Mapping alteration mineral zones and lithological units in Antarctic regions using spectral bands of ASTER remote sensing data

Abstract

Geological mapping is one of the primary tasks of remote sensing. Remote sensing applications are especially useful when extreme environmental conditions inhibit direct survey such as in Antarctica. In this investigation, a satellite-based remote sensing approach was used for mapping alteration mineral zones and lithological units using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data in the Oscar II coast area, north-eastern Graham Land, Antarctic Peninsula. Specialized band ratios and band combinations were developed using visible and near infrared, shortwave infrared (SWIR) and thermal infrared spectral bands of ASTER for detecting alteration mineral assemblages and lithological units in Antarctic environments. Constrained Energy Minimization, Orthogonal Subspace Projection and Adaptive Coherence Estimator algorithms were tested to ASTER SWIR bands for detecting sub-pixels’ abundance of spectral features related to muscovite, kaolinite, illite, montmorillonite, epidote, chlorite and biotite. Results indicate valuable applicability of ASTER data for Antarctic geological mapping.     

Machine Learning Approach to Predict Sediment Load – A Case Study

In this study, a novel machine learning technique called the support vector machine (SVM) method is proposed as a new predictive model to predict sediment loads in three Malaysian rivers. The SVM is employed without any restriction to an extensive database compiled from measurements in the Muda, Langat, and Kurau rivers. The SVM technique demonstrated a superior performance compared to other traditional sediment‐load methods. The coefficient of determination, 0.958, and the mean square error, 0.0698, of the SVM method are higher than those of the traditional method. The performance of the SVM method demonstrates its predictive capability and the possibility of the generalization of the model to nonlinear problems for river engineering applications.     

The cumulative impacts of reclamation and dredging on the marine ecology and land-use in the Kingdom of Bahrain

This article assesses the ecological and economic impacts of land reclamation and dredging through consulting recent environmental impact assessment reports. Geographic features of Bahrain during 1963-2008 are produced using Geographical Information System. Extensive but inexpensive shallow coastal areas and tidal flats have been reclaimed particularly from 1997 to 2007 at a high rate of 21 km(2)/year. Formal records show the increase in the original land mass by the year 2008 to be 91 km(2). An estimated total cumulative loss of major habitats resulting from 10 reclamation projects was around 153.58 km(2). Also much larger scale impacts should be considered resulting from the borrow areas used for the extraction of sand or infill materials. A number of key habitats and species are affected in the vicinity of these projects. The study attempts to assign a monetary value to the marine ecosystem functions. There is a need for efficient coastal zone management to regulate a sustainable use of the marine resources.     

Adaptation of ANFIS model to assess thermal comfort of an urban square in moderate and dry climate

Attractiveness of the open urban spaces, such as plazas or squares, depends on the visitor’s thermal comfort. In this respect, it is important to assess the environment of such open space along with the demographic factors of the visitors. This study used the soft-computing method of adaptive neuro fuzzy inference system (ANFIS) to investigate the thermal comfort of visitors at a public square in Iran during hot and cold weather conditions. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the individual’s comfortable feeling. Model’s training and testing data were collected through the field measurement and survey during hot and cold times of the year. We used 18 input parameters, representative of demographic and environmental factors, to compute visitor’s thermal sensation, comfort feeling, and 4 common indices, namely the mean radiant temperature (T_mrt), mean physiological equivalent temperature (PET), standard effective temperature (SET) and predicted mean vote (PMV). The results indicated that among the examined factors, the air temperature (T_a) is the most influential parameter and best predictor of accuracy for the individual’s comfort feeling at the studied urban square. The results show that T_a can best predict the common indices of outdoor comfort, namely the PMV, PET, SET, thermal sensation, T_mrt, and comfortable felling compared to other parameters with the least error of 1.94, 18.87, 13.67, 0.91, 7.80, and 0.34 %, respectively. Some of the main advantages of the ANFIS scheme are that it is adaptable to the optimization and adaptive methods, and is computationally efficient.     

Dredged marine sediments used as novel supply of filling materials for road construction

The dredged marine sediments are classified as waste and how to deal with such kind of abandoned materials is a great challenge. The main objective of this experimental work is to provide a novel way to reuse dredged sediment as filling materials in road section. The laboratory dewatering test is performed to model the in situ evaporation and dewatering process of untreated and treated sediment with chemical binder. The impact of binder amount and time is discussed on the change of water content influenced by evaporation. To valorize dredged sediments as roadbed materials, a hydraulic binder is incorporated to investigate its effect on the bearing capacity and strength performance. The suitability of stabilized sediment is assessed based on the obtained mechanical results followed by a detailed discussion on the in situ test roads.     

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...     

Seismic response of a four-storey RC school building with masonry-infilled walls

This paper focuses on the evaluation of the effect of masonry-infilled walls on the seismic response of a four-storey RC school building. Numerical simulation is presented with respect to the building with and without masonry-infilled walls. The simulation employed force-based fibre elements for beams and columns, and a single equivalent diagonal strut to represent the masonry infill. The numerical model was verified through comparison with shake table test results. It was found that the presence of masonry-infilled walls has significant influence on the global response of the structure. Although the displacement of the structure with infilled wall is reduced in the in-plane direction, it was increased in both in-plane and out-of-plane directions when the infilled walls experienced loss in serviceability.     

PITCHER PLANTS RECORDED FROM BRIS FOREST IN JAMBU BONGKOK, KUALA TRENGGANU, MALAYSIA

1INTRODUCTION NepentheswhichareknownlocallyinPeninsular Malaysiaasperiukkerabelongtoamonogenericfami lyNepenthaceae.Atpresent,atotalofninespecies(excludingnaturalhybrids)arerecordedfromPenin sularMalaysia.Ofthese,fourspeciesareendemic,whilsttheotherfivespeciesarefoundoutsidethepen insula.Thespeciescanarbitrarilybeclassifiedinto thelowlandspeciesandthehighlandspecies(Clarke,2002;Shivas,1984;Kurata,1976).Threespeciesarefoundinthehighlandhabitatscom monlyatanelevationofabove1000mforaboves…     

Developing nonlinear models for sediment load estimation in an irrigation canal

The study was performed to estimate the weekly sediment load in Thal canal located in Mianwali district Punjab, Pakistan. Past records of sediments and discharge have been considered as the input parameters. The best input combinations have been identified with the help of advanced algorithms including full, sequential and increasing embedding, genetic algorithm and hill climbing in combination with the gamma test. Model training has been carried out using two artificial neural network-based algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), back-propagation and a local linear regression technique. A variety of statistical parameters including R square, root mean squared error, mean square error and mean bias error (MBE) has been calculated in order to evaluate the best models. The results strongly suggested that BFGS-based model performed better than all other models with remarkably low values of MBE. Significantly high values of correlation coefficient (R square) in both training and testing evidenced a close similarity between actual and predicted sediment load values for the same model.