bluuebunny/crossref_metadata_2025_split huggingface dataset:
Monitoring Groundwater Depletion Due to Drought using Satellite Gravimetry: A Review
Authors: Mohamad N , Ahmad A , Din A H M 
Published: August 2020

AbstractGroundwater plays a vital role in the global water cycle as a source of water for human use in daily life. The problem of groundwater depletion attracts researchers to understand the phenomenon of terrestrial water storage (TWS) and the primary technique used to monitor changes in groundwater mass in the subsurface. Accurate quantification is subtle due to the weakness of gravity measurement methods, which cover a wide range with high precision. A global evaluation of improvements in groundwater storage used a calculation tool that may involve temporal differences in TWS. The Gravity Recovery and Climate Experiment (GRACE) and the GRACE Follow-On (GRACE-FO) satellite missions were able to monitor changes in water mass in the basin and calculate changes in water levels by measuring gravity variations and quantifying groundwater tables at 1-micron precision. This paper aims to discuss GRACE, GRACE-FO and hydrological variables in the monitoring of groundwater depletion during the drought season. This paper presents an estimation technique using satellite gravimetry and hydrological methods, as well as a study of several case studies in central Amazon (Brazil), Murray-Darling (Australia) and Mongolia Basin. Previous observations, including TWS-hydrological variables, trends in groundwater depletion and drought intensity, have been discussed as a vital outcome of the paper as a whole.
Deep Learning-Based Water Segmentation for Autonomous Surface Vessel
Authors: Mohd Adam Muhammad Ammar , Ibrahim Ahmad Imran , Abidin Zulkifli Zainal , Mohd Zaki Hasan Firdaus 
Published: August 2020

AbstractVisual-based obstacle detection from an autonomous surface vessel (ASV) is a complex task due to high variance of scene properties such as different illumination and presence of reflections. One approach in implementing the task is through extracting waterlines to enable inferring of vessel orientation and obstacles presence. Classical computer vision algorithms for detection holds limitation in robustness and scalability. With recent breakthroughs in deep neural network architectures, vision-based object detection is seen to obtain high performance. In this work, the deep learning models based on Convolutional Neural Network (CNN) to implement binary semantic segmentation is studied. This architecture identifies each pixel to water and non-water classes. In purpose of benchmarking models, Fully Convolutional Network (FCN), SegNet and U-Net are trained on a publicly available dataset, IntCatch Vision Data Set (ICVDS), to evaluate the performance. From the experiments carried out, quantitative results show effectiveness of the models with accuracy all above 95.55% and lowest average speed of 11 frames per second. To improve, pre-trained networks (VGG 16, Resnet-50 and MobileNet) are used as a backbone, obtaining an improved accuracy above 98.14% with lowest inferring speed of 10 frame per second. Using the developed ASV, new dataset of 143 images called Malaysia ASV Dataset (MASVD) is collected, labelled and made publicly available. The trained models are tested with the newly collected dataset obtaining accuracy of 75%. The high accuracy performance shows potential for the models to be employed for collision avoidance algorithm in ASV navigation.
Estimated relative permittivity of contaminated laterite soil: An empirical model for GPR waves
Authors: Ghazali Mimi Diana , Zainon Othman , Mohsin Rahmat , Idris Khairulnizam M. , Mustafa Nordhalia 
Published: August 2020

Abstract
Estimated relative permittivity performed on soil is essential for forecasting the performance of Ground Penetrating Radar (GPR) in an in-depth manner. This study investigated and verified the empirical relationship model between relative permittivity and volumetric water content in soil to predict the relative permittivity of contaminated laterite soil. In this study, a 24-hour measurement involving 800 MHz shielded antenna GPR was carried out in a concrete simulation field tank filled with Terap Red soil (1.5 m x 2.6 m x 1.5 m) at UiTM Perlis, Malaysia. Embedded moisture content probe was simultaneously measured to monitor the response of volumetric water content in contaminated soil in order to formulate an empirical relationship between relative permittivity and moisture content. The GPR data were pre-processed and filtered with Reflexw 7.5, while regression analysis was performed to evaluate the empirical relationship model. The model outcomes were retrieved from a number of cross-validation schemes, including correlation analysis (R2), root mean square error (RMSE), and calibrated Agilent Technologies Automated Vector Analyser (VNA). A third-order polynomial for analysis of variance (ANOVA) best fitted the model with positively strong correlation (R2=0.989, N=24, P < 0.01) and RMSE 0.003< RMSEpredicted < 0.19. Verification of the proposed model using calibrated VNA displayed exceptional agreement between 0.06% comparisons.
Assessing the economic value of carbon sequestration in Taman Negara Pahang
Authors: Hussainzad Emal Ahmad , Mohd Yusof Mohd Johari 
Published: August 2020

Abstract
Aesthetic benefits are not enough. Authorities need to be presented with economic benefits as well to make decisions in favour of forests and help them conserved or expanded. Thus, this paper aims to evaluate the economic value of carbon sequestration in Taman Negara Pahang (TNP), Malaysia, a national park with an extensive forest area. The study area covers 247,700 hectares of the Pahang State. The study uses a Geographic Information System (GIS) method of Normalized Difference Vegetation Index (NDVI) to identify the density of the forest across the study area. Consequently, by using the NDVI results, the carbon sequestration in TNP and its economic value were calculated. As a result, the research found that the potential of carbon storage or sequestration in TNP was 142,560,513 tonnes CO2. The economic value of which was worth RM2,637,369,490.50. The results of the GIS calculation were more accurate than the conventional method. It was due to the reason that they were based on evaluating density on a smaller scale of 900m x 900m (81ha). The results were aligned with previous studies pointing out higher density areas holding higher economic values. The study results can be used to convince Malaysian authorities to make wise decisions about such forests as they worth billions of Ringgit Malaysia.
An Approach for the Retrieval of Land Surface Temperature from the Industrial Area Using Landsat-8 Thermal Infrared Sensors
Authors: Dahiru M Z , Hashim Mazlan 
Published: August 2020

Abstract
The thermal imagine provides data with synoptic coverage for investigating thermal information from hot sources for detecting, mapping energy loss from the industrial area. This study attempts to retrieved heat loss from the industrial area using Landsat-8 TIRS experimented at an industrial area of Pasir Gudang, Peninsular Malaysia, the main objective is to investigate the sensitivity of Landsat-8 TIR for detecting industrial thermal energy within the various range of targets of different temperatures. An estimated heat map with absolute surface temperature values is the final output. Apart of the pre-processing of Landsat-8 TIRS data, data are processed for the retrieval of land surface temperature, then subjected to a downscaling process to final 30 x 30 m pixels, hence enable to merge with all Landsat-8 bands for visualization and validation of results. The split window algorithm (SWA) is used for the temperature retrieval from band 10 and 11, with other driven parameters. The Multiple Adaptive Regression Splines (MARS) model for spatial downscaling was adopted in this study. The generated thermal energy map was then validated over selected targets in the field and compared to corresponding downscaled MODIS LST product (MODIS11A2). TIR bands applied with SWA generated 13.7°C temperature dynamic range from 22.35˜51.36° C in comparison with MODIS LST product values range from 27.17 ˜ 37.65°C). Results indicated good agreement between the generated thermal energy map with the in-situ validations (RMSE=0.43 °C). It is therefore concluded that derived Land surface temperature map derived is suitable for study industrial thermal environment at 1:50,000 ˜ 100,000 scales, adequately to be used for environmental impact assessment.
Integrated Facility and Assets Management using GIS-Web Application
Authors: Saiful Bahri Mohd Aizat , Abdul Maulud Khairul Nizam , Rahman Muhammad Amartur , Ridzuan Oon Aslinda Oon , Che Hashim Che Hafify 
Published: August 2020

AbstractFacility and assets management has to become a crucial practice in the era of income generation in an education institution, the need to provide better service to the client/student or public as well as minimizing the maintenance cost has become a challenge to the institution. The aim of this paper is to develop a web GIS to store, manage, and manipulate the data in order to maximize the use of space and assets as well as making sure the assets is in good condition. Also, to determine the higher value assets to optimize its use for income generation. The methodology of this study is asset and space data acquisition to store in the GIS database and integrate with another native database (Structured Query Language-SQL) to ensure the efficiencies of data updates. The findings of this study focusing on the capabilities of the GIS to manage, store and manipulate as well as display all the online data with such query, searching and analysis widget in the application. In conclusion, asset and space management in university with the modules included in the application has become useful for personnel and assets officials to query, searching and make analysis for preliminary planning and decision making.
Spatiotemporal analysis land surface temperature in relation to earthquake occurrence around the cimandiri fault
Authors: Huda Diki Nurul , Supriatna  , Ash Shidiq Iqbal Putut , Taqyuddin  
Published: August 2020

Abstract
Cimandiri Fault is one of the faults that have high tectonic activity. Several earthquake events around the Cimandiri Fault have caused impacts and losses on humans. Efforts to detect earthquake events need to be done as disaster mitigation. Detection of earthquake events in this study by looking at deviations in surface temperature. This study was conducted to investigate the relationship between changes in land surface temperature (LST) in relation to the earthquake that occurred on 7 July 2018 (ML = 4.73) and 11 October 2018 (ML = 3.77). Landsat 8 satellite imagery are used in this study to get the value of the land surface temperature using the LST algorithm. To find out the deviation that occurred researchers used a statistical test x ± σ with a 66% confidence level and compared with the average LST for five years. The results of the study are an increase in land surface temperature due to the earthquake occurrence on July 7, 2011, an increase in surface temperature of land was 6.56 oC on the day before the earthquake, and October 11, 2011, there was an increase of 7.02°C the day before the earthquake.
Comprehensive Spatial Criteria and Parameters for Sustainable Landfill Site Selection
Authors: Ahamad M S S , Ahmad S Z 
Published: August 2020

Abstract
A landfill site is an important facility that must be occasionally evaluated when its demand gets high with the increment of generated waste. The task of selecting new landfill sites becomes difficult when there is a shortage of land and time available. It requires tedious planning decision about identifying the suitable sites. The complication will proliferate when there are various criteria that need to be apprehended. In Malaysia, the procedures for landfill site selection (LSS) is provided in the National Strategic Plan for Solid Waste Management (NSPSWM). It specifies the mitigation measures to be followed, but the restrictions on the appropriateness of suitable new landfill sites were not comprehensively deliberated. The criteria for the site suitability problem was not distinctly characterized as prescribed by the local Environmental Impact Assessment (EIA) guidelines. The significance of selecting proper landfill sites must encompass the physical characteristics, environmental impact, economic and social acceptance for the necessity of the sustainability community life. From the works of literature, this study introduced seventeen (17) universally adopted spatial criteria categorized under the environmental, physical and socio-economic (EPSE) characteristics. They were successively assigned with the relative weights and standard parameters. The EPSE criteria can assist in improving the regulated LSS policy currently available in the local NSPSWM and EIA guidelines. This will ascertain that future landfill site will be sustainable, safeguard public health, ensuring minimal impact on the environment, and provide long term isolation of the solid waste deposited.
RFID Monitoring System and Management on Deer Husbandry
Authors: Anak Rechie Rizzo Mungka , Kassim Murizah , Ya’acob Norsuzila , Mohamad Roslina 
Published: August 2020

Abstract
This research presents an RFID Monitoring System and Management on Deer Husbandry. The design of an RFID system monitoring on a deer farm that used the RFID Mifare tag at its 13.56 frequencies and database system is presented. The deer in a farm in Malaysia are hardly monitored with technological assistant such as the tagging system is not well supported. Thus, deer husbandry farm is hard to monitor especially on the management of deer biodata where a fast retrieved for a specific deer need to be located and inspected. Today, deer farmers faced a slow process and lack of Quality of Services. This research presents a designed system that the RFID able to scan the deer and data is captured in a short amount of time to a database. Every deer is designed with its monitoring ID based on group by ages, weight, breed, and its descendant. The design of deer farm management and monitoring system is developed based on hardware, software, and data analysis on deer on a farm that is presented on the web and mobile application. The result presents a success designed for prototype system on mobile application and data from the RFID tag deer is presented in gender, species, and year of birth. Analysis has been done based on a deer farm in Kulim Kedah and graphical outputs are presented. A total of 54 deers are successfully captured, monitored, and manage where Blynk and FavorIOT platform is designed. This research is significant to deer husbandry farmer in Malaysia where deer just become a new market in Malaysia.
A 2-way geo-crowdsourcing approach in flood relief coordination and distribution
Authors: Idris N H , Ishak M H I 
Published: August 2020

Abstract
Over recent years, the use of Web 2.0 social platforms and geo-crowdsourcing in disaster management and emergency responses has become common. The emergence of various communication channels has led to abundance of information, but it cannot be used directly by the first responders and flood volunteers in their responses for several reasons. This paper proposes a 2-way geo-crowdsourcing framework to centralise the flood relief distribution channels using mobile geolocation technology. The framework is anticipated to be suitable for use by flood volunteers, including NGOs, private agencies and ad-hoc volunteers, to systematically distribute the relief items to the victims. The lessons learnt from the NGOs previously involved in the volunteer efforts in the 2014 Kelantan massive flood were discussed and become the basis of this framework. The framework could be applied for other studies that seek to support a 2-way communication between netizens and the authorities using a geo-crowdsourcing platform.
Drone Technology as A Modern Tool in Monitoring the Rural-Urban Development
Authors: Yunus Asniza Md , Hamzah Abd Halim , Md. Azmi Fatin Afiqah 
Published: August 2020

Abstract
The current practice of monitoring the rural-urban development in Malaysia is mainly based on conventional method that has limitations. This suggests that a modern technology, both hardware and software could be investigated to examine the application of drone technology and the appropriate systems namely Rural Grid System and Characteristics of Rural Malaysia System (CHARMs). An extensive literature review has been conducted to address the weaknesses of conventional method, the potential of drone technology in monitoring rural urban development, as well as the challenges of drone technology in term of limitation usage, pilot and auto pilot, and the rules and regulations. The finding shows that drone technology has its own strengths and weaknesses but its role in monitoring the rural urban development should be given priority. It is hoped that this paper can be a guideline for government agencies like local authorities, Department of Director General of Lands and Mines, and also private sector such as developer and investor who partake and apply modern monitoring through technological innovation notably in the land development process and site development progress.
Mapping of Affordable Housing Sustainability Level from Environmental Geospatial Aspect in Gombak, Selangor
Authors: Ismail Nurul Aini , Tarmidi Zakri , Mohamed Razali Muhammad Najib , Adi Maimun Nurul Hana , Hassan Noordyana , Md Nasir Ahmad Nabil , Sidek Akhmal , Che Ya Nik Norasma 
Published: August 2020

Abstract
Housing is important since it provides security, privacy, neighbourhood and social relations, status, community facilities and services, access to jobs and control over the environment. To enable everybody to afford a house, affordable housing was introduced and developed throughout the world. But there’s an issues with current affordable housing development, especially the sustainability of these affordable housing, especially from environmental aspects. Several initiatives have been done to improve current sustainability, but current initiatives lack of spatial indicators to assess the sustainability level, from environmental aspects. This study focus on assessment of sustainability level from environmental aspect. The assessment method consists of 3 main components; the green space availability, proximity to water features, and proximity of flood areas. The results show that most of the affordable housing in this study area was less sustainable in term of environmental aspects. From this results, proper planning and development to sustain the affordable housing surrounding area can be done by government, or other stakeholders in the future.
Earthquake Social Vulnerability Assessment Using Entropy Method
Authors: Jena Ratiranjan , Pradhan Biswajeet 
Published: August 2020

Abstract
Earthquake is the most devastating event in the current time. Given the probability of highly dangerous future events, risk estimation should be given focus by using the limited and freely available data to predict future vulnerable scenarios of an area that observe the involved uncertainty in the analysis. However, vulnerability assessments should be prospective and based on expected scientifically acceptable events. Therefore, we applied a valuable weight calculation approach called entropy to produce a social vulnerability map for a particular city. We used the population data, including educated and non-educated people and household information, to develop the earthquake social vulnerability map. We used entropy to evaluate the actual weight and produce a good quality map because of some difficulty in the fuzzy synthetic evaluation method for factor weight calculation and relationship ignorance among layers. Results showed that approximately 6% of the population is under very high vulnerability and around 14% are under high vulnerability areas in Banda Aceh City. The developed model is accurate by considering the inventory earthquake vulnerability map. The applied method was favorable, and the process provided good evaluation results, which was reasonable for earthquake hazard, vulnerability, and risk assessment.
GIS Based Multi-Criteria Decision Making for Identification of Important Plant Areas: A Case Study in Johor, Malaysia
Authors: Hamidah M , Mohd Hasmadi I , Chua LSL , Lau KH , Yong WSY 
Published: August 2020

Abstract
Important Plant Areas (IPA) is a concept developed by Plantlife International to identify sites of exceptional botanical importance for conservation priority. The three main criteria for IPA are presence of threatened species, exceptional botanical richness and threatened habitats. Studies have shown that scoring method based on expert judgement is widely used in IPA identification as it is the easiest to understand and implement. However, reported drawbacks were uncertainty in score judgement and effect of the weighted score on each criterion was not understood. Therefore, a robust scoring system was needed to overcome this obscurity. The current study utilized Kepong Herbarium records, GIS-based multi-criteria decision making and analytical hierarchy process to identify IPA in the state of Johor, Malaysia. These techniques were considered to be effective tools in providing decision support for spatial planning aimed for plant conservation in Malaysia.
Landslides Risk Prediction Using Cascade Neural Networks Model at Muş In Turkey
Authors: Abujayyab Sohaib K. M. , Saleh Azlan 
Published: August 2020

Abstract
Globally, landslides risk represent a challenging issue that negatively affecting the infrastructure and human and neutral life. Among the former studies, the methods of predicting landslides risk maps found to be need further experiments. The aim of this study was to predict the landslide risk map at Muş in Turkey using cascade neural networks model. In this article, Trainlm function used to train 9954 sample points in the dataset using Matlab software. ArcGIS employed to prepare the explanatory variables, inventory landslides map, data sampling and producing the final landslides risk map. The developed model achieved the best performance accuracy by implanting an optimizer for the used number of neurons. After 60 training experiments, 52 neurons found the best number in this model. Chunks computing using Python programing in ArcGIS implemented to solve the intensive computing and data restructuring issues. Although the implementation at regional scale with 14015 km2, the final landslides risk map was successfully produced. The best-achieved performance accuracy was 80% based on receiver operating characteristic curve (ROC) and area under the curve (AUC). To summarize, the cascade neural networks model can reliably be implement in predicting landslides risk maps at regional-scale with the aid of chunks computing.