APG:A novel python-based ArcGIS toolbox to generate absence-datasets for geospatial studies

One important step in binary modeling of environmental problems is the generation of absence-datasets that are traditionally generated by random sampling and can undermine the quality of outputs.To solve this problem,this study develops the Absence Point Generation(APG)toolbox which is a Python-based ArcGIS toolbox for automated construction of absence-datasets for geospatial studies.The APG employs a frequency ratio analysis of four commonly used and important driving factors such as altitude,slope degree,topographic wetness index,and distance from rivers,and considers the presence locations buffer and density layers to define the low potential or susceptibility zones where absence-datasets are gener-ated.To test the APG toolbox,we applied two benchmark algorithms of random forest(RF)and boosted regression trees(BRT)in a case study to investigate groundwater potential using three absence datasets i.e.,the APG,random,and selection of absence samples(SAS)toolbox.The BRT-APG and RF-APG had the area under receiver operating curve(AUC)values of 0.947 and 0.942,while BRT and RF had weaker per-formances with the SAS and Random datasets.This effect resulted in AUC improvements for BRT and RF by 7.2,and 9.7％from the Random dataset,and AUC improvements for BRT and RF by 6.1,and 5.4％from the SAS dataset,respectively.The APG also impacted the importance of the input factors and the pattern of the groundwater potential maps,which proves the importance of absence points in environmental bin-ary issues.The proposed APG toolbox could be easily applied in other environmental hazards such as landslides,floods,and gully erosion,and land subsidence.     

Pore-water pressure development in a frozen saline clay under isotropic loading and undrained shearing

Acta Geotechnica - A systematical testing program on frozen Onsøy clay under isotropic loading and undrained shearing at different temperatures...     

An effective stress-based DSC model for predicting the coefficient of lateral soil pressure in unsaturated soils

Acta Geotechnica - In this study, an analytical model is developed to establish a framework for predicting the coefficient of lateral soil pressure in unsaturated soils. To this end, the disturbed...     

Late quaternary seismic stratigraphy and active faults of the Gulf of İzmit (NE Marmara Sea)

The Upper Quaternary seismic stratigraphy and active faults of the Gulf of İzmit were investigated by means of high-resolution shallow seismic profiling data in the source region of 1999 İzmit earthquake. High-resolution seismic reflection data correlated with borehole data indicate that the stratigraphy of İzmit Bay consists of three distinct depositional sequences formed in response to middle Pleistocene-Holocene sea-level changes. Reflector R, separating the pre-Holocene sequences (1 and 2) from the Holocene sequence (3), represents an erosional unconformity produced by the subaerial fluvial erosion of the continental shelves at the time of the last glacial maximum. Occasional, anomalous reflections (acoustic turbidity) observed within the Holocene sequence are interpreted as gas accumulations. The maximum thickness of the Holocene sediments is found to be about 25 m. The isopach map of Holocene sediment implies that the thickness of the Holocene decreases from the east towards the central and western basins of İzmit Bay. Two distinct fault systems are interpreted in İzmit Bay. The main fault system extending roughly in an E-W direction along the Gulf of İzmit is an active right lateral strike slip fault with a normal component. The secondary faults are normal faults striking in different directions and these are identified as being both active and inactive. In addition, prominent compressive features are identified in the seismic cross-sections of some profiles acquired to the east of Hersek Peninsula where the focal mechanisms of the aftershocks of the 1999 İzmit earthquake also reveal predominantly reverse faulting mechanisms, as identified by a local dense seismic network.     

Using uncertainty and sensitivity analysis for finding the best rainfall-runoff model in mountainous watersheds(Case study: the Navrood watershed in Iran)

In watersheds that have not sufficient meteorological and hydrometric data for simulating rainfall-runoff events, using geomorphologic and geomorphoclimatic characteristics of watershed is a conventional method for the simulation. A number of rainfall-runoff models utilize these characteristics such as Nash-IUH, Clark-IUH, Geomorphologic Instantaneous Unit Hydrograph(GIUH), Geomorphoclimatic Instantaneous Unit Hydrograph(GcIUH), GIUH-based Nash(GIUH-Nash) and GcIUH-based Clark(GcIUH-Clark). But all these models are not appropriate for mountainous watersheds. Therefore, the objective of this study is to select the best of them for the simulation. The procedure of this study is: a) selecting appropriate rainfall-runoff events for calibration and validation of six hybrid models, b) distinguishing the best model based on different performance criteria(Percentage Error in Volume(PEV); Percentage Error in Peak(PEP); Percentage Error in Time to Peak(PETP); Root Mean Square Error(RMSE) and Nash-Sutcliffe model efficiency coefficient(ENS)), c) Sensitivity analysis for determination of the most effective parameter at each model, d) Uncertainty determination of different parameters in each model and confirmation of the obtained results by application of the performance criteria. For application of this procedure, the Navrood watershed in the north of Iran as a mountainous watershed has been considered. The results showed that the ClarkIUH and GcIUH-Clark are suitable models for simulation of flood hydrographs, while other models cannot simulate flood hydrographs appropriately. The sensitivity analysis shows that the most sensitive parameters are the infiltration constant rate and time of concentration in the Clark-IUH model. Also, the most sensitive parameters include the infiltration constant rate and storage coefficient in the GcIUHClark model. The Clark-IUH and GcIUH-Clark models are more sensitive to their parameters. The Latin Hypercube Sampling(LHS) on Monte Carlo(MC) simulation method was used for evaluation of uncertainty of data in rainfall-runoff models. In this method 500 sets of data values are produced and then the peak discharge of flood hydrographs for each produced data set is simulated with rainfall-runoff models. The uncertainty of data changes the value of simulated peak discharge of flood hydrograph. The uncertainty analysis shows that the observed peak discharges of different rainfall-runoff events are within the range of values of simulated by the six hybrid rainfall-runoff models and IUH that inputs of these models were the produced data sets. The range of the produced peak discharge of flood hydrographs by the Clark-IUH and GcIUH-Clark models is wider than those of other models.     

Spatial analysis of water quality and its suitability in farming giant freshwater prawn (Macrobrachium rosenbergii) in Negeri Sembilan region,Peninsular Malaysia

Finding potential sites for resilient prawn production in the tropical environment that also prevents wastage of natural resources is not an easy task. The purpose of this study is to evaluate water quality suitability for prawn farming in Negeri Sembilan of Peninsular Malaysia based on Geographic Information System (GIS). To achieve this goal, numerous criteria including sources of water, water temperature, water pH, sources of pollution, salinity, soil texture and availability of phytoplankton criteria were considered for the modelling process. Analytic Hierarchy Process (AHP) technique was performed to standardize the criteria and the weighting process. The weighted overlay of indicators and results were accomplished by applying the Multi‐Criteria Decision Analysis (MCDA) method in GIS. It was indicated that the Negeri Sembilan area has potential for prawn farming. The results showed that about 25 per cent (163 056.93 ha) of the area was most suitable for prawn farming, about 58 per cent (384 656.88 ha) was considered moderately suitable, while 18 per cent (117 633.49 ha) was regarded as least suitable. The study concluded that the multi‐criteria decision analysis of water quality for prawn farming is vital for regional economic planning in the Negeri Sembilan area and also significant when establishing a model for aquaculture development.     

Use of genetic algorithm in new approach to modeling of flood routing

Journal of Oceanology and Limnology - The hydrological models and simplified methods of Saint-venant equations are used extensively in hydrological modeling, in particular for the simulation of the...     

Experimental Evaluation of A New Developed Algorithm for An Autonomous Surface Vehicle and Comparison with Simulink Results

China Ocean Engineering - Performing repeatable duties automatically was the dreams of human being for centuries. Although full autonomy has long been dreamed of by visionaries, many researches...     

Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings, crowd monitoring has taken a considerable attentions in many disciplines such as psychology, sociology, engineering, and computer vision. This is due to the fact that, monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents (e.g. sports). One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles (UAVs), because UAVs have the capability to acquiring fast, low costs, high-resolution and real-time images over crowd areas. In addition, geo-referenced images can also be provided through integration of on-board positioning sensors (e.g. GPS/IMU) with vision sensors (digital cameras and laser scanner). In this paper, a new testing procedure based on feature from accelerated segment test (FAST) algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions. The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order. A single pixel which takes the ranking number 9 (for FAST-9) or 12 (for FAST-12) was then compared with the center pixel. Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features. The results show that the proposed algorithms are able to extract crowd features from different UAV images. Overall, the values of Completeness range from 55 to 70 % whereas the range of correctness values was 91 to 94 %.     

Statistical analysis of surface urban heat island intensity variations: A case study of Babol city,Iran

The urban heat island is considered as one of the most important climate change phenomena in urban areas, which can result in remarkable negative effects on flora, concentration of pollutants, air quality, energy and water consumption, human health, ecological and economic impacts, and even on global warming. The variation analysis of the surface urban heat island intensity (SUHII) is important for understanding the effect of urbanization and urban planning. The objective of this study was to present a new strategy based on the Shannon’s entropy and Pearson chi-square statistic to investigate the spatial and temporal variations of the SUHII. In this study, Landsat TM, ETM+, OLI and TIRS images, MODIS products, meteorological data, topographic and population maps of the Babol city, Iran, from 1985 to 2017, and air temperature data recorded by ground recorder devices in 2017 were used. First, Single-Channel algorithm was used to estimate land surface temperature (LST), and the maximum likelihood classifier was employed to classify Landsat images. Then, based on LST maps, surface urban heat island ratio index was employed to calculate the SUHII. Further, several statistical methods, such as the degree-of-freedom, degree-of-sprawl and degree-of-goodness, were used to analyse the SUHII variation along different geographic directions and in various time periods. Finally, correlation between various parameters such as air temperature, SUHII, population variation and degree-of-goodness index values were investigated. The results indicated that the SUHII value increased by 24% in Babol over different time periods. The correlation coefficient yielded 0.82 between the values of the difference between the mean air temperature of the urban and suburbs and the SUHII values for the geographic directions. Furthermore, the correlation coefficient between the population variation and the degree-of-goodness index values reached 0.8. The results suggested that the SUHII variation of Babol city had a high degree-of-freedom, high degree-of-sprawl and negative degree-of-goodness.