• Journal of Forest and Environmental Science
• /
• v.28 no.3
• /
• pp.158-169
• /
• 2012

Sabah has experienced a rapid decline in the extent of forest cover. The precise impact of habitat loss on the conservation status of the plants of Sabah is uncertain. In this study we use the niche modelling algorithm MAXENT to construct preliminary, revised and final ecological niche models for Dipterocarpus lamellatus and Dipterocarpus ochraceus and combined these models with data on current land-use to derive conservation assessments for each species. Preliminary models were based on herbarium data alone. Ground surveys were conducted to evaluate the performance of these preliminary models, and a revised niche model was generated from the combined herbarium and ground survey data. The final model was obtained by constraining the predictions of the revised models by filters. The range overlap between the preliminary and revised models was 0.47 for D. lamellatus and 0.39 for D. ochraceus, suggesting poor agreement between them. There was substantial variation in estimates of habitat loss for D. ochraceus, among the preliminary, revised and constrained models, and this has the potential to lead to incorrect threat assessments. From these estimates of habitat loss, the historic distribution and estimates of population size we determine that both species should be classified as Critically Endangered under IUCN Red List guidelines. Our results suggest that ground-truthing of ecological niche models is essential, especially if the models are being used for conservation decision making.

• Journal of Environmental Health Sciences
• /
• v.41 no.2
• /
• pp.90-101
• /
• 2015

Objective: This study was conducted to develop a chemical oxygen demand (COD) regression model using water quality monitoring data (January, 2014) obtained from the Han River auto-monitoring stations. Methods: Surface water quality data at 198 sampling stations along the six major areas were assembled and analyzed to determine the spatial distribution and clustering of monitoring stations based on 18 WQPs and regression modeling using selected parameters. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR), cluster analysis (CA) and principal component analysis (PCA) were used to build a COD model using water quality data. Results: A best GA-MLR model facilitated computing the WQPs for a 5-descriptor COD model with satisfactory statistical results ($r^2=92.64$,$Q{^2}_{LOO}=91.45$,$Q{^2}_{Ext}=88.17$). This approach includes variable selection of the WQPs in order to find the most important factors affecting water quality. Additionally, ordination techniques like PCA and CA were used to classify monitoring stations. The biplot based on the first two principal components (PCs) of the PCA model identified three distinct groups of stations, but also differs with respect to the correlation with WQPs, which enables better interpretation of the water quality characteristics at particular stations as of January 2014. Conclusion: This data analysis procedure appears to provide an efficient means of modelling water quality by interpreting and defining its most essential variables, such as TOC and BOD. The water parameters selected in a COD model as most important in contributing to environmental health and water pollution can be utilized for the application of water quality management strategies. At present, the river is under threat of anthropogenic disturbances during festival periods, especially at upstream areas.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2008.05a
• /
• pp.40-48
• /
• 2008

The Waimea Basin is located on the northern tip of the South Island of New Zealand. It is a highly productive area with intense water use with multi-stakeholder interest in water. Irrigation from the underground aquifers here makes up the largest portion of used water; however the same aquifers are also the key urban and industrial sources of water. The Waimea/Wairoa Rivers are the main sources of recharge to the underlying aquifers and also feed the costal springs that highly valued by the community and iwi. Due to the location of the main rivers and springs close to the urban centre the water resource system here has high community and aesthetic values. Recent enhanced hydrological modelling work has shown the water resources in this area to be over allocated by 22% for a 1:10 year drought security for maintaining a minimalistic flow of 250 l/s in the lower Waimea River. The current irrigated land area is about 3700 hectares with an additional potential for irrigation of 1500 hectares. Further pressures are also coming on-line with significant population growth in the region. Recent droughts have resulted in significant water use cutbacks and the threat of seawater intrusion in the coastal margins. The Waimea Water Augmentation Committee (WWAC) initiated a three year stage 1 feasibility study in 2004/2005 into the viability of water storage in the upper parts of the catchment for enhancing water availability and its security of supply for consumptive, environmental, community and aesthetic benefits downstream. The project also sought to future proof water supply needs for the Waimea Plains and the surrounding areas for a 50 - 100 year planning horizon. The broad range stage 1 investigation programme has identified the Upper Lee Catchment as being suitable for a storage structure to provide the needs identified and also a possibility for some small scale hydro electricity generation as well. The stage 2 detailed feasibility investigations that are underway now (2007/2008), and to be completed in two years is to provide all details for progressing with the next stage of obtaining necessary permits for construction and commissioning a suitable dam.

• Geomechanics and Engineering
• /
• v.12 no.3
• /
• pp.523-539
• /
• 2017

Clay soils are typical for their swelling properties upon absorption of water during rains and development of cracks during summer time owing to the profile desorption of water through the inter-connected soil pores by water vapour diffusion leading to evaporation. This type of unstable soil phenomenon by and large poses a serious threat to the strength and stability of structures when rest on such type of soils. Even as lime and cement are extensively used for stabilization of clay soils it has become imperative to find relatively cheaper alternative materials to bring out the desired properties within the clay soil domain. In the present era of catastrophic environmental degradation as a side effect to modernized manufacturing processes, industrialization and urbanization the creative idea would be treating the waste products in a beneficial way for reuse and recycling. Bottom ash and ecosand are construed as a waste product from cement industry. An optimal combination of bottom ash-eco sand can be thought of as a viable alternative to stabilize the clay soils by means of an effective dispersion dynamics associated with the inter connected network of pore spaces. A CATIA model was created and imported to ANSYS Fluent to study the dispersion dynamics. Ion migration from the bottom ash-ecosand pile was facilitated through natural formation of cracks in clay soil subjected to atmospheric conditions. Treated samples collected at different curing days from inner and outer zones at different depths were tested for, plasticity index, Unconfined Compressive Strength (UCS), free swell index, water content, Cation Exchange Capacity (CEC), pH and ion concentration to show the effectiveness of the method in improving the clay soil.

• Structural Engineering and Mechanics
• /
• v.77 no.4
• /
• pp.441-461
• /
• 2021

Reinforced concrete (RC) columns are crucial in building structures and they are of higher vulnerability to terrorist threat than any other structural elements. Thus it is of great interest and necessity to achieve a comprehensive understanding of the possible responses of RC columns when exposed to high intensive blast loads. The primary objective of this study is to derive analytical formulas to assess vulnerability of RC columns using an advanced numerical modelling approach. This investigation is necessary as the effect of blast loads would be minimal to the RC structure if the explosive charge is located at the safe standoff distance from the main columns in the building and therefore minimizes the chance of disastrous collapse of the RC columns. In the current research, finite element model is developed for RC columns using LS-DYNA program that includes a comprehensive discussion of the material models, element formulation, boundary condition and loading methods. Numerical model is validated to aid in the study of RC column testing against the explosion field test results. Residual capacity of RC column is selected as damage criteria. Intensive investigations using Arbitrary Lagrangian Eulerian (ALE) methodology are then implemented to evaluate the influence of scaled distance, column dimension, concrete and steel reinforcement properties and axial load index on the vulnerability of RC columns. The generated empirical formulae can be used by the designers to predict a damage degree of new column design when consider explosive loads. With an extensive knowledge on the vulnerability assessment of RC structures under blast explosion, advancement to the convention design of structural elements can be achieved to improve the column survivability, while reducing the lethality of explosive attack and in turn providing a safer environment for the public.

• International Journal of Computer Science & Network Security
• /
• v.22 no.6
• /
• pp.390-399
• /
• 2022

Cyber security and resilience are phrases that describe safeguards of ICTs (information and communication technologies) from cyber-attacks or mitigations of cyber event impacts. The sole purpose of Risk models are detections, analyses, and handling by considering all relevant perceptions of risks. The current research effort has resulted in the development of a new paradigm for safeguarding services offered online which can be utilized by both service providers and users. customers. However, rather of relying on detailed studies, this approach emphasizes task selection and execution that leads to successful risk treatment outcomes. Modelling intelligent CSGs (Cyber Security Games) using MLTs (machine learning techniques) was the focus of this research. By limiting mission risk, CSGs maximize ability of systems to operate unhindered in cyber environments. The suggested framework's main components are the Threat and Risk models. These models are tailored to meet the special characteristics of online services as well as the cyberspace environment. A risk management procedure is included in the framework. Risk scores are computed by combining probabilities of successful attacks with findings of impact models that predict cyber catastrophe consequences. To assess successful attacks, models emulating defense against threats can be used in topologies. CSGs consider widespread interconnectivity of cyber systems which forces defending all multi-step attack paths. In contrast, attackers just need one of the paths to succeed. CSGs are game-theoretic methods for identifying defense measures and reducing risks for systems and probe for maximum cyber risks using game formulations (MiniMax). To detect the impacts, the attacker player creates an attack tree for each state of the game using a modified Extreme Gradient Boosting Decision Tree (that sees numerous compromises ahead). Based on the findings, the proposed model has a high level of security for the web sources used in the experiment.

• Korean Journal of Ecology and Environment
• /
• v.56 no.3
• /
• pp.268-279
• /
• 2023

Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier's abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.