GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

In this study, a novel approach of the landslide numerical risk factor(LNRF) bivariate model was used in ensemble with linear multivariate regression(LMR) and boosted regression tree(BRT) models, coupled with radar remote sensing data and geographic information system(GIS), for landslide susceptibility mapping(LSM) in the Gorganroud watershed, Iran. Fifteen topographic, hydrological, geological and environmental conditioning factors and a landslide inventory(70%, or 298 landslides) were used in mapping. Phased array-type L-band synthetic aperture radar data were used to extract topographic parameters. Coefficients of tolerance and variance inflation factor were used to determine the coherence among conditioning factors. Data for the landslide inventory map were obtained from various resources, such as Iranian Landslide Working Party(ILWP), Forestry, Rangeland and Watershed Organisation(FRWO), extensive field surveys, interpretation of aerial photos and satellite images, and radar data. Of the total data, 30% were used to validate LSMs, using area under the curve(AUC), frequency ratio(FR) and seed cell area index(SCAI).Normalised difference vegetation index, land use/land cover and slope degree in BRT model elevation, rainfall and distance from stream were found to be important factors and were given the highest weightage in modelling. Validation results using AUC showed that the ensemble LNRF-BRT and LNRFLMR models(AUC = 0.912(91.2%) and 0.907(90.7%), respectively) had high predictive accuracy than the LNRF model alone(AUC = 0.855(85.5%)). The FR and SCAI analyses showed that all models divided the parameter classes with high precision. Overall, our novel approach of combining multivariate and machine learning methods with bivariate models, radar remote sensing data and GIS proved to be a powerful tool for landslide susceptibility mapping.     

Geotechnical assessment of cut slopes in the landslide-prone Himalayas: rock mass characterization and simulation approach

Natural Hazards - The roadway networks serve as arteries for the ongoing socio-economic activities within the Himalayan region. The perilous conditions of geologically active and fragile terrain...     

Interacting system NGC 7805/6 (Arp 112) and its tidal dwarf galaxy candidate

We present results from our Giant Metrewave Radio Telescope(GMRT)HⅠ,Himalayan Chandra Telescope(HCT)Hα,1 m Sampurnanand Telescope(ST)and 1.3 m Devasthal Fast Optical Telescope(DFOT)deep optical observations of the NGC 7805/6(Arp 112)system to test KUG 2359+311’s tidal dwarf galaxy(TDG)candidacy and explore the properties of the interacting system.Our GMRT HⅠmap shows no HⅠdetection associated with KUG 2359+311,nor any HⅠtail or bridge-like structure connecting KUG 2359+311 to the NGC 7805/6 system.Our HCT Hαimage,on the other hand,displays strong detections in KUG 2359+311,with net SFR0.035±0.009 M_⊙yr^-1.The Hαdata constrain the redshift of KUG 2359+311 to 0.00≤z≤0.043,compared to the redshift of NGC 7806 of0.015.TDGs detected to date have all been HⅠrich,and displayed HⅠ,ionised gas and stellar tidal debris trails(bridges or tails)linking them to their parent systems.However,neither our HⅠdata nor our optical imaging,while three magnitudes deeper than SDSS,reveals a tidal trail connecting KUG 2359+311 to NGC 7805/6.Lack of HⅠ,presence of an old stellar population,ongoing star formation and reasonably high SFR compared to normal dwarf galaxies suggest that KUG 2359+311 may not be an Arp 112 TDG.It is most likely a case of a regular gas-rich dwarf galaxy undergoing a morphological transformation after having lost its entire gas content to an interaction with the Arp 112 system.Redshift and metallicity from future spectroscopic observations of KUG 2359+311 would help clarify the nature of this enigmatic structure.     

Improving the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three machine learning approaches

The main objective of this study is to integrate adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural network (ANN) to design an integrated supervised committee machine artificial intelligence (SCMAI) model to spatially predict the groundwater vulnerability to seawater intrusion in Gharesoo-Gorgan Rood coastal aquifer placed in the northern part of Iran. Six hydrological GALDIT parameters (i.e., G groundwater occurrence, A aquifer hydraulic conductivity, L level of groundwater above sea level, D distance from the shore, I impact of the existing status of seawater intrusion in the region, and T thickness of the aquifer) were considered as inputs for each model. In the training step, the values of GALDIT’s vulnerability index were conditioned by using the values of TDS concentration in order to obtain the conditioned vulnerability index (CVI). The CVI was considered as the target for each model. After training the models, each model was tested using a separate TDS dataset. The results indicated that the ANN and ANFIS algorithms performed better than the SVM algorithm. The values of correlation were obtained as 88, 87, and 80% for ANN, ANFIS, and SVM models, respectively. In the testing step of the SCMAI model, the values of RMSE, R², and r were obtained as 6.4, 0.95, and 97%, respectively. Overall, SCMAI model outperformed other models to spatially predicting vulnerable zones. The result of the SCMAI model confirmed that the western zones along the shoreline had the highest vulnerability to seawater intrusion; therefore, it seems critical to consider emergency protection plans for study area.

Graphic abstract