Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province,Vietnam

Natural Hazards - Vietnam’s central coastal region is the most vulnerable and always at flood risk, severely affecting people’s livelihoods and socio-economic development. In...     

Electroconducting Elements of the Ultradeep Fluid–Fault Systems as Indicators of Seismically Active Zones of the Eastern Margin of the Eurasian Continent: Evidence from Magnetotelluric Data

Doklady Earth Sciences - A fundamentally new conclusion that zones of high seismic activity are controlled by electroconducting (fluid-saturated) fragments rather than by the entire deep fault is...     

Locally weighted learning based hybrid intelligence models for groundwater potential mapping and modeling: A case study at Gia Lai province,Vietnam

The groundwater potential map is an important tool for a sustainable water management and land use planning,particularly for agricultural countries like Vietnam. In this article, we proposed new machine learning ensemble techniques namely Ada Boost ensemble(ABLWL), Bagging ensemble(BLWL), Multi Boost ensemble(MBLWL),Rotation Forest ensemble(RFLWL) with Locally Weighted Learning(LWL) algorithm as a base classifier to build the groundwater potential map of Gia Lai province in Vietnam. For this study, eleven conditioning factors(aspect, altitude, curvature, slope, Stream Transport Index(STI), Topographic Wetness Index(TWI), soil, geology,river density, rainfall, land-use) and 134 wells yield data was used to create training(70%) and testing(30%)datasets for the development and validation of the models. Several statistical indices were used namely Positive Predictive Value(PPV), Negative Predictive Value(NPV), Sensitivity(SST), Specificity(SPF), Accuracy(ACC),Kappa, and Receiver Operating Characteristics(ROC) curve to validate and compare performance of models. Results show that performance of all the models is good to very good(AUC: 0.75 to 0.829) but the ABLWL model with AUC = 0.89 is the best. All the models applied in this study can support decision-makers to streamline the management of the groundwater and to develop economy not only of specific territories but also in other regions across the world with minor changes of the input parameters.     

Flash flood susceptibility mapping using a novel deep learning model based on deep belief network,back propagation and genetic algorithm

Flash floods are responsible for loss of life and considerable property damage in many countries.Flood susceptibility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately used by landuse planners and emergency managers.The main objective of this study is to prepare an accurate flood susceptibility map for the Haraz watershed in Iran using a novel modeling approach(DBPGA) based on Deep Belief Network(DBN) with Back Propagation(BP) algorithm optimized by the Genetic Algorithm(GA).For this task, a database comprising ten conditioning factors and 194 flood locations was created using the One-R Attribute Evaluation(ORAE) technique.Various well-known machine learning and optimization algorithms were used as benchmarks to compare the prediction accuracy of the proposed model.Statistical metrics include sensitivity,specificity accuracy, root mean square error(RMSE), and area under the receiver operatic characteristic curve(AUC) were used to assess the validity of the proposed model.The result shows that the proposed model has the highest goodness-of-fit(AUC = 0.989) and prediction accuracy(AUC = 0.985), and based on the validation dataset it outperforms benchmark models including LR(0.885), LMT(0.934), BLR(0.936), ADT(0.976), NBT(0.974), REPTree(0.811), ANFIS-BAT(0.944), ANFIS-CA(0.921), ANFIS-IWO(0.939), ANFIS-ICA(0.947), and ANFIS-FA(0.917).We conclude that the DBPGA model is an excellent alternative tool for predicting flash flood susceptibility for other regions prone to flash floods.     

The sediment load and deposition by river discharge and their relation to organochlorine pesticides pollutants in the sediment bottom of Nha Trang Bay,Vietnam

Ocean Science Journal - Based on previous results from the “River reef impact studies project that was carried out as a co-operation programme between ZMT (Germany) and NIO (Vietnam) from...     

Seismic ground-roll noise attenuation using deep learning

We propose to adopt a deep learning based framework using generative adversarial networks for ground-roll attenuation in land seismic data. Accounting for the non-stationary properties of seismic data and the associated ground-roll noise, we create training labels using local time–frequency transform and regularized non-stationary regression. The basic idea is to train the network using a few shot gathers such that the network can learn the weights associated with noise attenuation for the training shot gathers. We then apply the learned weights to test ground-roll attenuation on shot gathers, that are not a part of training input to obtain the desired signal. This approach gives results similar to local time–frequency transform and regularized non-stationary regression but at a significantly reduced computational cost. The proposed approach automates the ground-roll attenuation process without requiring any manual input in picking the parameters for each shot gather other than in the training data. Tests on field-data examples verify the effectiveness of the proposed approach.     

Ionosphere probing with simultaneous GNSS radio occultations

GPS Solutions - Radio occultation (RO) is a powerful technique for providing vertical profiles of refractivity, temperature, pressure, and water vapor of the neutral atmosphere and electron density...     

Electrical conductivity and crustal structure beneath the central Hellenides around the Gulf of Corinth (Greece) and their relationship with the seismotectonics

A deep magnetotelluric sounding (MTS) investigation in the western part of the Gulf of Corinth has revealed a complex electrical image of the crustal structure. The geotectonic structure of the Parnassos unit and the Transition zone in the central Hellenides, overthrusting the Pindos zone both towards the west and towards the south, has been clearly identified by its higher resistivity and its intrinsic anisotropy related to the N–S strike of the Hellenides range. Subsequent N–S extension of the Gulf introduced another heterogeneous anisotropy characteristic that corresponds to E–W-trending normal faults on both sides of the Gulf. The 2-D modelling of the MTS results reveals the existence of a relatively conductive layer about 4 km thick at a depth greater than 10 km in the middle crust. It corresponds to a ductile detachment zone suggested by microseismic and seismic studies ( King et al . 1985 ; Rigo et al . 1996 ; Bernard et al . 1997a ). It may be attributed to the phyllite series lying between the allochthonous Hellenic nappes and the autochthonous Plattenkalk basement. Towards the east, under the Pangalos peninsula, approaching the internal Hellenides, the detachment zone could root deeply into the lower crust.
Some strong local electrical anomalies are observed, reaching the conductive layer in the middle crust, such as that under the Mamousia fault and under the front of the overthrust of the Transition zone on the Pindos zone. Other anomalies affect only the shallower zones such as that beneath the Helike fault and in the Psaromita peninsula. These shallower anomalies provide complementary information to the study of spatial and temporal variations of the seismic anisotropy in relation to the short- and long-term tectonic activity of the Gulf ( Bouin et al . 1996 ; Gamar et al . 1999 ).     

Lang Trang Cave (Northern Vietnam): Pleistocene Mammalian Locality with Gigantopithecus Finds

Doklady Earth Sciences - Speleological, geological and paleontological characteristics of the Lang Trang cave in northern Vietnam are presented. Primates Gigantopithecus blacki von Koenigswald,...