GIS-Based Mapping of Seismic Parameters for the Pyrenees

In the present paper, three of the main seismic parameters, maximum magnitude -Mmax, b-value, and annual rate -AR, have been studied for the Pyrenees range in southwest Europe by a Geographic Information System (GIS). The main aim of this work is to calculate, represent continuously, and analyze some of the most crucial seismic indicators for this belt. To this end, an updated and homogenized Poissonian earthquake catalog has been generated, where the National Geographic Institute of Spain earthquake catalog has been considered as a starting point. Herein, the details about the catalog compilation, the magnitude homogenization, the declustering of the catalog, and the analysis of the completeness, are exposed. When the catalog has been produced, a GIS tool has been used to drive the parameters’ calculations and representations properly. Different grids (0.5 × 0.5° and 1 × 1°) have been created to depict a continuous map of these parameters. The b-value and AR have been obtained that take into account different pairs of magnitude–year of completeness. Mmax has been discretely obtained (by cells). The analysis of the results shows that the Central Pyrenees (mainly from Arudy to Bagnères de Bigorre) present the most pronounced seismicity in the range

Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines

Preparation of landslide susceptibility map is the first step for landslide hazard mitigation and risk assessment. The main aim of this study is to explore potential applications of two new models such as two-class Kernel Logistic Regression (KLR) and Alternating Decision Tree (ADT) for landslide susceptibility mapping at the Yihuang area (China). The ADT has not been used in landslide susceptibility modeling and this paper attempts a novel application of this technique. For the purpose of comparison, a conventional method of Support Vector Machines (SVM) which has been widely used in the literature was included and their results were assessed. At first, a landslide inventory map with 187 landslide locations for the study area was constructed from various sources. Landslide locations were then spatially randomly split in a ratio of 70/30 for building landslide models and for the model validation. Then a spatial database with a total of fourteen landslide conditioning factors was prepared, including slope, aspect, altitude, topographic wetness index (TWI), stream power index (SPI),sediment transport index (STI), plan curvature, land use, normalized difference vegetation index (NDVI), lithology, distance to faults, distance to rivers, distance to roads, and rainfall. Using the KLR, the SVM, and the ADT, three landslide susceptibility models were constructed using the training dataset. The three resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and five statistical evaluation measures. In addition, pairwise comparisons of the area under the ROC curve were carried out to assess if there are significant differences on the overall performance of the three models. The goodness-of- fits are 92.5%(the KLR model), 88.8% (the SVM model), and 95.7% (the ADT model). The prediction capabilities are 81.1%,84.2%, and 93.3% for the KLR, the SVM, and the ADT models, respectively. The result shows that the ADT model yielded better overall performance and accurate results than the KLR and SVM models. The KLR model considered slightly better than SVM model in terms of the positive prediction values. The ADT and KLR are the two promising data mining techniques which might be considered to use in landslide susceptibility mapping. The results from this study may be useful for land use planning and decision making in landslide prone areas

Reducing the impacts of intra-class spectral variability on the accuracy of soft classification and super-resolution mapping of shoreline

The main objective of this research is to assess the impact of intra-class spectral variation on the accuracy of soft classification and super-resolution mapping. The accuracy of both analyses was negatively related to the degree of intra-class spectral variation, but the effect could be reduced through use of spectral sub-classes. The latter is illustrated in mapping the shoreline at a sub-pixel scale from Landsat ETM+ data. Reducing the degree of intra-class spectral variation increased the accuracy of soft classification, with the correlation between predicted and actual class coverage rising from 0.87 to 0.94, and super-resolution mapping, with the RMSE in shoreline location decreasing from 41.13 m to 35.22 m

Assessment and Simulation of Impacts of Climate Change on Erosion and Water Flow by Using the Soil and Water Assessment Tool and GIS: Case Study in Upper Cau River basin in Vietnam

The Upper Cau river basin which plays an important role in socio-economic developments the North of Vietnam is sensitive to changes of climate influencing flows, erosion, and water resources. The main objective of this study is to assess and simulate impacts of climate change on erosion and water flow in the basin. Using a GIS database and Soil and Water Assessment Tool (SWAT) model, the water flow and soil loss were assessed with data in period 1980-1999 called the based period, then simulated until 2100 considering the medium emission scenario (B2). The simulation result showed that the total annual runoff and soil loss tends to increase compared to the base period. For flow, the change rate of the simulation period is higher than the base period; the water flow rate will increase by 0.22% (2020-2039) and up to 1.37% (2080-2100). The total annual soil loss of the simulation period at Gia Bay station tends to increase steadily compared to the baseline, namely by 6.2% (2020-2039) and 25.5% (2080-2100). Overall, the result in this study shows that effects of climate changes on the basin are severe enough under the scenario B2 which is useful for authorities for basin management.ReferencesAli R., McFarlane D., Varma S., Dawes W., Emelyanova I., Hodgson G., Charles S., 2012. Potential climate change impacts on groundwater resources of south-western Australia. Journal of Hydrology, 475, 456-472. doi: http://dx.doi.org/10.1016/j.jhydrol.2012.04.043 Arnell N. W., 2004. Climate change and global water resources: SRES emissions and socio-economic scenarios. Global Environmental Change, 14(1), 31- 52. doi:10.1016/j.gloenvcha.2003.10.006 Arnold J. G., Fohrer N., 2005. SWAT2000: Current capabilities and research opportunities in applied watershed modeling. Hydrol. Proc., 19(3), 563-572. Arnold J.G., Kiniry J.R., Srinivasan R., Williams J.R., Haney E.B., Neitsch S.L., 2012. Soil and water assessment tool. Input/output Documentation: Texas Water Resources Institute. Beare S., Heaney A., 2002. Climate Change and water resources in the Murray Darling Basin, Australia, impacts and possible adaptation. Paper presented at the World Congress of Environmental and Resource Economists, Monterey, California, USA. Binh N.D., Tuan N.A., Huong H.L., 2010. SWAT application coupled with web technologies for soil erosion assessment in north western region of Vietnam. Paper presented at the International SWAT Conference Mayfield Hotel. Seoul, South Korea: Hanoi University of Algriculture. Chau T.L.M., Tuan N.Q., 2011. Application of SWAT for soil erosion management at river subbasins in Duong Hoa commune, Huong Thuy town, Thua Thien Hue province. Paper presented at the 3rd National GIS conference Danang University of Education, Danang, Vietnam. CLIMsystems. http://www.climsystems.com/simclim/. Department of Geography, L. U. SDSM Statistical Downscaling Model: http://copublic.lboro.ac.uk/cocwd/SDSM/software.html. FAO. http://www.fao.org/land-water/databases-and-software/cropwat/en/. Hanratty M.P., Stefan H.G., 1998. Simulating climate change effects in a Minnesota agricultural watershed. J. Environ. Qual., 27, 1524-1532. IPCC, 2000. Special Report on Emissions Scenarios. United States of America. IPCC, 2007. Fourth Assessment Report: Climate Change 2007 (AR4). Li Y., Chen B.M., Wang Z.G., Peng S.L., 2011. Effects of temperature change on water discharge, and sediment and nutrient loading in the lower Pearl River basin based on SWAT modeling. Hydrolog. Sci. J., 56, 68-83. Liem N.D., Hong N.T., Minh T.P., Loi N.K., 2011. Assessing water discharge in Be river basin, Vietnam using GIS and SWAT model. Paper presented at the National GIS application Vietnam. http://gisnetwork.vn/wpcontent/uploads/2012/04/GIS2011_BAI1.swf. McBean E., Motiee H., 2008. Assessment of impact of climate change on water resources: a long term analysis of the Great Lakes of North America. Hydrology and Earth System Sciences, 12, 239-255. MONRE, 2009. Climate Change, Sea level rise scenarios for Vietnam.  Vietnam. MONRE, 2012. Climate Change, Sea level rise scenarios for Vietnam.  Vietnam. MONRE, 2016. Climate Change, Sea level rise scenarios for Vietnam.  Vietnam. Nhu N.Y., 2011. Researching on the impacts of Climate Change on the extreme of the flow on Nhue-Day rivers basin, Hanoi. (Master), Hanoi University of Science, Hanoi National University, Vietnam. Ouyang W., Gao X., Hao Z., Liu H., Shi Y., Hao F., 2017. Farmland shift due to climate warming and impacts on temporal-spatial distributions of water resources in a middle-high latitude agricultural watershed. Journal of Hydrology, 547, 156-167.  http://dx.doi.org/10.1016/j.jhydrol.2017.01.050 Phan D.B., Wu C.C., Hsieh S.C., 2011. Impact of Climate Change and Deforestation on Stream Discharge and Sediment Yield in Phu Luong Watershed, Viet Nam Environmental Science and Engineering, 5, 1063-1072. Phan D.B., Wu C.C., Hsieh S.C., 2011. Impact of Climate Change on Stream Discharge and Sediment Yield in Northern Vietnam. Water Resources, 38(6), 827-836. doi: 10.1134/S0097807811060133. Rossi C.G., Srinivasan R., Jirayoot K., Duc T.L., Souvannabouth P., Binh N.D., Gassman P.W., 2009. Hydrologic evaluation of the lower Mekong river basin with the soil and water assessment tool model. International Agricultural Engineering, 18, 1-13. Son N.T., Tuan N.C., Hang V.T., Nhu N.Y., 2011. Impact of climate change on water resources to transform Nhue-Day rivers basin. Natural and Technological Science, 27, 218-226. Thang T.Q., 2010. Application of remote sensing images and GIS technique to assess soil erosion in Tam Nong Commune, Phu Tho province. Master. Hanoi University of Agriculture, Hanoi. Trong T.D., Viet N.Q., Huong D.T.V., 2012. Assessing the soil erosion possibility in Dakrong Commune, Quang Tri province using RMMF (Rrevised Morgan-Morgan-Finney) model. Scientific journal, Hue University,Vietnam, 74A(5), 173-184. Tu L.H., Liem N.D., Minh T.P., Loi N.K., 2011. Assessing soil erosion in Da Tam watershed, Lam Dong province using GIS technique Paper presented at the National GIS application  Danang, Vietnam. Penginapan Ciawi. (2020). Retrieved 21 May 2020, from http://www.penginapanciawi.my.id/ Vargas-Amelin E., Pindado P., 2014. The challenge of climate change in Spain: Water resources, agriculture and land. Journal of Hydrology, 518, Part B, 243-249. http://dx.doi.org/10.1016/j.jhydrol.2013.11.035 Winchell M., Srinivasan R., Di Luzio M., Arnold J., 2013. ArcSWAT Interface for SWAT 2012. User's Guide. Texas: Blackland Research and Extension Center; Grassland Soil and Water research laboratory

GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks

The main objective of this study is to propose and verify a novel ensemble methodology that could improve prediction performances of landslide susceptibility models. The proposed methodology is based on the functional tree classifier and three current state-of-the art machine learning ensemble frameworks, Bagging, AdaBoost, and MultiBoost. According to current literature, these methods have been rarely used for the modeling of rainfall-induced landslides. The corridor of the National Road 32 (Vietnam) was selected as a case study. In the first stage, the landslide inventory map with 262 landslide polygons that occurred during the last 20 years was constructed and then was randomly partitioned into a ratio of 70/30 for training and validating the models. Second, ten landslide conditioning factors were prepared such as slope, aspect, relief amplitude, topographic wetness index, topographic shape, distance to roads, distance to rivers, distance to faults, lithology, and rainfall. The model performance was assessed and compared using the receiver operating characteristic and statistical evaluation measures. Overall, the FT with Bagging model has the highest prediction capability (AUC = 0.917), followed by the FT with MultiBoost model (AUC = 0.910), the FT model (AUC = 0.898), and the FT with AdaBoost model (AUC = 0.882). Compared with those derived from popular methods such as J48 decision trees and artificial neural networks, the performance of the FT with Bagging model is better. Therefore, it can be concluded that the FT with Bagging is promising and could be used as an alternative in landslide susceptibility assessment. The result in this study is useful for land use planning and decision making in landslide prone areas

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree

Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping

Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines

The main objective of this study is to investigate the potential application of GIS-based Support Vector Machines (SVM) with four kernel functions, i.e., radial basis function (RBF), polynomial (PL), sigmoid (SIG), and linear (LN) for landslide susceptibility mapping at Luxi city in Jiangxi province, China. At the first stage of the study, a landslide inventory map with 282 landslide locations was identified using aerial photographs, satellite images, and field surveys. Of this, 70 % of the landslides (196 landslide locations) are used as a training dataset and the rest (86 landslide locations) were used as the validation dataset. Then, 15 landslide conditioning factors were prepared, i.e., altitude, aspect, slope, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), plan curvature, profile curvature, distance from river, distance from road, distance from fault, lithology, land use, NDVI, and rainfall. Using these conditioning factors, landslide susceptibility indexes were calculated using SVM with the four kernel functions. Subsequently, the results were exported and plotted in ArcGIS and four landslide susceptibility maps were produced. The four susceptibility maps were validated and compared using the landslide locations and the success rate and prediction rate methods. The validation results showed that success rates for the four SVM models are 82.0 % (RBF), 83.0 % (PL), 45.0 % (SIG), and 70.0 % (LN). The prediction rates for the four SVM models are 81.0 % (RBF), 71.0 % (PL), 40.0 % (SIG), and LN 63.0 % (SIG). The result shows that the RBF-SVM model has the highest overall performance. The produced susceptibility maps may be useful for general land-use planning in landslides

Deformation forecasting of a hydropower dam by hybridizing a long short-term memory deep learning network with the coronavirus optimization algorithm

The safety operation and management of hydropower dam play a critical role in social-economic development and ensure people’s safety in many countries; therefore, modeling and forecasting the hydropower dam’s deformations with high accuracy is crucial. This research aims to propose and validate a new model based on deep learning long short-term memory (LSTM) and the coronavirus optimization algorithm (CVOA), named CVOA-LSTM, for forecasting the defor mations of the hydropower dam. The second-largest hydropower dam of Viet nam, located in the Hoa Binh province, is focused. Herein, we used the LSTM to establish the deformation model, whereas the CVOA was utilized to opti mize the three parameters of the LSTM, the number of hidden layers, the learn ing rate, and the dropout. The efficacy of the proposed CVOA-LSTM model is assessed by comparing its forecasting performance with state-of-the-art bench marks, sequential minimal optimization for support vector regression, Gaussian process, M5’ model tree, multilayer perceptron neural network, reduced error pruning tree, random tree, random forest, and radial basis function neural net work. The result shows that the proposed CVOA-LSTM model has high fore casting capability (R2 = 0.874, root mean square error = 0.34, mean absolute error = 0.23) and outperforms the benchmarks. We conclude that CVOA-LSTM is a new tool that can be considered to forecast the hydropower dam’s deforma tions.Ministerio de Ciencia, Innovación y Universidades PID2020-117954RB-C2

Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, the Hai Phong city (Vietnam) using GIS-Based Kernel Logistic Regression

-The Cat Ba National Park area (Vietnam) with the tropical forest is recognized to be part of the world biodiversity conservation by United Nations Educational, Scientific and Cultural Oranization (UNESCO) and is a well-known destination for tourist with around 500,000 travellers per year. This area has been the site for many research projects; however no project has been carried out for the forest fire susceptibility assessment. Thus, protection of the forest including fire prevention is one of the main concerns of the local authority. This work aims to produce a tropical forest fire susceptibility map for the Cat Ba National Park area, which may be helpful for the local authority in the forest fire protection management. To obtain this purpose, first, historical forest fires and related factors were collected from various sources to construct a GIS database. Then a forest fire susceptibility model was developed using Kernel logistic regression. The quality of the model was assessed using the Receiver Operating Characteristic (ROC) curve, area under the ROC curve (AUC), and five statistical evaluation measures. The usability of the resulting model is further compared with a benchmark model, the Support vector machine. The results show that the Kernel logistic regression model has high performance on both the training and validation dataset with a prediction capability of 92.2%. Since the Kernel logistic regression model outperform the benchmark model, we conclude that the proposed model is a promising alternative tool that should be considered for forest fire susceptibility mapping also for other areas. The result in this study is useful for the local authority in forest planning and management

A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam)

The main objective of this study is to investigate potential application of an integrated evidential belief function (EBF)-based fuzzy logic model for spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam). First, a landslide inventory map was constructed from various sources. Then the landslide inventory map was randomly partitioned as a ratio of 70/30 for training and validation of the models, respectively. Second, six landslide conditioning factors (slope angle, slope aspect, lithology, distance to faults, soil type, land use) were prepared and fuzzy membership values for these factors classes were estimated using the EBF. Subsequently, fuzzy operators were used to generate landslide susceptibility maps. Finally, the susceptibility maps were validated and compared using the validation dataset. The results show that the lowest prediction capability is the fuzzy SUM (76.6%). The prediction capability is almost the same for the fuzzy PRODUCT and fuzzy GAMMA models (79.6%). Compared to the frequency-ratio based fuzzy logic models, the EBF-based fuzzy logic models showed better result in both the success rate and prediction rate. The results from this study may be useful for local planner in areas prone to landslides. The modelling approach can be applied for other areas