Determining the role of lineaments in underground hydrodynamics using Landsat 7 ETM+ data,case of the Chott El Gharbi Basin (western Algeria)

This paper attempts an overview of the application of remote sensing to groundwater studies. Its objective is to define the role of the geological features in the underground hydrodynamic in the aquifer system of the Chott El Gharbi Basin (Algerian western high plains) and identify a link between the fracturing and the meteoric water supply of this deep aquifer. The methodology followed consists to study the fracturing map of studied area which is obtained after Landsat 7 ETM+ processing images. It is based on structural lineaments mapping. The obtained map has been validated by geophysical results and geological map. Statistical analysis of the lineaments network shows the presence of about 537 lineaments divided into four families oriented according to the following directions NE-SW, NW-SE, N-S, and E-W. The lineament analysis of the studied basin provides important information on subsurface fractures that may control the circulation and storage of groundwater. These fractures have an undeniable hydrogeological interest because of their size, a priori favorable for the aquifers recharge in the region. The probable link between the Chott El Gharbi implementation and the presence of mega fractures which some of them correspond actually to Wadis is confirmed. The correlation between the productivity of high debit drillings and the closest lineament confirms that these lineaments are surface traces of regional discontinuities and act as main groundwater flow paths.     

Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed,Iran

The purpose of current study is to produce groundwater qanat potential map using frequency ratio (FR) and Shannon's entropy (SE) models in the Moghan watershed, Khorasan Razavi Province, Iran. The qanat is basically a horizontal, interconnected series of underground tunnels that accumulate and deliver groundwater from a mountainous source district, along a water- bearing formation (aquifer), and to a settlement. A qanat locations map was prepared for study area in 2013 based on a topographical map at a 1:50,000-scale and extensive field surveys. 53 qanat locations were detected in the field surveys. 70 % (38 locations) of the qanat locations were used for groundwater potential mapping and 30 % (15 locations) were used for validation. Fourteen effective factors were considered in this investigation such as slope degree, slope aspect, altitude, topographic wetness index (TWI), stream power index (SPI), slope length (LS), plan curvature, profile curvature, distance to rivers, distance to faults, lithology, land use, drainage density, and fault density. Using the above conditioning factors, groundwater qanat potential map was generated implementing FR and SE models, and the results were plotted in ArcGIS. The predictive capability of frequency ratio and Shannon's entropy models were determined by the area under the relative operating characteristic curve. The area under the curve (AUC) for frequency ratio model was calculated as 0.8848. Also AUC for Shannon's entropy model was 0.9121, which depicts the excellence of this model in qanat occurrence potential estimation in the study area. So the Shannon's entropy model has higher AUC than the frequency ratio model. The produced groundwater qanat potential maps can assist planners and engineers in groundwater development plans and land use planning.     

Location-allocation modeling for emergency evacuation planning with GIS and remote sensing: A case study of Northeast Bangladesh

This work developed models to identify optimal spatial distribution of emergency evacuation centers(EECs) such as schools, colleges, hospitals, and fire stations to improve flood emergency planning in the Sylhet region of northeastern Bangladesh.The use of location-allocation models(LAMs) for evacuation in regard to flood victims is essential to minimize disaster risk.In the first step, flood susceptibility maps were developed using machine learning models(MLMs), including: Levenberg–Marquardt back propagation(LM-BP) neural network and decision trees(DT) and multi-criteria decision making(MCDM) method.Performance of the MLMs and MCDM techniques were assessed considering the area under the receiver operating characteristic(AUROC) curve.Mathematical approaches in a geographic information system(GIS) for four well-known LAM problems affecting emergency rescue time are proposed: maximal covering location problem(MCLP), the maximize attendance(MA), p-median problem(PMP), and the location set covering problem(LSCP).The results showed that existing EECs were not optimally distributed, and that some areas were not adequately served by EECs(i.e., not all demand points could be reached within a 60-min travel time).We concluded that the proposed models can be used to improve planning of the distribution of EECs, and that application of the models could contribute to reducing human casualties, property losses, and improve emergency operation.     

Projections of temperature changes over South America during the twenty-first century using CMIP6 models

A 22-member ensemble from CMIP6 is used to analyze the projected changes and seasonal behavior in surface air temperature over South America during the twenty-first century. In the future projections, CMIP6 models shown a high dependency to the socioeconomic pathway over each country of South America. The multimodel ensemble projects a continuous increase in the annual mean temperature over South America during the twenty-first century under the three future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5). Besides, it was possible to identify consistent positive trends across all the models, with values between 0.45 ± 0.05 and 2.05 ± 0.31 °C cy⁻¹ under the historical experiment, however largest trends occurs for the projection periods (near, mid and far future), with values between − 0.87 ± 0.84 to 2.88 ± 0.60 °C cy⁻¹ (SSP1-2.6), 1.41 ± 0.88 to 5.32 ± 0.81 °C cy⁻¹ (SSP2-4.5) and 4.75 ± 0.58 to 8.76 ± 0.74 °C cy⁻¹ (SSP5-8.5) with maximum values at Bolivia, Brasil, Paraguay and Venezuela whilst minimum values for Argentina and Uruguay, regardless of the SSP scenario used. From the seasonal behavior analysis was possible to identify maximum values between January and March whilst minimum between June and July, except in Brasil, Venezuela and Guyana–Surinam–French Guayana, with annual range decreasing as the latidude decreases. By the end of the twenty-first century the annual mean temperature over South america is projected to increase between 0.92–2.11 °C, 0.97–3.37 °C and 1.27–6.14 °C under SSP1-2.6, SSP2-4.5 and SSP5-8.5 projection scenarios respectively. This projected increase of temperature across the continent will produce negative repercussions in the social, economic and political spheres. The results obtained in this study provide insights about the CMIP6 performance over this region, which can be used to develop adaptation strategies and might be useful for the adaptation to the climate change.

     

Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin,Egypt

Natural Hazards - Floods represent catastrophic environmental hazards that have a significant impact on the environment and human life and their activities. Environmental and water management in...     

Flood risk mapping and crop-water loss modeling using water footprint analysis in agricultural watershed,northern Iran

Natural Hazards - Spatial information on flood risk and flood-related crop losses is important in flood mitigation and risk management in agricultural watersheds. In this study, loss of water bound...     

Prediction of safety factors for slope stability: comparison of machine learning techniques

Natural Hazards - Because of the disasters associated with slope failure, the analysis and forecasting of slope stability for geotechnical engineers are crucial. In this work, in order to forecast...     

Cracking Problems in the Segments of Tabriz Metro Tunnel: A 3D Computational Study

This paper presents numerical models of tunnel segments with respect to the steel rebars using a finite element method under TBM thrust jack referring to the Tabriz metro line 2. The analyses have been carried out in five different steps. Numerical modellings have been done from the lowest ratio of reinforcement (the weakest case) to the highest ratio of it (the strongest case), which means that the curved longitudinal rebars were only placed within the concrete in the first step of the analysis, then other types of rebars are added gradually. The results indicated that stress contours in the z-direction (S33) of the model was approximately equal in all the steps. While distributed stress indicated more different behavior than the S33, which the maximum compressive and tensile distributed stresses are just applied on the reinforcement. The higher the reinforcement ratio, distributed stresses are increased step by step. So, it may be noticed that the reinforcement ratio was so crucial to the stresses which are needed to bear. A cracking form of the segment has been analyzed using different types of strains. The curved longitudinal rebars have a remarkable impact on cracking form in the z-direction in step 1, so that the magnitude of plastic strain in the z-direction (PE33) was remarkably decreased when they were placed within the concrete. Although adding the other main rebars has a negligible impact on the cracking form in z-direction whereas they can decline the plastic strain in the x-direction (PE11). The rebars which were added to the model in step 3, decrease the cracks around the shoulders of the segment. Nonetheless, all the rebars have not affected the segment behavior. Finally, in step 5, the cracking mechanism between the loading surfaces has completely changed. Also, displacement contours showed that the magnitude is decreased when the rebars are added to the reinforcement in every step.