brent Ward, Göran Ekstrom, John Clague, Patrick Lynett, Pierre Friele, Andrew Schaeffer, Jennifer Jackson, Bretwood Higman, Chunli Dai, Camille Brillon, Derek Heathfield, Gemma Bullard, Ian Giesbrecht, Katie Hughes. A landslide-generated tsunami and outburst flood at Elliot Creek, coastal British Columbia . . 2021; ():1.
Chicago/Turabian StyleMarten Geertsema; Brian Menounos; Dan Shugar; Tom Millard; Brent Ward; Göran Ekstrom; John Clague; Patrick Lynett; Pierre Friele; Andrew Schaeffer; Jennifer Jackson; Bretwood Higman; Chunli Dai; Camille Brillon; Derek Heathfield; Gemma Bullard; Ian Giesbrecht; Katie Hughes. 2021. "A landslide-generated tsunami and outburst flood at Elliot Creek, coastal British Columbia ." , no. : 1.
On 28 November 2020, some 18 Mm3 of quartz diorite detached from a steep rock face at the head of Elliot Creek in the southern Coast Mountains of British Columbia. The rock mass fragmented as it descended 1000 m and flowed across a debris-covered glacier. The rock avalanche was recorded on local and distant seismometers, with long-period amplitudes equivalent to a M 4.9 earthquake. Local seismic stations detected several earthquakes of magnitude <2.4 over the minutes and hours preceding the slide, though no causative relationship is yet suggested. Pre-slide optical and radar remote sensing data indicated some slope deformation leading up to failure. More than half of the rock debris entered a 0.6 km2 lake, where it generated a 115 m displacement wave that overtopped the moraine at the far end of the lake. We estimate that some 13.5 Mm3 of water left the lake from the combined impact of the landslide as well as erosion of the dam. The water that left the lake was channelized along Elliot Creek, scouring the valley more than 40 m in some places over a distance of 10 km before depositing debris on a 2 km2 fan in the Southgate River valley. Debris temporarily dammed the river, and turbid water continued down the Southgate River to Bute Inlet, where it produced a 70 km turbidity current and altered turbidity and water chemistry in the inlet for weeks. The landslide followed a century of rapid glacier retreat and thinning that exposed a growing lake basin. The outburst flood extended the damage of the landslide far beyond the limit of the landslide, destroying forest and impacting salmon spawning and rearing habitat. We expect more cascading impacts from landslides in the glacierized mountains of British Columbia as glaciers continue to retreat, exposing water bodies below steep slopes while simultaneously removing buttressing support.
Marten Geertsema; Brian Menounous; Dan Shugar; Tom Millard; Brent Ward; Göran Ekstrom; John Clague; Patrick Lynett; Jonathan Carrivick; Pierre Friele; Andrew Schaeffer; Davide Donatti; Doug Stead; Jennifer Jackson; Bretwood Higman; Chunli Dai; Camille Brillon; Derek Heathfield; Gemma Bullard; Ian Giesbrecht; Katie Hughes; Mylène Jacquemart. Terrestrial overview of a landslide-tsunami-flood cascade at Elliot Creek, British Columbia . 2021, 1 .
AMA StyleMarten Geertsema, Brian Menounous, Dan Shugar, Tom Millard, Brent Ward, Göran Ekstrom, John Clague, Patrick Lynett, Jonathan Carrivick, Pierre Friele, Andrew Schaeffer, Davide Donatti, Doug Stead, Jennifer Jackson, Bretwood Higman, Chunli Dai, Camille Brillon, Derek Heathfield, Gemma Bullard, Ian Giesbrecht, Katie Hughes, Mylène Jacquemart. Terrestrial overview of a landslide-tsunami-flood cascade at Elliot Creek, British Columbia . . 2021; ():1.
Chicago/Turabian StyleMarten Geertsema; Brian Menounous; Dan Shugar; Tom Millard; Brent Ward; Göran Ekstrom; John Clague; Patrick Lynett; Jonathan Carrivick; Pierre Friele; Andrew Schaeffer; Davide Donatti; Doug Stead; Jennifer Jackson; Bretwood Higman; Chunli Dai; Camille Brillon; Derek Heathfield; Gemma Bullard; Ian Giesbrecht; Katie Hughes; Mylène Jacquemart. 2021. "Terrestrial overview of a landslide-tsunami-flood cascade at Elliot Creek, British Columbia ." , no. : 1.
A bathymetric survey of Harrison Lake in southwest British Columbia revealed deposits of three large landslides on the lake floor. The blocky and flow-like surface morphology of the deposits suggests rapid emplacement from subaerial sources. The multibeam survey, together with a subbottom acoustic survey, allowed us to estimate deposit volumes of 2.4 Mm3, 1.3 Mm3, and 0.2 Mm3 for the Mount Douglas, Mount Breakenridge, and Silver Mountain landslides, respectively. The large volumes and inferred rapid emplacement of the Mount Douglas and Mount Breakenridge landslides suggest they were tsunamigenic. Because people live along the shoreline of Harrison Lake, our discovery and characterization of these landslide deposits and their tsunami-generating potential form an important foundation for further landslide-tsunami hazard analysis in the region.
K. E. Hughes; M. Geertsema; E. Kwoll; M. N. Koppes; N. J. Roberts; J. J. Clague; S. Rohland. Previously undiscovered landslide deposits in Harrison Lake, British Columbia, Canada. Landslides 2020, 18, 529 -538.
AMA StyleK. E. Hughes, M. Geertsema, E. Kwoll, M. N. Koppes, N. J. Roberts, J. J. Clague, S. Rohland. Previously undiscovered landslide deposits in Harrison Lake, British Columbia, Canada. Landslides. 2020; 18 (2):529-538.
Chicago/Turabian StyleK. E. Hughes; M. Geertsema; E. Kwoll; M. N. Koppes; N. J. Roberts; J. J. Clague; S. Rohland. 2020. "Previously undiscovered landslide deposits in Harrison Lake, British Columbia, Canada." Landslides 18, no. 2: 529-538.
Zrebar Lake is one of the largest freshwater lakes in Iran and it plays an important role in the ecosystem of the environment, while its desiccation has a negative impact on the surrounded ecosystem. Despite this, this lake provides an interesting recreation setting in terms of ecotourism. The prediction and forecasting of the water level of the lake through simple but practical methods can provide a reliable tool for future lake water resource management. In the present study, we predict the daily water level of Zrebar Lake in Iran through well-known decision tree-based algorithms, including the M5 pruned (M5P), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). We used five different water input combinations to find the most effective one. For our modeling, we chose 70% of the dataset for training (from 2011 to 2015) and 30% for model evaluation (from 2015 to 2017). We evaluated the models’ performances using different quantitative (root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), percent bias (PBIAS) and ratio of the root mean square error to the standard deviation of measured data (RSR)) and visual frameworks (Taylor diagram and box plot). Our results showed that water level with a one-day lag time had the highest effect on the result and, by increasing the lag time, its effect on the result was decreased. This result indicated that all the developed models had a good prediction capability, but the M5P model outperformed the others, followed by RF and RT equally and then REPT. Our results showed that these algorithms can predict water level accurately only with a one-day lag time in water level as an input and they are cost-effective tools for future predictions.
Viet-Ha Nhu; Himan Shahabi; Ebrahim Nohani; Ataollah Shirzadi; Nadhir Al-Ansari; Sepideh Bahrami; Shaghayegh Miraki; Marten Geertsema; Hoang Nguyen. Daily Water Level Prediction of Zrebar Lake (Iran): A Comparison between M5P, Random Forest, Random Tree and Reduced Error Pruning Trees Algorithms. ISPRS International Journal of Geo-Information 2020, 9, 479 .
AMA StyleViet-Ha Nhu, Himan Shahabi, Ebrahim Nohani, Ataollah Shirzadi, Nadhir Al-Ansari, Sepideh Bahrami, Shaghayegh Miraki, Marten Geertsema, Hoang Nguyen. Daily Water Level Prediction of Zrebar Lake (Iran): A Comparison between M5P, Random Forest, Random Tree and Reduced Error Pruning Trees Algorithms. ISPRS International Journal of Geo-Information. 2020; 9 (8):479.
Chicago/Turabian StyleViet-Ha Nhu; Himan Shahabi; Ebrahim Nohani; Ataollah Shirzadi; Nadhir Al-Ansari; Sepideh Bahrami; Shaghayegh Miraki; Marten Geertsema; Hoang Nguyen. 2020. "Daily Water Level Prediction of Zrebar Lake (Iran): A Comparison between M5P, Random Forest, Random Tree and Reduced Error Pruning Trees Algorithms." ISPRS International Journal of Geo-Information 9, no. 8: 479.
We used remote sensing techniques and machine learning to detect and map landslides, and landslide susceptibility in the Cameron Highlands, Malaysia. We located 152 landslides using a combination of interferometry synthetic aperture radar (InSAR), Google Earth (GE), and field surveys. Of the total slide locations, 80% (122 landslides) were utilized for training the selected algorithms, and the remaining 20% (30 landslides) were applied for validation purposes. We employed 17 conditioning factors, including slope angle, aspect, elevation, curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), lithology, soil type, land cover, normalized difference vegetation index (NDVI), distance to river, distance to fault, distance to road, river density, fault density, and road density, which were produced from satellite imageries, geological map, soil maps, and a digital elevation model (DEM). We used these factors to produce landslide susceptibility maps using logistic regression (LR), logistic model tree (LMT), and random forest (RF) models. To assess prediction accuracy of the models we employed the following statistical measures: negative predictive value (NPV), sensitivity, positive predictive value (PPV), specificity, root-mean-squared error (RMSE), accuracy, and area under the receiver operating characteristic (ROC) curve (AUC). Our results indicated that the AUC was 92%, 90%, and 88% for the LMT, LR, and RF algorithms, respectively. To assess model performance, we also applied non-parametric statistical tests of Friedman and Wilcoxon, where the results revealed that there were no practical differences among the used models in the study area. While landslide mapping in tropical environment such as Cameron Highlands remains difficult, the remote sensing (RS) along with machine learning techniques, such as the LMT model, show promise for landslide susceptibility mapping in the study area.
Viet-Ha Nhu; Ayub Mohammadi; Himan Shahabi; Baharin Bin Ahmad; Nadhir Al-Ansari; Ataollah Shirzadi; Marten Geertsema; Victoria R. R. Kress; Sadra Karimzadeh; Khalil valizadeh Kamran; Wei Chen; Hoang Nguyen. Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms. Forests 2020, 11, 830 .
AMA StyleViet-Ha Nhu, Ayub Mohammadi, Himan Shahabi, Baharin Bin Ahmad, Nadhir Al-Ansari, Ataollah Shirzadi, Marten Geertsema, Victoria R. R. Kress, Sadra Karimzadeh, Khalil valizadeh Kamran, Wei Chen, Hoang Nguyen. Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms. Forests. 2020; 11 (8):830.
Chicago/Turabian StyleViet-Ha Nhu; Ayub Mohammadi; Himan Shahabi; Baharin Bin Ahmad; Nadhir Al-Ansari; Ataollah Shirzadi; Marten Geertsema; Victoria R. R. Kress; Sadra Karimzadeh; Khalil valizadeh Kamran; Wei Chen; Hoang Nguyen. 2020. "Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms." Forests 11, no. 8: 830.
Liquefaction is a hazardous seismic-based phenomenon, which causes an abrupt decrease in soil strength properties and can result in the massive destruction of the built environment. This research presents a novel approach to reduce the risk of soil liquefaction using jet-grouted micropiles in clean sands. The saturated soil profile of the study project mainly contains clean sands, which are suitable to more reliably employ simplified soil liquefaction analyses. The grouting is conducted using 420 micropiles to increase the existing soil properties. The effect of jet grouting on reducing the potential of liquefaction is assessed using the results of the cone penetration test (CPT) and the standard penetration test (SPT), which were conducted before and after jet grouting by implementing micropiles in the project sites. According to three CPT-based liquefaction analyses, the Juang method predicts the most effective improvement range of the factor of safety in the clean sand. The Boulanger and Idriss, and Eurocode methods show comparable evaluations. Results of the SPT-based analyses show the most considerable increase of the factor of safety following the Boulanger and Idriss, and NCEER approaches in the SP soil. CPT- and SPT-based analyses confirm the effectiveness of jet grouting by micropiles on enhancing soil properties and reducing the risk of liquefaction.
Visar Farhangi; Moses Karakouzian; Marten Geertsema. Effect of Micropiles on Clean Sand Liquefaction Risk Based on CPT and SPT. Applied Sciences 2020, 10, 3111 .
AMA StyleVisar Farhangi, Moses Karakouzian, Marten Geertsema. Effect of Micropiles on Clean Sand Liquefaction Risk Based on CPT and SPT. Applied Sciences. 2020; 10 (9):3111.
Chicago/Turabian StyleVisar Farhangi; Moses Karakouzian; Marten Geertsema. 2020. "Effect of Micropiles on Clean Sand Liquefaction Risk Based on CPT and SPT." Applied Sciences 10, no. 9: 3111.
We generated high-quality shallow landslide susceptibility maps for Bijar County, Kurdistan Province, Iran, using Random Forest (RAF), an ensemble computational intelligence method and three meta classifiers—Bagging (BA, BA-RAF), Random Subspace (RS, RS-RAF), and Rotation Forest (RF, RF-RAF). Modeling and validation were done on 111 shallow landslide locations using 20 conditioning factors tested by the Information Gain Ratio (IGR) technique. We assessed model performance with statistically based indexes, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). All four machine learning models that we tested yielded excellent goodness-of-fit and prediction accuracy, but the RF-RAF ensemble model (AUC = 0.936) outperformed the BA-RAF, RS-RAF (AUC = 0.907), and RAF (AUC = 0.812) models. The results also show that the Random Forest model significantly improved the predictive capability of the RAF-based classifier and, therefore, can be considered as a useful and an effective tool in regional shallow landslide susceptibility mapping.
Viet-Ha Nhu; Ataollah Shirzadi; Himan Shahabi; Wei Chen; John J Clague; Marten Geertsema; Abolfazl Jaafari; Mohammadtaghi Avand; Shaghayegh Miraki; Davood Talebpour Asl; Binh Thai Pham; Baharin Bin Ahmad; Saro Lee. Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran. Forests 2020, 11, 421 .
AMA StyleViet-Ha Nhu, Ataollah Shirzadi, Himan Shahabi, Wei Chen, John J Clague, Marten Geertsema, Abolfazl Jaafari, Mohammadtaghi Avand, Shaghayegh Miraki, Davood Talebpour Asl, Binh Thai Pham, Baharin Bin Ahmad, Saro Lee. Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran. Forests. 2020; 11 (4):421.
Chicago/Turabian StyleViet-Ha Nhu; Ataollah Shirzadi; Himan Shahabi; Wei Chen; John J Clague; Marten Geertsema; Abolfazl Jaafari; Mohammadtaghi Avand; Shaghayegh Miraki; Davood Talebpour Asl; Binh Thai Pham; Baharin Bin Ahmad; Saro Lee. 2020. "Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran." Forests 11, no. 4: 421.
Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas.
Himan Shahabi; Ataollah Shirzadi; Kayvan Ghaderi; Ebrahim Omidvar; Nadhir Al-Ansari; John J. Clague; Marten Geertsema; Khabat Khosravi; Ata Amini; Sepideh Bahrami; Omid Rahmati; Kyoumars Habibi; Ayub Mohammadi; Hoang Nguyen; Assefa M. Melesse; Baharin Bin Ahmad; Anuar Ahmad. Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier. Remote Sensing 2020, 12, 266 .
AMA StyleHiman Shahabi, Ataollah Shirzadi, Kayvan Ghaderi, Ebrahim Omidvar, Nadhir Al-Ansari, John J. Clague, Marten Geertsema, Khabat Khosravi, Ata Amini, Sepideh Bahrami, Omid Rahmati, Kyoumars Habibi, Ayub Mohammadi, Hoang Nguyen, Assefa M. Melesse, Baharin Bin Ahmad, Anuar Ahmad. Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier. Remote Sensing. 2020; 12 (2):266.
Chicago/Turabian StyleHiman Shahabi; Ataollah Shirzadi; Kayvan Ghaderi; Ebrahim Omidvar; Nadhir Al-Ansari; John J. Clague; Marten Geertsema; Khabat Khosravi; Ata Amini; Sepideh Bahrami; Omid Rahmati; Kyoumars Habibi; Ayub Mohammadi; Hoang Nguyen; Assefa M. Melesse; Baharin Bin Ahmad; Anuar Ahmad. 2020. "Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier." Remote Sensing 12, no. 2: 266.
In this paper, we examine the influence of the 27 October 2012, Mw 7.8 earthquake on landslide occurrence in the southern half of Haida Gwaii (formerly Queen Charlotte Islands), British Columbia, Canada. Our 1350 km2 study area is undisturbed, primarily forested terrain that has not experienced road building or timber harvesting. Our inventory of landslide polygons is based on optical airborne and spaceborne images acquired between 2007 and 2018, from which we extracted and mapped 446 individual landslides (an average of 33 landslides per 100 km2). The landslide rate in years without major earthquakes averages 19.4 per year, or 1.4/100 km2/year, and the annual average area covered by non-seismically triggered landslides is 35 ha/year. The number of landslides identified in imagery closely following the 2012 earthquake, and probably triggered by it, is 244 or an average of about 18 landslides per 100 km2. These landslides cover a total area of 461 ha. In the following years—2013–2016 and 2016–2018—the number of landslides fell, respectively, to 26 and 13.5 landslides per year. In non-earthquake years, most landslides happen on south-facing slopes, facing the prevailing winds. In contrast, during or immediately after the earthquake, up to 32% of the landslides occurred on north and northwest-facing slopes. Although we could not find imagery from the day after the earthquake, overview reconnaissance flights 10 and 16 days later showed that most of the landslides were recent, suggesting they were co-seismic.
Sophia Barth; Marten Geertsema; Alexandre R. Bevington; Alison L. Bird; John J. Clague; Tom Millard; Peter T. Bobrowsky; Andreas Hasler; Hongjiang Liu. Landslide response to the 27 October 2012 earthquake (MW 7.8), southern Haida Gwaii, British Columbia, Canada. Landslides 2019, 17, 517 -526.
AMA StyleSophia Barth, Marten Geertsema, Alexandre R. Bevington, Alison L. Bird, John J. Clague, Tom Millard, Peter T. Bobrowsky, Andreas Hasler, Hongjiang Liu. Landslide response to the 27 October 2012 earthquake (MW 7.8), southern Haida Gwaii, British Columbia, Canada. Landslides. 2019; 17 (3):517-526.
Chicago/Turabian StyleSophia Barth; Marten Geertsema; Alexandre R. Bevington; Alison L. Bird; John J. Clague; Tom Millard; Peter T. Bobrowsky; Andreas Hasler; Hongjiang Liu. 2019. "Landslide response to the 27 October 2012 earthquake (MW 7.8), southern Haida Gwaii, British Columbia, Canada." Landslides 17, no. 3: 517-526.
We prepared a landslide susceptibility map for the Sarkhoon watershed, Chaharmahal-w-bakhtiari, Iran, using novel ensemble artificial intelligence approaches. A classifier of support vector machine (SVM) was employed as a base classifier, and four Meta/ensemble classifiers, including Adaboost (AB), bagging (BA), rotation forest (RF), and random subspace (RS), were used to construct new ensemble models. SVM has been used previously to spatially predict landslides, but not together with its ensembles. We selected 20 conditioning factors and randomly portioned 98 landslide locations into training (70%) and validating (30%) groups. Several statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC), were used for model comparison and validation. Using the One-R Attribute Evaluation (ORAE) technique, we found that all 20 conditioning factors were significant in identifying landslide locations, but “distance to road” was found to be the most important. The RS (AUC = 0.837) and RF (AUC = 0.834) significantly improved the goodness-of-fit and prediction accuracy of the SVM (AUC = 0.810), whereas the BA (AUC = 0.807) and AB (AUC = 0.779) did not. The random subspace based support vector machine (RSSVM) model is a promising technique for helping to better manage land in landslide-prone areas.
Dieu Tien Bui; Ataollah Shirzadi; Himan Shahabi; Marten Geertsema; Ebrahim Omidvar; John J. Clague; Binh Thai Pham; Jie Dou; Dawood Talebpour Asl; Baharin Bin Ahmad; Saro Lee. New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed. Forests 2019, 10, 743 .
AMA StyleDieu Tien Bui, Ataollah Shirzadi, Himan Shahabi, Marten Geertsema, Ebrahim Omidvar, John J. Clague, Binh Thai Pham, Jie Dou, Dawood Talebpour Asl, Baharin Bin Ahmad, Saro Lee. New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed. Forests. 2019; 10 (9):743.
Chicago/Turabian StyleDieu Tien Bui; Ataollah Shirzadi; Himan Shahabi; Marten Geertsema; Ebrahim Omidvar; John J. Clague; Binh Thai Pham; Jie Dou; Dawood Talebpour Asl; Baharin Bin Ahmad; Saro Lee. 2019. "New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed." Forests 10, no. 9: 743.
We used a novel hybrid functional machine learning algorithm to predict the spatial distribution of landslides in the Sarkhoon watershed, Iran. We developed a new ensemble model which is a combination of a functional algorithm, stochastic gradient descent (SGD) and an AdaBoost (AB) Meta classifier namely ABSGD model to predict the landslides. The model incorporates 20 landslide conditioning factors, which we ranked using the least-square support vector machine (LSSVM) technique. For the modeling, we considered 98 landslide locations, of which 70% (79) were used for training and 30% (19) for validation processes. Model validation was performed using sensitivity, specificity, accuracy, the root mean square error (RMSE) and the area under the receiver operatic characteristic (AUC) curve. We also used soft computing benchmark models, including SGD, logistic regression (LR), logistic model tree (LMT) and functional tree (FT) algorithms for model validation and comparison. The selected conditioning factors were significant in landslide occurrence but distance to road was found to be the most important factor. The ABSGD model (AUC= 0.860) outperformed the LR (0.797), SGD (0.776), LMT (0.740) and FT (0.734) models. Our results confirm that the combined use of a functional algorithm and a Meta classifier prevents over-fitting, reduces noise and enhances the power prediction of the individual SGD algorithm for the spatial prediction of landslides.
Dieu Tien Bui; Himan Shahabi; Ebrahim Omidvar; Ataollah Shirzadi; Marten Geertsema; John J. Clague; Khabat Khosravi; Biswajeet Pradhan; Binh Thai Pham; Kamran Chapi; Zahra Barati; Baharin Bin Ahmad; Hosein Rahmani; Gyula Gróf; Saro Lee. Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm. Remote Sensing 2019, 11, 931 .
AMA StyleDieu Tien Bui, Himan Shahabi, Ebrahim Omidvar, Ataollah Shirzadi, Marten Geertsema, John J. Clague, Khabat Khosravi, Biswajeet Pradhan, Binh Thai Pham, Kamran Chapi, Zahra Barati, Baharin Bin Ahmad, Hosein Rahmani, Gyula Gróf, Saro Lee. Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm. Remote Sensing. 2019; 11 (8):931.
Chicago/Turabian StyleDieu Tien Bui; Himan Shahabi; Ebrahim Omidvar; Ataollah Shirzadi; Marten Geertsema; John J. Clague; Khabat Khosravi; Biswajeet Pradhan; Binh Thai Pham; Kamran Chapi; Zahra Barati; Baharin Bin Ahmad; Hosein Rahmani; Gyula Gróf; Saro Lee. 2019. "Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm." Remote Sensing 11, no. 8: 931.
A large subaerial landslide entered Taan Fiord, Alaska, on 17 October 2015 producing a tsunami with runup to 193 m. We use LiDAR data to show the slide volume to be 76 +3/‐4 million m3 and that 51 million m3 entered Taan Fiord. In 2016, we mapped the fjord with multibeam bathymetry and high‐resolution seismic data. Landslide and post‐landslide deposits extend 6 km downfjord, are up to 70±11‐m thick, and have a total volume of ~147 million m3. Seismic data image a blocky landslide unit and two units deposited immediately after the landslide. The blocky landslide unit is ~65 million m3. We infer it consists dominantly of subaerially derived material and secondarily of fjord‐floor sediment. The overlying units are likely megaturbidites presumably deposited within minutes‐to‐days after the landslide. We infer these deposits dominantly consist of fjord‐floor material mobilized and suspended as the slide entered and traveled downfjord. The lower post‐landslide unit is up to 35±6‐m thick, and the upper unit is up to 12±3‐m thick. These deposits are distinctive and will leave a lasting record of the event. This subaerial‐to‐submarine landslide deposit is distinct from other submarine landslide deposits studied in Alaskan fjords because it has a much greater thickness, larger and more angular blocks, distinctive post‐landslide megaturbidites, and a higher‐amplitude acoustic signature of the blocky deposit. The tight constraints on the landslide source and deposit volumes, topography, bathymetry, and tsunami runup heights and flow directions, should make this a benchmark site for landslide‐tsunami models.
P. J. Haeussler; S. P. S. Gulick; N. McCall; M. Walton; R. Reece; C. Larsen; D. H. Shugar; M. Geertsema; J. G. Venditti; K. Labay. Submarine Deposition of a Subaerial Landslide in Taan Fiord, Alaska. Journal of Geophysical Research: Earth Surface 2018, 123, 2443 -2463.
AMA StyleP. J. Haeussler, S. P. S. Gulick, N. McCall, M. Walton, R. Reece, C. Larsen, D. H. Shugar, M. Geertsema, J. G. Venditti, K. Labay. Submarine Deposition of a Subaerial Landslide in Taan Fiord, Alaska. Journal of Geophysical Research: Earth Surface. 2018; 123 (10):2443-2463.
Chicago/Turabian StyleP. J. Haeussler; S. P. S. Gulick; N. McCall; M. Walton; R. Reece; C. Larsen; D. H. Shugar; M. Geertsema; J. G. Venditti; K. Labay. 2018. "Submarine Deposition of a Subaerial Landslide in Taan Fiord, Alaska." Journal of Geophysical Research: Earth Surface 123, no. 10: 2443-2463.
Marten Geertsema. Quick Clay. Encyclopedia of Solid Earth Geophysics 2018, 739 -740.
AMA StyleMarten Geertsema. Quick Clay. Encyclopedia of Solid Earth Geophysics. 2018; ():739-740.
Chicago/Turabian StyleMarten Geertsema. 2018. "Quick Clay." Encyclopedia of Solid Earth Geophysics , no. : 739-740.
B. Menounos; Alexandre Bevington; Marten Geertsema. Glacier Environments. Encyclopedia of Solid Earth Geophysics 2018, 421 -425.
AMA StyleB. Menounos, Alexandre Bevington, Marten Geertsema. Glacier Environments. Encyclopedia of Solid Earth Geophysics. 2018; ():421-425.
Chicago/Turabian StyleB. Menounos; Alexandre Bevington; Marten Geertsema. 2018. "Glacier Environments." Encyclopedia of Solid Earth Geophysics , no. : 421-425.
The Lakelse Lake area in northwestern British Columbia, Canada, has a long history, and prehistory, of rapid sensitive clay landslides moving on very low gradients. However, until now, many landslides have gone undetected. We use an array of modern tools to identify hitherto unknown or poorly known landslide deposits, including acoustic subbottom profiles, multibeam sonar, and LiDAR. The combination of these methods reveals not only landslide deposits, but also geomorphic and sedimentologic structures that give clues about landslide type and mode of emplacement. LiDAR and bathymetric data reveal the areal extent of landslide deposits as well as the orientation of ridges that differentiate between spreading and flowing kinematics. The subbottom profiles show two-dimensional structures of disturbed landslide deposits, including horst and grabens indicative of landslides classified as spreads. A preliminary computer tomography (CT) scan of a sediment core confirms the structures of one subbottom profile. We also use archival data from the Ministry of Transportation and Infrastructure and resident interviews to better characterize historic landslides.
Marten Geertsema; Andrée Blais-Stevens; Eva Kwoll; Brian Menounos; Jeremy G. Venditti; Alain Grenier; Kelsey Wiebe. Sensitive clay landslide detection and characterization in and around Lakelse Lake, British Columbia, Canada. Sedimentary Geology 2018, 364, 217 -227.
AMA StyleMarten Geertsema, Andrée Blais-Stevens, Eva Kwoll, Brian Menounos, Jeremy G. Venditti, Alain Grenier, Kelsey Wiebe. Sensitive clay landslide detection and characterization in and around Lakelse Lake, British Columbia, Canada. Sedimentary Geology. 2018; 364 ():217-227.
Chicago/Turabian StyleMarten Geertsema; Andrée Blais-Stevens; Eva Kwoll; Brian Menounos; Jeremy G. Venditti; Alain Grenier; Kelsey Wiebe. 2018. "Sensitive clay landslide detection and characterization in and around Lakelse Lake, British Columbia, Canada." Sedimentary Geology 364, no. : 217-227.
A. Dufresne; M. Geertsema; Dan Shugar; M. Koppes; B. Higman; P.J. Haeussler; C. Stark; J.G. Venditti; D. Bonno; C. Larsen; Sean Gulick; N. McCall; Maureen Walton; M.G. Loso; M.J. Willis. Sedimentology and geomorphology of a large tsunamigenic landslide, Taan Fiord, Alaska. Sedimentary Geology 2018, 364, 302 -318.
AMA StyleA. Dufresne, M. Geertsema, Dan Shugar, M. Koppes, B. Higman, P.J. Haeussler, C. Stark, J.G. Venditti, D. Bonno, C. Larsen, Sean Gulick, N. McCall, Maureen Walton, M.G. Loso, M.J. Willis. Sedimentology and geomorphology of a large tsunamigenic landslide, Taan Fiord, Alaska. Sedimentary Geology. 2018; 364 ():302-318.
Chicago/Turabian StyleA. Dufresne; M. Geertsema; Dan Shugar; M. Koppes; B. Higman; P.J. Haeussler; C. Stark; J.G. Venditti; D. Bonno; C. Larsen; Sean Gulick; N. McCall; Maureen Walton; M.G. Loso; M.J. Willis. 2018. "Sedimentology and geomorphology of a large tsunamigenic landslide, Taan Fiord, Alaska." Sedimentary Geology 364, no. : 302-318.
We examined seven landslide dams and their changes over time in the Peace River region of Canada. These landslides had subchannel rupture surfaces in glacial and glaciolacustrine sediments. We assessed the stability of the dams using 6 separate, morphometric-based stability indices (with a total of 10 stability thresholds). The landslides caused the streambeds to be elevated from 4 to 30 m forming the dams. The landslide lakes diminished in size over one to several years through stream incision into the dams and sediment infilling. The longest-lived dam persisted for up to 20 years. For two dams, incision into the dams lowered the lake levels by about half of the total depth, while the remainder of the water in the basins was displaced by sediment infilling. After the lakes drain, the sediment accumulations behind the dams can persist for decades. The stability analyses overpredicted unstable conditions which is inconsistent with the observed longterm persistence of the dams. The landslide dams in our study were relatively stable. Their lakes persisted for up to 2 decades and diminished over time through a combination of slow incision and basin infilling. The stability indices we assessed overpredicted unstable conditions and thus would require modification for these particular types of dams in this regional setting.
Brendan Miller; Anja Dufresne; Marten Geertsema; Nigel Atkinson; Heidi Evensen; David Cruden. Longevity of dams from landslides with sub-channel rupture surfaces, Peace River region, Canada. Geoenvironmental Disasters 2018, 5, 1 .
AMA StyleBrendan Miller, Anja Dufresne, Marten Geertsema, Nigel Atkinson, Heidi Evensen, David Cruden. Longevity of dams from landslides with sub-channel rupture surfaces, Peace River region, Canada. Geoenvironmental Disasters. 2018; 5 (1):1.
Chicago/Turabian StyleBrendan Miller; Anja Dufresne; Marten Geertsema; Nigel Atkinson; Heidi Evensen; David Cruden. 2018. "Longevity of dams from landslides with sub-channel rupture surfaces, Peace River region, Canada." Geoenvironmental Disasters 5, no. 1: 1.