ORCID Profile
0000-0001-8783-5120
Current Organisation
Universiti Malaysia Terengganu
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Publisher: IEEE
Date: 07-2019
Publisher: MDPI AG
Date: 02-2021
DOI: 10.3390/RS13030519
Abstract: In recent decades, multispectral and hyperspectral remote sensing data provide unprecedented opportunities for the initial stages of mineral exploration and environmental hazard monitoring [...]
Publisher: MDPI AG
Date: 27-04-2023
DOI: 10.3390/RS15092306
Abstract: Cloud-to-ground (CG) lightning is a natural phenomenon that poses significant threats to human safety, infrastructure, and equipment. The destructive impacts of lightning strikes on humans and their property have been a longstanding concern for both society and industry. Countries with high thunderstorm frequencies, such as Malaysia, experience significant fatalities and damage due to lightning strikes. To this end, a lightning locating system (LLS) was developed and deployed in a 400 km2 study area at the University Technology Malaysia (UTM), Johor, Malaysia for detecting cloud-to-ground lightning discharges. The study utilized a particle swarm optimization (PSO) algorithm as a mediator to identify the best location for a lightning strike. The algorithm was initiated with 30 particles, considering the outcomes of the MDF and TDOA techniques. The effectiveness of the PSO algorithm was found to be dependent on how the search process was arranged. The results of the detected lightning strikes by the PSO-based LLS were compared with an industrial lightning detection system installed in Malaysia. From the experimental data, the mean distance differences between the PSO-based LLS and the industrial LLS inside the study area was up to 573 m. Therefore, the proposed PSO-based LLS would be efficient and accurate to detect and map the lightning discharges occurring within the coverage area. This study is significant for researchers, insurance companies, and the public seeking to be informed about the impacts of lightning discharges.
Publisher: MDPI AG
Date: 23-09-2018
DOI: 10.3390/S18103213
Abstract: Remote sensing imagery has become an operative and applicable tool for the preparation of geological maps by reducing the costs and increasing the precision. In this study, ASTER satellite remote sensing data were used to extract lithological information of Deh-Molla sedimentary succession, which is located in the southwest of Shahrood city, Semnan Province, North Iran. A robust and effective approach named Band Ratio Matrix Transformation (BRMT) was developed to characterize and discriminate the boundary of sedimentary rock formations in Deh-Molla region. The analysis was based on the forward and continuous ision of the visible-near infrared (VNIR) and the shortwave infrared (SWIR) spectral bands of ASTER with subsequent application of principal component analysis (PCA) for producing new transform datasets. The approach was implemented to ASTER spectral band ratios for mapping dominated mineral assemblages in the study area. Quartz, carbonate, and Al, Fe, Mg –OH bearing-altered minerals such as kaolinite, alunite, chlorite and mica were appropriately mapped using the BRMT approach. The results match well with geology map of the study area, fieldwork data and laboratory analysis. Accuracy assessment of the mapping result represents a reasonable kappa coefficient (0.70%) and appropriate overall accuracy (74.64%), which verified the robustness of the BRMT approach. This approach has great potential and capability for mapping sedimentary succession with erse local–geological–physical characteristics around the world.
Publisher: Informa UK Limited
Date: 05-06-2018
Publisher: MDPI AG
Date: 14-12-2021
DOI: 10.3390/LAND10121382
Abstract: The effects of global warming are putting the world’s coasts at risk. Coastal planners need relatively accurate projections of the rate of sea-level rise and its possible consequences, such as extreme sea-level changes, flooding, and coastal erosion. The east coast of Peninsular Malaysia is vulnerable to sea-level change. The purpose of this study is to present an Artificial Neural Network (ANN) model to analyse sea-level change based on observed data of tide gauge, rainfall, sea level pressure, sea surface temperature, and wind. A Feed-forward Neural Network (FNN) approach was used on observed data from 1991 to 2012 to simulate and predict the sea level change until 2020 from five tide gauge stations in Kuala Terengganu along the East Coast of Malaysia. From 1991 to 2020, predictions estimate that sea level would increase at a pace of roughly 4.60 mm/year on average, with a rate of 2.05 ± 7.16 mm on the East Coast of Peninsular Malaysia. This study shows that Peninsular Malaysia’s East Coast is vulnerable to sea-level rise, particularly at Kula Terengganu, Terengganu state, with a rate of 1.38 ± 7.59 mm/year, and Tanjung Gelang, Pahang state, with a rate of 1.87 ± 7.33 mm/year. As a result, strategies and planning for long-term adaptation are needed to control potential consequences. Our research provides crucial information for decision-makers seeking to protect coastal cities from the risks of rising sea levels.
Publisher: MDPI AG
Date: 19-10-2019
DOI: 10.3390/RS11202430
Abstract: Several regions in the High Arctic still lingered poorly explored for a variety of mineralization types because of harsh climate environments and remoteness. Inglefield Land is an ice-free region in northwest Greenland that contains copper-gold mineralization associated with hydrothermal alteration mineral assemblages. In this study, Landsat-8, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and WorldView-3 multispectral remote sensing data were used for hydrothermal alteration mapping and mineral prospecting in the Inglefield Land at regional, local, and district scales. Directed principal components analysis (DPCA) technique was applied to map iron oxide/hydroxide, Al/Fe-OH, Mg-Fe-OH minerals, silicification (Si-OH), and SiO2 mineral groups using specialized band ratios of the multispectral datasets. For extracting reference spectra directly from the Landsat-8, ASTER, and WorldView-3 (WV-3) images to generate fraction images of end-member minerals, the automated spectral hourglass (ASH) approach was implemented. Linear spectral unmixing (LSU) algorithm was thereafter used to produce a mineral map of fractional images. Furthermore, adaptive coherence estimator (ACE) algorithm was applied to visible and near-infrared and shortwave infrared (VINR + SWIR) bands of ASTER using laboratory reflectance spectra extracted from the USGS spectral library for verifying the presence of mineral spectral signatures. Results indicate that the boundaries between the Franklinian sedimentary successions and the Etah metamorphic and meta-igneous complex, the orthogneiss in the northeastern part of the Cu-Au mineralization belt adjacent to Dallas Bugt, and the southern part of the Cu-Au mineralization belt nearby Marshall Bugt show high content of iron oxides/hydroxides and Si-OH/SiO2 mineral groups, which warrant high potential for Cu-Au prospecting. A high spatial distribution of hematite/jarosite, chalcedony/opal, and chlorite/epidote/biotite were identified with the documented Cu-Au occurrences in central and southwestern sectors of the Cu-Au mineralization belt. The calculation of confusion matrix and Kappa Coefficient proved appropriate overall accuracy and good rate of agreement for alteration mineral mapping. This investigation accomplished the application of multispectral/multi-sensor satellite imagery as a valuable and economical tool for reconnaissance stages of systematic mineral exploration projects in remote and inaccessible metallogenic provinces around the world, particularly in the High Arctic regions.
Publisher: MDPI AG
Date: 13-04-2020
DOI: 10.3390/RS12081239
Abstract: The exploration of carbonate-hosted Pb-Zn mineralization is challenging due to the complex structural-geological settings and costly using geophysical and geochemical techniques. Hydrothermal alteration minerals and structural features are typically associated with this type of mineralization. Application of multi-sensor remote sensing satellite imagery as a fast and inexpensive tool for mapping alteration zones and lithological units associated with carbonate-hosted Pb-Zn deposits is worthwhile. Multiple sources of spectral data derived from different remote sensing sensors can be utilized for detailed mapping a variety of hydrothermal alteration minerals in the visible near infrared (VNIR) and the shortwave infrared (SWIR) regions. In this research, Landsat-8, Sentinel-2, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and WorldView-3 (WV-3) satellite remote sensing sensors were used for prospecting Zn-Pb mineralization in the central part of the Kashmar–Kerman Tectonic Zone (KKTZ), the Central Iranian Terrane (CIT). The KKTZ has high potential for hosting Pb-Zn mineralization due to its specific geodynamic conditions (folded and thrust belt) and the occurrence of large carbonate platforms. For the processing of the satellite remote sensing datasets, band ratios and principal component analysis (PCA) techniques were adopted and implemented. Fuzzy logic modeling was applied to integrate the thematic layers produced by image processing techniques for generating mineral prospectivity maps of the study area. The spatial distribution of iron oxide/hydroxides, hydroxyl-bearing and carbonate minerals and dolomite were mapped using specialized band ratios and analyzing eigenvector loadings of the PC images. Subsequently, mineral prospectivity maps of the study area were generated by fusing the selected PC thematic layers using fuzzy logic modeling. The most favorable rospective zones for hydrothermal ore mineralizations and carbonate-hosted Pb-Zn mineralization in the study region were particularly mapped and indicated. Confusion matrix, field reconnaissance and laboratory analysis were carried out to verify the occurrence of alteration zones and highly prospective locations of carbonate-hosted Pb-Zn mineralization in the study area. Results indicate that the spectral data derived from multi-sensor remote sensing satellite datasets can be broadly used for generating remote sensing-based prospectivity maps for exploration of carbonate-hosted Pb-Zn mineralization in many metallogenic provinces around the world.
Publisher: IEEE
Date: 07-2019
Publisher: MDPI AG
Date: 13-06-2019
DOI: 10.3390/RS11121408
Abstract: Listvenites normally form during hydrothermal/metasomatic alteration of mafic and ultramafic rocks and represent a key indicator for the occurrence of ore mineralizations in orogenic systems. Hydrothermal/metasomatic alteration mineral assemblages are one of the significant indicators for ore mineralizations in the damage zones of major tectonic boundaries, which can be detected using multispectral satellite remote sensing data. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral remote sensing data were used to detect listvenite occurrences and alteration mineral assemblages in the poorly exposed damage zones of the boundaries between the Wilson, Bowers and Robertson Bay terranes in Northern Victoria Land (NVL), Antarctica. Spectral information for detecting alteration mineral assemblages and listvenites were extracted at pixel and sub-pixel levels using the Principal Component Analysis (PCA)/Independent Component Analysis (ICA) fusion technique, Linear Spectral Unmixing (LSU) and Constrained Energy Minimization (CEM) algorithms. Mineralogical assemblages containing Fe2+, Fe3+, Fe-OH, Al-OH, Mg-OH and CO3 spectral absorption features were detected in the damage zones of the study area by implementing PCA/ICA fusion to visible and near infrared (VNIR) and shortwave infrared (SWIR) bands of ASTER. Silicate lithological groups were mapped and discriminated using PCA/ICA fusion to thermal infrared (TIR) bands of ASTER. Fraction images of prospective alteration minerals, including goethite, hematite, jarosite, biotite, kaolinite, muscovite, antigorite, serpentine, talc, actinolite, chlorite, epidote, calcite, dolomite and siderite and possible zones encompassing listvenite occurrences were produced using LSU and CEM algorithms to ASTER VNIR+SWIR spectral bands. Several potential zones for listvenite occurrences were identified, typically in association with mafic metavolcanic rocks (Glasgow Volcanics) in the Bowers Mountains. Comparison of the remote sensing results with geological investigations in the study area demonstrate invaluable implications of the remote sensing approach for mapping poorly exposed lithological units, detecting possible zones of listvenite occurrences and discriminating subpixel abundance of alteration mineral assemblages in the damage zones of the Wilson-Bowers and Bowers-Robertson Bay terrane boundaries and in intra-Bowers and Wilson terranes fault zones with high fluid flow. The satellite remote sensing approach developed in this research is explicitly pertinent to detecting key alteration mineral indicators for prospecting hydrothermal/metasomatic ore minerals in remote and inaccessible zones situated in other orogenic systems around the world.
Publisher: MDPI AG
Date: 19-06-2018
DOI: 10.3390/RS10060975
Publisher: Wiley
Date: 13-09-2016
DOI: 10.1111/RGE.12105
Publisher: MDPI AG
Date: 14-11-2018
DOI: 10.3390/IJGI7110444
Abstract: Earthquakes are among the most catastrophic natural geo-hazards worldwide and endanger numerous lives annually. Therefore, it is vital to evaluate seismic vulnerability beforehand to decrease future fatalities. The aim of this research is to assess the seismic vulnerability of residential houses in an urban region on the basis of the Multi-Criteria Decision Making (MCDM) model, including the analytic hierarchy process (AHP) and geographical information system (GIS). Tabriz city located adjacent to the North Tabriz Fault (NTF) in North-West Iran was selected as a case study. The NTF is one of the major seismogenic faults in the north-western part of Iran. First, several parameters such as distance to fault, percent of slope, and geology layers were used to develop a geotechnical map. In addition, the structural construction materials, building materials, size of building blocks, quality of buildings and buildings-floors were used as key factors impacting on the building’s structural vulnerability in residential areas. Subsequently, the AHP technique was adopted to measure the priority ranking, criteria weight (layers), and alternatives (classes) of every criterion through pair-wise comparison at all levels. Lastly, the layers of geotechnical and spatial structures were superimposed to design the seismic vulnerability map of buildings in the residential area of Tabriz city. The results showed that South and Southeast areas of Tabriz city exhibit low to moderate vulnerability, while some regions of the north-eastern area are under severe vulnerability conditions. In conclusion, the suggested approach offers a practical and effective evaluation of Seismic Vulnerability Assessment (SVA) and provides valuable information that could assist urban planners during mitigation and preparatory phases of less examined areas in many other regions around the world.
Publisher: MDPI AG
Date: 11-2021
Abstract: Climate change is regarded as a serious threat to both environment and humanity, and as a result, it has piqued worldwide attention in the twenty-first century. Natural hazards are expected to have major effects in the coastal cities of the globe. At the same time, about two-thirds of the world’s human population lives in the coastal margins. One of the fundamental issues for coastal city planners is the coastal cities’ environmental change. This paper presents the application of a model framework for the management and sustainable development of coastal cities under a changing climate in Kuala Terengganu Malaysia. The analytic hierarchy process (AHP) is performed in the Expert Choice software for coastal city hazard management. This approach enables decision-makers to evaluate and identify the relative priorities of vulnerability and hazard criteria and sub-criteria based on a set of preferences, criteria, and alternatives. This paper also presents a hierarchy erosion design applied in Kuala Terengganu to choose the important sustainable weights of criteria and sub-criteria as well as the zone as an alternative model.
Publisher: Elsevier BV
Date: 05-2019
Publisher: MDPI AG
Date: 27-07-2018
DOI: 10.3390/RS10081186
Abstract: Geological mapping and mineral exploration programs in the High Arctic have been naturally hindered by its remoteness and hostile climate conditions. The Franklinian Basin in North Greenland has a unique potential for exploration of world-class zinc deposits. In this research, multi-sensor remote sensing satellite data (e.g., Landsat-8, Phased Array L-band Synthetic Aperture Radar (PALSAR) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)) were used for exploring zinc in the trough sequences and shelf-platform carbonate of the Franklinian Basin. A series of robust image processing algorithms was implemented for detecting spatial distribution of pixels/sub-pixels related to key alteration mineral assemblages and structural features that may represent potential undiscovered Zn–Pb deposits. Fusion of Directed Principal Component Analysis (DPCA) and Independent Component Analysis (ICA) was applied to some selected Landsat-8 mineral indices for mapping gossan, clay-rich zones and dolomitization. Major lineaments, intersections, curvilinear structures and sedimentary formations were traced by the application of Feature-oriented Principal Components Selection (FPCS) to cross-polarized backscatter PALSAR ratio images. Mixture Tuned Matched Filtering (MTMF) algorithm was applied to ASTER VNIR/SWIR bands for sub-pixel detection and classification of hematite, goethite, jarosite, alunite, gypsum, chalcedony, kaolinite, muscovite, chlorite, epidote, calcite and dolomite in the prospective targets. Using the remote sensing data and approaches, several high potential zones characterized by distinct alteration mineral assemblages and structural fabrics were identified that could represent undiscovered Zn–Pb sulfide deposits in the study area. This research establishes a straightforward/cost-effective multi-sensor satellite-based remote sensing approach for reconnaissance stages of mineral exploration in hardly accessible parts of the High Arctic environments.
Publisher: MDPI AG
Date: 10-11-2021
DOI: 10.3390/RS13224519
Abstract: Tabriz city in NW Iran is a seismic-prone province with recurring devastating earthquakes that have resulted in heavy casualties and damages. This research developed a new computational framework to investigate four main dimensions of vulnerability (environmental, social, economic and physical). An Artificial Neural Network (ANN) Model and a SWOT-Quantitative Strategic Planning Matrix (QSPM) were applied. Firstly, a literature review was performed to explore indicators with significant impact on aforementioned dimensions of vulnerability to earthquakes. Next, the twenty identified indicators were analyzed in ArcGIS, a geographic information system (GIS) software, to map earthquake vulnerability. After classification and reclassification of the layers, standardized maps were presented as input to a Multilayer Perceptron (MLP) and Self-Organizing Map (SOM) neural network. The resulting Earthquake Vulnerability Maps (EVMs) showed five categories of vulnerability ranging from very high, to high, moderate, low and very low. Accordingly, out of the nine municipality zones in Tabriz city, Zone one was rated as the most vulnerable to earthquakes while Zone seven was rated as the least vulnerable. Vulnerability to earthquakes of residential buildings was also identified. To validate the results data were compared between a Multilayer Perceptron (MLP) and a Self-Organizing Map (SOM). The scatter plots showed strong correlations between the vulnerability ratings of the different zones achieved by the SOM and MLP. Finally, the hybrid SWOT-QSPM paradigm was proposed to identify and evaluate strategies for hazard mitigation of the most vulnerable zone. For hazard mitigation in this zone we recommend to diligently account for environmental phenomena in designing and locating of sites. The findings are useful for decision makers and government authorities to reconsider current natural disaster management strategies.
Publisher: MDPI AG
Date: 09-02-2022
DOI: 10.3390/RS14040819
Abstract: Lithological mapping is a critical aspect of geological mapping that can be useful in studying the mineralization potential of a region and has implications for mineral prospectivity mapping. This is a challenging task if performed manually, particularly in highly remote areas that require a large number of participants and resources. The combination of machine learning (ML) methods and remote sensing data can provide a quick, low-cost, and accurate approach for mapping lithological units. This study used deep learning via convolutional neural networks and conventional ML methods involving support vector machines and multilayer perceptron to map lithological units of a mineral-rich area in the southeast of Iran. Moreover, we used and compared the efficiency of three different types of multispectral remote-sensing data, including Landsat 8 operational land imager (OLI), advanced spaceborne thermal emission and reflection radiometer (ASTER), and Sentinel-2. The results show that CNNs and conventional ML methods effectively use the respective remote-sensing data in generating an accurate lithological map of the study area. However, the combination of CNNs and ASTER data provides the best performance and the highest accuracy and adaptability with field observations and laboratory analysis results so that almost all the test data are predicted correctly. The framework proposed in this study can be helpful for exploration geologists to create accurate lithological maps in other regions by using various remote-sensing data at a low cost.
Publisher: MDPI AG
Date: 24-12-2020
DOI: 10.3390/RS13010038
Abstract: In Antarctica, spectral mapping of altered minerals is very challenging due to the remoteness and inaccessibility of poorly exposed outcrops. This investigation evaluates the capability of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite remote sensing imagery for mapping and discrimination of phyllosilicate mineral groups in the Antarctic environment of northern Victoria Land. The Mixture-Tuned Matched-Filtering (MTMF) and Constrained Energy Minimization (CEM) algorithms were used to detect the sub-pixel abundance of Al-rich, Fe3+-rich, Fe2+-rich and Mg-rich phyllosilicates using the visible and near-infrared (VNIR), short-wave infrared (SWIR) and thermal-infrared (TIR) bands of ASTER. Results indicate that Al-rich phyllosilicates are strongly detected in the exposed outcrops of the Granite Harbour granitoids, Wilson Metamorphic Complex and the Beacon Supergroup. The presence of the smectite mineral group derived from the Jurassic basaltic rocks (Ferrar Dolerite and Kirkpatrick Basalts) by weathering and decomposition processes implicates Fe3+-rich and Fe2+-rich phyllosilicates. Biotite (Fe2+-rich phyllosilicate) is detected associated with the Granite Harbour granitoids, Wilson Metamorphic Complex and Melbourne Volcanics. Mg-rich phyllosilicates are mostly mapped in the scree, glacial drift, moraine and crevasse fields derived from weathering and decomposition of the Kirkpatrick Basalt and Ferrar Dolerite. Chlorite (Mg-rich phyllosilicate) was generally mapped in the exposures of Granite Harbour granodiorite and granite and partially identified in the Ferrar Dolerite, the Kirkpatrick Basalt, the Priestley Formation and Priestley Schist and the scree, glacial drift and moraine. Statistical results indicate that Al-rich phyllosilicates class pixels are strongly discriminated, while the pixels attributed to Fe3+-rich class, Fe2+-rich and Mg-rich phyllosilicates classes contain some spectral mixing due to their subtle spectral differences in the VNIR+SWIR bands of ASTER. Results derived from TIR bands of ASTER show that a high level of confusion is associated with mafic phyllosilicates pixels (Fe3+-rich, Fe2+-rich and Mg-rich classes), whereas felsic phyllosilicates (Al-rich class) pixels are well mapped. Ground truth with detailed geological data, petrographic study and X-ray diffraction (XRD) analysis verified the remote sensing results. Consequently, ASTER image-map of phyllosilicate minerals is generated for the Mesa Range, C bell and Priestley Glaciers, northern Victoria Land of Antarctica.
Publisher: MDPI AG
Date: 03-2019
DOI: 10.3390/RS11050495
Abstract: Polymetallic vein-type ores are important sources of precious metal and a principal type of orebody for various base-metals. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data were used for mapping hydrothermal alteration zones associated with epithermal polymetallic vein-type mineralization in the Toroud–Chahshirin Magmatic Belt (TCMB), North of Iran. The TCMB is the largest known goldfield and base metals province in the central-north of Iran. Propylitic, phyllic, argillic, and advanced argillic alteration and silicification zones are typically associated with Au-Cu, Ag, and/or Pb-Zn mineralization in the TCMB. Specialized image processing techniques, namely Selective Principal Component Analysis (SPCA), Band Ratio Matrix Transformation (BRMT), Spectral Angle Mapper (SAM) and Mixture Tuned Matched Filtering (MTMF) were implemented and compared to map hydrothermal alteration minerals at the pixel and sub-pixel levels. Subtle differences between altered and non-altered rocks and hydrothermal alteration mineral assemblages were detected and mapped in the study area. The SPCA and BRMT spectral transformation algorithms discriminated the propylitic, phyllic, argillic and advanced argillic alteration and silicification zones as well as lithological units. The SAM and MTMF spectral mapping algorithms detected spectrally dominated mineral groups such as muscovite/montmorillonite/illite, hematite/jarosite, and chlorite/epidote/calcite mineral assemblages, systematically. Comprehensive fieldwork and laboratory analysis, including X-ray diffraction (XRD), petrographic study, and spectroscopy were conducted in the study area for verifying the remote sensing outputs. Results indicate several high potential zones of epithermal polymetallic vein-type mineralization in the northeastern and southwestern parts of the study area, which can be considered for future systematic exploration programs. The approach used in this research has great implications for the exploration of epithermal polymetallic vein-type mineralization in other base metals provinces in Iran and semi-arid regions around the world.
Publisher: MDPI AG
Date: 17-09-2021
DOI: 10.3390/IJGI10090621
Abstract: Since at least half of the world’s population resides and works within coastal land, the coastal zone processes and resource management is of great economic and social importance. One of the fundamental issues for coastal city planners, researchers, managers, and engineers is the coastal city land-use suitability. Land-use suitability is the ability of a given type of land to support a defined use. Rapid urbanization and consequent haphazard growth of cities result in deterioration of infrastructure facilities, loss of agricultural land, water bodies, open spaces, and many micro-climatic changes. Hence, accurate data on coastal city hazards are essential and valuable tools for coastal planning and management, sustainable coastal development, coastal environment conservation, selection of a site for coastal city structures, and coastal resources. In this investigation, the Delphi and Analytical Hierarchy Process (D-AHP) Hybrid model and Geographic Information System (GIS) technique for Coastal Land-Use Assessment (CLUA) are mapped to detect the most suitable and unsuitable areas in the Kuala Terengganu coastal zone. Furthermore, this research offered information not only on the present urban land-use trend and established amenity status in Kuala Terengganu, but also on the suitability of land for the potential establishment of urban facilities for improved urban planning and appropriate decision-making. Using the D-AHP Hybrid model and GIS tool for coastal city management is broadly practical for government, policymakers, and planners to appropriately strategize and plan for the future of coastal cities in Malaysia and other analog coastal cities around the world.
Publisher: Elsevier BV
Date: 07-2020
Publisher: Springer Science and Business Media LLC
Date: 09-2019
Publisher: Institution of Engineering and Technology (IET)
Date: 02-05-2021
DOI: 10.1049/RPG2.12196
Publisher: Informa UK Limited
Date: 27-07-2021
Publisher: Informa UK Limited
Date: 11-01-2022
Publisher: MDPI AG
Date: 06-11-2021
DOI: 10.3390/MIN11111235
Abstract: The application of machine learning (ML) algorithms for processing remote sensing data is momentous, particularly for mapping hydrothermal alteration zones associated with porphyry copper deposits. The unsupervised Dirichlet Process (DP) and the supervised Support Vector Machine (SVM) techniques can be executed for mapping hydrothermal alteration zones associated with porphyry copper deposits. The main objective of this investigation is to practice an algorithm that can accurately model the best training data as input for supervised methods such as SVM. For this purpose, the Zefreh porphyry copper deposit located in the Urumieh-Dokhtar Magmatic Arc (UDMA) of central Iran was selected and used as training data. Initially, using ASTER data, different alteration zones of the Zefreh porphyry copper deposit were detected by Band Ratio, Relative Band Depth (RBD), Linear Spectral Unmixing (LSU), Spectral Feature Fitting (SFF), and Orthogonal Subspace Projection (OSP) techniques. Then, using the DP method, the exact extent of each alteration was determined. Finally, the detected alterations were used as training data to identify similar alteration zones in full scene of ASTER using SVM and Spectral Angle Mapper (SAM) methods. Several high potential zones were identified in the study area. Field surveys and laboratory analysis were used to validate the image processing results. This investigation demonstrates that the application of the SVM algorithm for mapping hydrothermal alteration zones associated with porphyry copper deposits is broadly applicable to ASTER data and can be used for prospectivity mapping in many metallogenic provinces around the world.
Publisher: IEEE
Date: 08-2015
Publisher: MDPI AG
Date: 16-04-2020
DOI: 10.3390/RS12081261
Abstract: There are a significant number of image processing methods that have been developed during the past decades for detecting anomalous areas, such as hydrothermal alteration zones, using satellite images. Among these methods, dimensionality reduction or transformation techniques are known to be a robust type of methods, which are helpful, as they reduce the extent of a study area at the initial stage of mineral exploration. Principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF) are the dimensionality reduction techniques known as multivariate statistical methods that convert a set of observed and correlated input variables into uncorrelated or independent components. In this study, these techniques were comprehensively compared and integrated, to show how they could be jointly applied in remote sensing data analysis for mapping hydrothermal alteration zones associated with epithermal Cu–Au deposits in the Toroud-Chahshirin range, Central Iran. These techniques were applied on specific subsets of the advanced spaceborne thermal emission and reflection radiometer (ASTER) spectral bands for mapping gossans and hydrothermal alteration zones, such as argillic, propylitic, and phyllic zones. The fuzzy logic model was used for integrating the most rational thematic layers derived from the transformation techniques, which led to an efficient remote sensing evidential layer for mineral prospectivity mapping. The results showed that ICA was a more robust technique for generating hydrothermal alteration thematic layers, compared to the other dimensionality reduction techniques. The capabilities of this technique in separating source signals from noise led to improved enhancement of geological features, such as specific alteration zones. In this investigation, several previously unmapped prospective zones were detected using the integrated hydrothermal alteration map and most of the known hydrothermal mineral occurrences showed a high prospectivity value. Fieldwork and laboratory analysis were conducted to validate the results and to verify new prospective zones in the study area, which indicated a good consistency with the remote sensing output. This study demonstrated that the integration of remote sensing-based alteration thematic layers derived from the transformation techniques is a reliable and low-cost approach for mineral prospectivity mapping in metallogenic provinces, at the reconnaissance stage of mineral exploration.
Publisher: Wiley
Date: 29-09-2015
DOI: 10.1111/RGE.12076
Publisher: Research Square Platform LLC
Date: 08-2022
DOI: 10.21203/RS.3.RS-1711210/V1
Abstract: The cloud to ground (CG) lightning has negative impacts on humans and properties. A lightning strike is a great concern to mankind and industry because of its detrimental impact on human safety, hazard, and equipment failures. There are different lightning detection methods, including time difference of arrival (TDOA) and magnetic direction finding (MDF). Using combined techniques is an innovative approach to achieve higher location accuracy and detection efficiency of lightning flashes. In this investigation, a Lightning locating system (LLS) was designed and implemented at University Technology Malaysia (UTM), Johor, Malaysia to detect the cloud to ground lightning discharges in a study area of 400 km 2 . A particle swarm optimization (PSO) algorithm was applied in this study as the combination mediator to find the optimum point of the lightning strike. The PSO was initialized by 30 particles based on the results of the MDF and TDOA methods. The performance of the PSO-based algorithm is known to be affected by the arrangement of the searching process. The results of the detected lightning strikes by the PSO-based LLS were validated using an industrial lightning detection system for December and March. In addition, the whole study area was ided into 36 equal sections to analyze the abundance of CG discharges in each section. From the experimental data, the mean distance differences between the PSO-based LLS and the industrial LLS inside the study area varied from 0 to 573 m. Therefore, the proposed PSO-based LLS is efficient and accurate to detect and map the lightning discharges occurring within the coverage area. Although an industrial LLS monitors a large area or a country with several sensors, the detected lightning discharges and the statistical data analysis of the captured flashes are not obtainable by public in iduals. Meanwhile, estimation and localization of lightning strikes are necessary for the public to mitigate the problems related to lightning discharges. Moreover, this study will be significant for the researchers, the insurance companies, and public users to be aware of the detected storms and estimation of imminent rainfalls. The PSO-based LLS provides an accurate lightning detection system for a specific local region and can be implemented to regional scale in other parts of the world.
Publisher: MDPI AG
Date: 12-02-2021
DOI: 10.3390/PR9020339
Abstract: Energy generation and its utilization is bound to increase in the following years resulting in accelerating depletion of fossil fuels, and consequently, undeniable damages to our environment. Over the past decade, despite significant efforts in renewable energy realization and developments for electricity generation, carbon dioxide emissions have been increasing rapidly. This is due to the fact that there is a need to go beyond the power sector and target energy generation in an integrated manner. In this regard, energy systems integration is a concept that looks into how different energy systems, or forms, can connect together in order to provide value for consumers and producers. Cogeneration and trigeneration are the two most well established technologies that are capable of producing two or three different forms of energy simultaneously within a single system. Integrated energy systems make for a very strong proposition since it results in energy saving, fuel ersification, and supply of cleaner energy. Optimization of such systems can be carried out using several techniques with regards to different objective functions. In this study, a variety of optimization methods that provides the possibility of performance improvements, with or without presence of constraints, are demonstrated, pinpointing the characteristics of each method along with detailed statistical reports. In this context, optimization techniques are classified into two primary groups including unconstrained optimization and constrained optimization techniques. Further, the potential applications of evolutionary computing in optimization of Integrated Energy Systems (IESs), particularly Combined Heat and Power (CHP) and Combined Cooling, Heating, and Power (CCHP), utilizing renewable energy sources are grasped and reviewed thoroughly. It was illustrated that the employment of classical optimization methods is fading out, replacing with evolutionary computing techniques. Amongst modern heuristic algorithms, each method has contributed more to a certain application while the Genetic Algorithm (GA) was favored for thermoeconomic optimization, Particle Swarm Optimization (PSO) was mostly applied for economic improvements. Given the mathematical nature and constraint satisfaction property of Mixed-Integer Linear Programming (MILP), this method is gaining prominence for scheduling applications in energy systems.
Publisher: MDPI AG
Date: 19-11-2021
DOI: 10.3390/LAND10111271
Abstract: Coastal hazards are an urgent issue of global concern considering the increasing population pressure in coastal regions, retreating coastlines, and rising seawater levels. Here we demonstrate the process of assessing the vulnerability of a coastal urban environment using the case of Kuala Terengganu, a coastal town in Malaysia, and evaluating the potential social, environmental, and economic impacts. Uncertainties in the human dimensions of global change deeply affect the assessment and responses to environmental, climatic, and non-climate impacts on coastal city population growth and communities. We address these uncertainties by combining a Delphi-Analytical Hierarchy Process (Delphi-AHP) model and Geographic Information System (GIS)tools to determine mitigation and adaptation probabilities as part of a Coastal City Vulnerability Assessment. We conclude by presenting calculations of the short- and long-term suitability for land use and recommending hazard mitigation measures to equip city planners and decision-makers in evaluating hazards and potential impacts on coastal city areas.
No related grants have been discovered for Amin Beiranvand Pour.