ORCID Profile
0000-0002-5100-8921
Current Organisations
University of New England
,
Qatar University
,
Flinders University
,
RMIT University
,
Macquarie University
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Publisher: PeerJ
Date: 19-03-2018
DOI: 10.7717/PEERJ.4474
Abstract: Aedes albopictus , the Asian Tiger Mosquito, vector of Chikungunya, Dengue Fever and Zika viruses, has proven its hardy adaptability in expansion from its natural Asian, forest edge, tree hole habitat on the back of international trade transportation, re-establishing in temperate urban surrounds, in a range of water receptacles and semi-enclosures of organic matter. Conventional aerial spray mosquito vector controls focus on wetland and stagnant water expanses, proven to miss the protected hollows and crevices favoured by Ae. albopictus. New control or eradication strategies are thus essential, particular in light of potential expansions in the southeastern and eastern USA. Successful regional vector control strategies require risk level analysis. Should strategies prioritize regions with non-climatic or climatic suitability parameters for Ae. albopictus ? Our study used current Ae. albopictus distribution data to develop two independent models: (i) regions with suitable non-climatic factors, and (ii) regions with suitable climate for Ae. albopictus in southeastern USA. Non-climatic model processing used Evidential Belief Function (EBF), together with six geographical conditioning factors (raster data layers), to establish the probability index. Validation of the analysis results was estimated with area under the curve (AUC) using Ae. albopictus presence data. Climatic modeling was based on two General Circulation Models (GCMs), Miroc3.2 and CSIRO-MK30 running the RCP 8.5 scenario in MaxEnt software. EBF non-climatic model results achieved a 0.70 prediction rate and 0.73 success rate, confirming suitability of the study site regions for Ae. albopictus establishment. The climatic model results showed the best-fit model comprised Coldest Quarter Mean Temp, Precipitation of Wettest Quarter and Driest Quarter Precipitation factors with mean AUC value of 0.86. Both GCMs showed that the whole study site is highly suitable and will remain suitable climatically, according to the prediction for 2055, for Ae. albopictus expansion.
Publisher: MDPI AG
Date: 10-11-2020
DOI: 10.3390/RS12223682
Abstract: This study predicts forest fire susceptibility in Chaloos Rood watershed in Iran using three machine learning (ML) models—multivariate adaptive regression splines (MARS), support vector machine (SVM), and boosted regression tree (BRT). The study utilizes 14 set of fire predictors derived from vegetation indices, climatic variables, environmental factors, and topographical features. To assess the suitability of the models and estimating the variance and bias of estimation, the training dataset obtained from the Natural Resources Directorate of Mazandaran province was subjected to res ling using cross validation (CV), bootstrap, and optimism bootstrap techniques. Using variance inflation factor (VIF), weight indicating the strength of the spatial relationship of the predictors to fire occurrence was assigned to each contributing variable. Subsequently, the models were trained and validated using the receiver operating characteristics (ROC) area under the curve (AUC) curve. Results of the model validation based on the res ling techniques (non, 5- and 10-fold CV, bootstrap and optimism bootstrap) produced AUC values of 0.78, 0.88, 0.90, 0.86 and 0.83 for the MARS model 0.82, 0.82, 0.89, 0.87, 0.84 for the SVM and 0.87, 0.90, 0.90, 0.90, 0.91 for the BRT model. Across the in idual model, the 10-fold CV performed best in MARS and SVM with AUC values of 0.90 and 0.89. Overall, the BRT outperformed the other models in all ramification with highest AUC value of 0.91 using optimism bootstrap res ling algorithm. Generally, the res ling process enhanced the prediction performance of all the models.
Publisher: Springer Science and Business Media LLC
Date: 19-07-2017
DOI: 10.1038/S41598-017-05804-0
Abstract: At the global level, maize is the third most important crop on the basis of harvested area. Given its importance, an assessment of the variation in regional climatic suitability under climate change is critical. CliMond 10′ data were used to model the potential current and future climate distribution of maize at the global level using the CLIMEX distribution model with climate data from two general circulation models, CSIRO-Mk3.0 and MIROC-H, assuming an A2 emissions scenario for 2050 and 2100. The change in area under future climate was analysed at continental level and for major maize-producing countries of the world. Regions between the tropics of Cancer and Capricorn indicate the highest loss of climatic suitability, contrary to poleward regions that exhibit an increase of suitability. South America shows the highest loss of climatic suitability, followed by Africa and Oceania. Asia, Europe and North America exhibit an increase in climatic suitability. This study indicates that globally, large areas that are currently suitable for maize cultivation will suffer from heat and dry stresses that may constrain production. For the first time, a model was applied worldwide, allowing for a better understanding of areas that are suitable and that may remain suitable for maize.
Publisher: Informa UK Limited
Date: 03-07-2014
Publisher: Public Library of Science (PLoS)
Date: 10-12-2013
Publisher: Elsevier BV
Date: 2014
Publisher: Wiley
Date: 20-06-2017
DOI: 10.1111/JPH.12593
Publisher: MDPI AG
Date: 13-09-2023
DOI: 10.3390/RS15184501
Publisher: Informa UK Limited
Date: 25-12-2019
Publisher: Elsevier BV
Date: 07-2015
Publisher: Springer Science and Business Media LLC
Date: 12-01-2014
Publisher: MDPI AG
Date: 28-05-2020
DOI: 10.3390/RS12111737
Abstract: Predicting landslide occurrences can be difficult. However, failure to do so can be catastrophic, causing unwanted tragedies such as property damage, community displacement, and human casualties. Research into landslide susceptibility mapping (LSM) attempts to alleviate such catastrophes through the identification of landslide prone areas. Computational modelling techniques have been successful in related disaster scenarios, which motivate this work to explore such modelling for LSM. In this research, the potential of supervised machine learning and ensemble learning is investigated. Firstly, the Flexible Discriminant Analysis (FDA) supervised learning algorithm is trained for LSM and compared against other algorithms that have been widely used for the same purpose, namely Generalized Logistic Models (GLM), Boosted Regression Trees (BRT or GBM), and Random Forest (RF). Next, an ensemble model consisting of all four algorithms is implemented to examine possible performance improvements. The dataset used to train and test all the algorithms consists of a landslide inventory map of 227 landslide locations. From these sources, 13 conditioning factors are extracted to be used in the models. Experimental evaluations are made based on True Skill Statistic (TSS), the Receiver Operation characteristic (ROC) curve and kappa index. The results show that the best TSS (0.6986), ROC (0.904) and kappa (0.6915) were obtained by the ensemble model. FDA on its own seems effective at modelling landslide susceptibility from multiple data sources, with performance comparable to GLM. However, it slightly underperforms when compared to GBM (BRT) and RF. RF seems most capable compared to GBM, GLM, and FDA, when dealing with all conditioning factors.
Publisher: Wiley
Date: 10-07-2019
DOI: 10.1002/LDR.3375
Publisher: Springer Science and Business Media LLC
Date: 11-09-2018
Publisher: Elsevier BV
Date: 12-2014
Publisher: MDPI AG
Date: 19-08-2016
DOI: 10.3390/NU8080510
Publisher: MDPI AG
Date: 05-04-2023
DOI: 10.3390/RS15071939
Abstract: The level of destruction caused by an earthquake depends on a variety of factors, such as magnitude, duration, intensity, time of occurrence, and underlying geological features, which may be mitigated and reduced by the level of preparedness of risk management measures. Geospatial technologies offer a means by which earthquake occurrence can be predicted or foreshadowed managed in terms of levels of preparation related to land use planning availability of emergency shelters, medical resources, and food supplies and assessment of damage and remedial priorities. This literature review paper surveys the geospatial technologies employed in earthquake research and disaster management. The objectives of this review paper are to assess: (1) the role of the range of geospatial data types (2) the application of geospatial technologies to the stages of an earthquake (3) the geospatial techniques used in earthquake hazard, vulnerability, and risk analysis and (4) to discuss the role of geospatial techniques in earthquakes and related disasters. The review covers past, current, and potential earthquake-related applications of geospatial technology, together with the challenges that limit the extent of usefulness and effectiveness. While the focus is mainly on geospatial technology applied to earthquake research and management in practice, it also has validity as a framework for natural disaster risk assessments, emergency management, mitigation, and remediation, in general.
Publisher: Springer Science and Business Media LLC
Date: 13-10-2016
DOI: 10.1007/S00484-016-1256-2
Abstract: Seasonal variations are important components in understanding the ecology of insect population of crops. Ecological studies through modeling may be a useful tool for enhancing knowledge of seasonal patterns of insects on field crops as well as seasonal patterns of favorable climatic conditions for species. Recently CLIMEX, a semi-mechanistic niche model, was upgraded and enhanced to consider spatio-temporal dynamics of climate suitability through time. In this study, attempts were made to determine monthly variations of climate suitability for Neoleucinodes elegantalis (Guenée) (Lepidoptera: Crambidae) in five commercial tomato crop localities through the latest version of CLIMEX. We observed that N. elegantalis displays seasonality with increased abundance in tomato crops during summer and autumn, corresponding to the first 6 months of the year in monitored areas in this study. Our model demonstrated a strong accord between the CLIMEX weekly growth index (GI
Publisher: Elsevier BV
Date: 08-2023
Publisher: Elsevier BV
Date: 11-2023
Publisher: Springer Science and Business Media LLC
Date: 08-02-2018
DOI: 10.1038/S41598-018-20942-9
Abstract: Citric acid (CA) was evaluated in terms of its efficiency as a biodegradable chelating agent, in removing zinc (Zn) from heavily contaminated soil, using a soil washing process. To determine preliminary ranges of variables in the washing process, single factor experiments were carried out with different CA concentrations, pH levels and washing times. Optimization of batch washing conditions followed using a response surface methodology (RSM) based central composite design (CCD) approach. CCD predicted values and experimental results showed strong agreement, with an R 2 value of 0.966. Maximum removal of 92.8% occurred with a CA concentration of 167.6 mM, pH of 4.43, and washing time of 30 min as optimal variable values. A leaching column experiment followed, to examine the efficiency of the optimum conditions established by the CCD model. A comparison of two soil washing techniques indicated that the removal efficiency rate of the column experiment (85.8%) closely matching that of the batch experiment (92.8%). The methodology supporting the research experimentation for optimizing Zn removal may be useful in the design of protocols for practical engineering soil decontamination applications
Publisher: Informa UK Limited
Date: 15-02-2019
Publisher: Wiley
Date: 31-03-2017
Abstract: Environment and genetic factors play an important role in the development of obesity, and diet is one of the main contributing factors to this disease. High fat intake is associated with body weight gain, leading to obesity and other metabolic diseases. MicroRNAs (miRNAs) are a group of small, noncoding RNAs that are important regulators of gene expression at posttranscriptional level. Studies have shown that high fat intake, independent of body weight status, can significantly impact both negatively and positively the expression of miRNAs and thus the biological function of tissues such as adipose, skeletal, and cardiac muscle, liver, neuronal, and endothelial. This review will summarize the effects of high calorie diet in the form of high fat intake on miRNA expression in various tissues of animal models and of high fat fed offspring. We will also briefly review the impact of different dietary lipids on miRNA expression. Given changes in miRNA expression have been associated with the development of many diseases including obesity, understanding their biological role could have important clinical implications and offer tangible therapeutic targets for the prevention, management, and/or treatment of obesity and other lifestyle-related disorders.
Publisher: Informa UK Limited
Date: 09-10-2017
Publisher: Cambridge University Press (CUP)
Date: 26-04-2017
DOI: 10.1017/S0021859617000260
Abstract: Date palm ( Phoenix dactylifera L.) is an important cash crop in many countries, including Saudi Arabia. Understanding the likely potential distribution of this crop under current and future climate scenarios will enable environmental managers to prepare appropriate strategies to manage the changes. In the current study, the simulation model CLIMEX was used to develop a niche model to estimate the impacts of climate change on the current and future potential distribution of date palm. Two global climate models (GCMs), CSIRO-Mk3·0 and MIROC-H under the A2 emission scenario for 2050 and 2100, were used to assess the impacts of climate change. A sensitivity analysis was conducted to identify which model parameters had the most effect on date palm distribution. Further refinements of the potential distributions were performed through the integration of six non-climatic parameters in a geographic information system. Areas containing suitable soil taxonomy, soil texture, soil salinity, land use, landform and slopes of ° for date palm were selected as suitable refining variables in order to achieve more realistic models. The results from both GCMs exhibited a significant reduction in climatic suitability for date palm cultivation in Saudi Arabia by 2100. Climate sensitivity analysis indicates that the lower optimal soil moisture, cold stress temperature threshold and wet stress threshold parameters had the most effect on sensitivity, while other parameters were moderately sensitive or insensitive to change. The study also demonstrated that the inclusion of non-climatic parameters with CLIMEX outputs increased the explanatory power of the models. Such models can provide early warning scenarios for how environmental managers should respond to changes in the distribution of the date palm in Saudi Arabia.
Publisher: Public Library of Science (PLoS)
Date: 10-04-2014
Publisher: Public Library of Science (PLoS)
Date: 14-06-2018
Publisher: Informa UK Limited
Date: 11-08-2017
Publisher: Wiley
Date: 27-07-2016
DOI: 10.1002/ECE3.2332
Publisher: Wiley
Date: 02-08-2017
DOI: 10.1002/PS.4344
Abstract: Neoleucinodes elegantalis is one of the major insect pests of Solanum lycopersicum. Currently, N. elegantalis is present only in America and the Caribbean, and is a threat in the world's largest S. lycopersicum-producing countries. In terms of potential impact on agriculture, the impact of climate change on insect invasions must be a concern. At present, no research exists regarding the effects of climatic change on the risk level of N. elegantalis. The purpose of this study was to develop a model for S. lycopersicum and N. elegantalis, utilizing CLIMEX to determine risk levels of N. elegantalis in open-field S. lycopersicum cultivation in the present and under projected climate change, using the global climate model CSIRO-Mk3.0. Large areas are projected to be suitable for N. elegantalis and optimal for open-field S. lycopersicum cultivation at the present time. However, in the future these areas will become unsuitable for both species. Conversely, other regions in the future may become optimal for open-field S. lycopersicum cultivation, with a varying risk level for N. elegantalis. The risk level results presented here provide a useful tool to design strategies to prevent the introduction and establishment of N. elegantalis in open-field S. lycopersicum cultivation. © 2016 Society of Chemical Industry.
Publisher: Elsevier BV
Date: 07-2018
Publisher: Informa UK Limited
Date: 13-05-2016
Publisher: PeerJ
Date: 05-09-2018
DOI: 10.7717/PEERJ.5545
Abstract: Climate change has determined shifts in distributions of species and is likely to affect species in the future. Our study aimed to (i) demonstrate the linkage between spatial climatic variability and the current and historical Dubas bug ( Ommatissus lybicus Bergevin) distribution in Oman and (ii) model areas becoming highly suitable for the pest in the future. The Dubas bug is a pest of date palm trees that can reduce the crop yield by 50% under future climate scenarios in Oman. Projections were made in three species distribution models generalized linear model, maximum entropy, boosted regression tree using of four global circulation models (GCMs) (a) HadGEM2, (b) CCSM4, (c) MIROC5 and (d) HadGEM2-AO, under four representative concentration pathways (2.6, 4.5, 6.0 and 8.5) for the years 2050 and 2070. We utilized the most commonly used threshold of maximum sensitivity + specificity for classifying outputs. Results indicated that northern Oman is currently at great risk of Dubas bug infestations (highly suitable climatically) and the infestations level will remain high in 2050 and 2070. Other non-climatic integrated pest management methods may be greater value than climatic parameters for monitoring infestation levels, and may provide more effective strategies to manage Dubas bug infestations in Oman. This would ensure the continuing competitiveness of Oman in the global date fruit market and preserve national yields.
Publisher: Cambridge University Press (CUP)
Date: 09-09-2017
DOI: 10.1017/S0021859616000654
Abstract: Tomato ( Solanum lycopersicum L.) is one of the most important vegetable crops globally and an important agricultural sector for generating employment. Open field cultivation of tomatoes exposes the crop to climatic conditions, whereas greenhouse production is protected. Hence, global warming will have a greater impact on open field cultivation of tomatoes rather than the controlled greenhouse environment. Although the scale of potential impacts is uncertain, there are techniques that can be implemented to predict these impacts. Global climate models (GCMs) are useful tools for the analysis of possible impacts on a species. The current study aims to determine the impacts of climate change and the major factors of abiotic stress that limit the open field cultivation of tomatoes in both the present and future, based on predicted global climate change using CLIMatic indEX and the A2 emissions scenario, together with the GCM Commonwealth Scientific and Industrial Research Organisation (CSIRO)-Mk3·0 (CS), for the years 2050 and 2100. The results indicate that large areas that currently have an optimum climate will become climatically marginal or unsuitable for open field cultivation of tomatoes due to progressively increasing heat and dry stress in the future. Conversely, large areas now marginal and unsuitable for open field cultivation of tomatoes will become suitable or optimal due to a decrease in cold stress. The current model may be useful for plant geneticists and horticulturalists who could develop new regional stress-resilient tomato cultivars based on needs related to these modelling projections.
Publisher: Cambridge University Press (CUP)
Date: 10-09-2015
DOI: 10.1017/S001447971400026X
Abstract: One consequence of climate change is change in the phenology and distribution of plants, including the date palm ( Phoenix dactylifera L.). Date palm, as a crop specifically adapted to arid conditions in desert oases and to very high temperatures, may be dramatically affected by climate changes. Some areas that are climatically suitable for date palm growth at the present time will become climatically unsuitable in the future, while other areas that are unsuitable under current climate will become suitable in the future. This study used CLIMEX to estimate potential date palm distribution under current and future climate scenarios using one emission scenario (A2) with two different global climate models (GCMs), CSIRO-Mk3.0 (CS) and MIROC-H (MR). The results of this study indicated that Saudi Arabia, Iraq and Iran are most affected countries as a result of climate change. In Saudi Arabia, 129 million ha (68%) of currently suitable area is projected to become unsuitable by 2100. However, this is based on climate modelling alone. The actual decrease in area may be much smaller when abiotic and other factors are taken into account. On the other hand, 13 million ha (33%) of currently unsuitable area is projected to become suitable by 2100 in Iran. Additionally, by 2050, Israel, Jordan and western Syria will become climatically more suitable. Cold and heat stresses will play a significant role in date palm distribution in the future. These results can inform strategic planning by government and agricultural organizations to identify areas for cultivation of this profitable crop in the future, and to address those areas that will need greater attention because they are becoming marginal regions for date palm cultivation.
Publisher: Cambridge University Press (CUP)
Date: 26-11-2014
DOI: 10.1017/S0021859613000816
Abstract: The objective of the present paper is to use CLIMEX software to project how climate change might impact the future distribution of date palm ( Phoenix dactylifera L.) in Iran. Although the outputs of this software are only based on the response of a species to climate, the CLIMEX results were refined in the present study using two non-climatic parameters: ( a ) the location of soils containing suitable physicochemical properties and ( b ) the spatial distribution of soil types having suitable soil taxonomy for dates, as unsuitable soil types impose problems in air permeability, hydraulic conductivity and root development. Here, two different Global climate models (GCMs), CSIRO-Mk3.0 (CS) and MIROC-H (MR), were employed with the A2 emission scenario to model the potential date palm distribution under current and future climates in Iran for the years 2030, 2050, 2070 and 2100. The results showed that only c . 0·30 of the area identified as suitable by CLIMEX will actually be suitable for date palm cultivation: the rest of the area comprises soil types that are not favourable for date palm cultivation. Moreover, the refined outputs indicate that the total area suitable for date palm cultivation will increase to 31·3 million ha by 2100, compared with 4·8 million ha for current date palm cultivation. The present results also indicate that only heat stress will have an impact on date palm distribution in Iran by 2100, with the areas currently impacted by cold stress diminishing by 2100.
Publisher: Elsevier BV
Date: 09-2022
Publisher: Wiley
Date: 10-07-2023
DOI: 10.1111/ECOG.06619
Abstract: Species interactions play a fundamental role in ecosystems. However, few ecological communities have complete data describing such interactions, which is an obstacle to understanding how ecosystems function and respond to perturbations. Because it is often impractical to collect empirical data for all interactions in a community, various methods have been developed to infer interactions. Machine learning is increasingly being used for making interaction predictions, with random forest being one of the most frequently used of these methods. However, performance of random forest in inferring predator‐prey interactions in terrestrial vertebrates and its sensitivity to training data quality remain untested. We examined predator–prey interactions in two erse, primarily terrestrial vertebrate classes: birds and mammals. Combining data from a global interaction dataset and a specific community (Simpson Desert, Australia), we tested how well random forest predicted predator–prey interactions for mammals and birds using species' ecomorphological and phylogenetic traits. We also tested how variation in training data quality – manipulated by removing records and switching interaction records to non‐interactions – affected model performance. We found that random forest could predict predator–prey interactions for birds and mammals using ecomorphological or phylogenetic traits, correctly predicting up to 88 and 67% of interactions and non‐interactions in the global and community‐specific datasets, respectively. These predictions were accurate even when there were no records in the training data for focal species. In contrast, false non‐interactions for focal predators in training data strongly degraded model performance. Our results demonstrate that random forest can identify predator–prey interactions for birds and mammals that have few or no interaction records. Furthermore, our study provides guidance on how to prepare training data to optimise machine learning classifiers for predicting species interactions, which could help ecologists 1) address knowledge gaps and explore network‐related questions in data‐poor situations, and 2) predict interactions for range‐expanding species.
Publisher: Public Library of Science (PLoS)
Date: 23-09-2013
Publisher: Springer Science and Business Media LLC
Date: 08-02-2018
DOI: 10.1038/S41598-018-20968-Z
Abstract: We studied the effects of soil matric potential and salinity on the water use (WU), water use efficiency (WUE) and yield response factor (Ky), for wheat ( Triticum aestivum cv. Mahdavi) and bean ( Phaseoulus vulgaris cv. COS16) in sandy loam and clay loam soils under greenhouse conditions. Results showed that aeration porosity is the predominant factor controlling WU, WUE, Ky and shoot biomass (Bs) at high soil water potentials. As matric potential was decreased, soil aeration improved, with Bs, WU and Ky reaching maximum value at −6 to −10 kPa, under all salinities. Wheat WUE remained almost unchanged by reduction of matric potential under low salinities (EC ≤ 8 dSm −1 ), but increased under higher salinities (EC ≥ 8 dSm −1 ), as did bean WUE at all salinities, as matric potential decreased to −33 kPa. Wheat WUE exceeds that of bean in both sandy loam and clay loam soils. WUE of both plants increased with higher shoot/root ratio and a high correlation coefficient exists between them. Results showed that salinity decreases all parameters, particularly at high potentials (h = −2 kPa), and lifies the effects of waterlogging. Further, we observed a strong relationship between transpiration (T) and root respiration (Rr) for all experiments.
Publisher: Cold Spring Harbor Laboratory
Date: 05-09-2022
DOI: 10.1101/2022.09.02.506446
Abstract: Species interactions play a fundamental role in ecosystems. However, few ecological communities have complete data describing such interactions, which is an obstacle to understanding how ecosystems function and respond to perturbations. Because it is often impractical to collect empirical data for all interactions in a community, various methods have been developed to infer interactions. Machine learning is increasingly being used for making interaction predictions, with random forest being one of the most frequently used of these methods. However, performance of random forest in inferring predator-prey interactions in terrestrial vertebrates and its sensitivity to training data quality remain untested. We examined predator-prey interactions in two erse, primarily terrestrial vertebrate classes: birds and mammals. Combining data from a global interaction dataset and a specific community (Simpson Desert, Australia), we tested how well random forest predicted predator-prey interactions for mammals and birds using species’ ecomorphological and phylogenetic traits. We also tested how variation in training data quality—manipulated by removing records and switching interaction records to non-interactions—affected model performance. We found that random forest could predict predator-prey interactions for birds and mammals using ecomorphological or phylogenetic traits, correctly predicting up to 88% and 67% of interactions and non-interactions in the global and community-specific datasets, respectively. These predictions were accurate even when there were no records in the training data for focal species. In contrast, false non-interactions for focal predators in training data strongly degraded model performance. Our results demonstrate that random forest can identify predator-prey interactions for birds and mammals that have few or no interaction records. Furthermore, our study provides guidance on how to prepare training data to optimise machine-learning classifiers for predicting species interactions, which could help ecologists ( i ) address knowledge gaps and explore network-related questions in data-poor situations, and ( ii ) predict interactions for range-expanding species.
Publisher: Hindawi Limited
Date: 22-04-2021
DOI: 10.1155/2021/6638241
Abstract: The survival of humanity is dependent on the survival of forests and the ecosystems they support, yet annually wildfires destroy millions of hectares of global forestry. Wildfires take place under specific conditions and in certain regions, which can be studied through appropriate techniques. A variety of statistical modeling methods have been assessed by researchers however, ensemble modeling of wildfire susceptibility has not been undertaken. We hypothesize that ensemble modeling of wildfire susceptibility is better than a single modeling technique. This study models the occurrence of wildfire in the Brisbane Catchment of Australia, which is an annual event, using the index of entropy (IoE), evidential belief function (EBF), and logistic regression (LR) ensemble techniques. As a secondary goal of this research, the spatial distribution of the wildfire risk from different aspects such as urbanization and ecosystem was evaluated. The highest accuracy (88.51%) was achieved using the ensemble EBF and LR model. The outcomes of this study may be helpful to particular groups such as planners to avoid susceptible and risky regions in their planning model builders to replace the traditional in idual methods with ensemble algorithms and geospatial users to enhance their knowledge of geographic information system (GIS) applications.
Publisher: Springer Science and Business Media LLC
Date: 22-02-2017
Publisher: Springer Science and Business Media LLC
Date: 28-04-2015
Publisher: Springer Science and Business Media LLC
Date: 03-2017
Publisher: Springer Science and Business Media LLC
Date: 27-01-2201
Publisher: Elsevier BV
Date: 09-2017
Publisher: Elsevier BV
Date: 12-2017
Publisher: Springer Science and Business Media LLC
Date: 21-04-2022
Publisher: Cambridge University Press (CUP)
Date: 08-08-2016
DOI: 10.1017/S0021859616000605
Abstract: Palm oil (PO) is a very important commodity used as food, in pharmaceuticals, for cooking and as biodiesel: PO is a major contributor to the economies of many countries, especially Indonesia and Malaysia. Novel tropical regions are being explored increasingly to grow oil palm as current land decreases, whilst recent published modelling studies by the current authors for Malaysia and Indonesia indicate that the climate will become less suitable. Countries that grow the crop commercially include those in Latin America, Africa and Asia. How will climate change (CC) affect the ability to grow oil palm in these countries? Worldwide projections for apt climate were made using Climex software in the present paper and the global area with unsuitable climate was assessed to increase by 6%, whilst highly suitable climate (HSC) decreased by 22% by 2050. The suitability decreases are dramatic by 2100 suggesting regions totally unsuitable for growing OP, which are currently appropriate: the global area with unsuitable climate increased from 154 to 169 million km 2 and HSC decreased from 17 to 4 million km 2 . This second assessment of Indonesia and Malaysia confirmed the original findings by the current authors of large decreases in suitability. Many parts of Latin America and Africa were dramatically decreased: reductions in HSC for Brazil, Columbia and Nigeria are projected to be 119 000, 35 and 1 from 5 000 000, 219 and 69 km 2 , respectively. However, increases in aptness were observed in 2050 for Paraguay and Madagascar (HSC increases were 90 and 41%, respectively), which were maintained until 2100 (95 and 45%, respectively). Lesser or transient increases were seen for a few other countries. Hot, dry and cold climate stresses upon oil palm for all regions are also provided. These results have negative implications for growing oil palm in countries as: (a) alternatives to Malaysia and Indonesia or (b) economic resources per se . The inability to grow oil palm may assist in amelioration of CC, although the situation is complex. Data suggest a moderate movement of apposite climate towards the poles as previously predicted.
Publisher: Cambridge University Press (CUP)
Date: 23-05-2017
DOI: 10.1017/S0021859617000314
Abstract: Spodoptera frugiperda , or the fall armyworm (FAW) (Lepidoptera: Noctuidae), is an endemic and important agricultural pest in America. Several outbreaks have occurred with losses estimated at millions of dollars. Insects are affected by climate factors, and climate change may affect geographical range, growth rate, abundance, survival, mortality, number of generations per year and other characteristics. These effects are difficult to project due to the complex interactions among insects, hosts and predators. The aim of the current research is to project the impact of climate change on future suitability for the expansion and final range of FAW as well as highlight the risk of damage due to the pest under current and future conditions. The modelling was carried out using two general circulation models (GCMs), CSIRO Mk3.0 and MIROC-H, for 2050 and 2100 under the A2 Special Report on Emissions Scenarios (SRES), using the known distribution of the species and the CliMond meteorological database. The possible number of generations was estimated to exceed five in the south-eastern USA by 2100. A unique modelling approach linking environmental suitability and number of generations was developed to project the risks of FAW damage. The results show changes in suitability and risk across America, with an increase in the northern hemisphere and decreases or extinction in the southern hemisphere, except for southern Brazil, Uruguay, Paraguay and northern Argentina, which indicate high future levels of risk. The current study highlights the possible extinction of a tropical pest in areas near the Equator. The two GCMs both projected increases in the low-risk category of 40% by 2050 and 23% by 2100, with the medium- and high-risk categories decreasing by % by 2050 and % by 2100, compared with the current risk. In general, agricultural pest management may become more challenging under future climate change and variation, and thus, understanding and quantifying the possible impacts of FAW under future climate conditions is essential for the future economic production of crops.
Publisher: Public Library of Science (PLoS)
Date: 24-10-2012
Publisher: Springer Science and Business Media LLC
Date: 02-05-2017
Publisher: MDPI AG
Date: 30-03-2020
Abstract: Forest fire is an environmental disaster that poses immense threat to public safety, infrastructure, and bio ersity. Therefore, it is essential to have a rapid and robust method to produce reliable forest fire maps, especially in a data-poor country or region. In this study, the knowledge-based qualitative Analytic Hierarchy Process (AHP) and the statistical-based quantitative Frequency Ratio (FR) techniques were utilized to model forest fire-prone areas in the Himalayan Kingdom of Bhutan. Seven forest fire conditioning factors were used: land-use land cover, distance from human settlement, distance from road, distance from international border, aspect, elevation, and slope. The fire-prone maps generated by both models were validated using the Area Under Curve assessment method. The FR-based model yielded a fire-prone map with higher accuracy (87% success rate 82% prediction rate) than the AHP-based model (71% success rate 63% prediction rate). However, both the models showed almost similar extent of ‘very high’ prone areas in Bhutan, which corresponded to coniferous-dominated areas, lower elevations, steeper slopes, and areas close to human settlements, roads, and the southern international border. Moderate Resolution Imaging Spectroradiometer (MODIS) fire points were overlaid on the model generated maps to assess their reliability in predicting forest fires. They were found to be not reliable in Bhutan, as most of them overlapped with fire-prone classes, such as ‘moderate’, ‘low’, and ‘very low’. The fire-prone map derived from the FR model will assist Bhutan’s Department of Forests and Park Services to update its current National Forest Fire Management Strategy.
Publisher: Elsevier BV
Date: 08-2017
Publisher: Cambridge University Press (CUP)
Date: 19-06-2016
DOI: 10.1017/S0021859615000398
Abstract: The present study applies refined and improved scenarios for climate change to quantify the effects of potential alterations in climatic factors on localities for wheat and cotton production, which are two crops important to Australia's economy. The future distributions of Gossypium (cotton) and Triticum aestivum L. (wheat) were modelled using CLIMEX software with the A2 emission scenario generated by CSIRO-Mk3·0 and MIROC-H global climate models. The results were correlated to identify areas suitable for these economically important crops for the years 2030, 2050, 2070 and 2100 in Australia. The analysis shows that the areas where wheat and cotton can be grown in Australia will diminish from 2030 to 2050 and 2070 through to 2100. While cotton can be grown over extensive areas of the country until 2070, the area grown to wheat will decrease significantly over the period.
Publisher: Elsevier BV
Date: 09-2015
Publisher: Springer Science and Business Media LLC
Date: 24-01-2019
DOI: 10.1007/S00484-018-01661-2
Abstract: The whitefly, Bemisia tabaci, is considered one of the most important pests for tomato Solanum lycopersicum. The population density of this pest varies throughout the year in response to seasonal variation. Studies of seasonality are important to understand the ecological dynamics and insect population in crops and help to identify which seasons have the best climatic conditions for the growth and development of this insect species. In this research, we used CLIMEX to estimate the seasonal abundance of a species in relation to climate over time and species geographical distribution. Therefore, this research is designed to infer the mechanisms affecting population processes, rather than simply provide an empirical description of field observations based on matching patterns of meteorological data. In this research, we identified monthly suitability for Bemisia tabaci, with the climate models, for 12 commercial tomato crop locations through CLIMEX (version 4.0). We observed that B. tabaci displays seasonality with increased abundance in tomato crops during March, April, May, June, October and November (first year) and during March, April, May, September and October (second year) in all monitored areas. During this period, our model demonstrated a strong agreement between B. tabaci density and CLIMEX weekly growth index (GIw), which indicates significant reliability of our model results. Our results may be useful to design s ling and control strategies, in periods and locations when there is high suitability for B. tabaci.
Publisher: Elsevier BV
Date: 09-2020
Publisher: Elsevier BV
Date: 04-2019
Publisher: Wiley
Date: 24-04-2015
DOI: 10.1002/JSFA.7195
Abstract: Micronutrient deficiency develops when nutrient intake does not match nutritional requirements for maintaining healthy tissue and organ functions which may have long-ranging effects on health, learning ability and productivity. Inadequacy of iron, zinc and vitamin A are the most important micronutrient deficiencies. Consumption of a 100 g portion of date flesh from date palm (Phoenix dactylifera L.) has been reported to meet approximately half the daily dietary recommended intake of these micronutrients. This study investigated the potential distribution of P. dactylifera under future climates to address its potential long-term use as a food commodity to tackle micronutrient deficiencies in some developing countries. Modelling outputs indicated large shifts in areas conducive to date palm cultivation, based on global-scale alteration over the next 60 years. Most of the regions suffering from micronutrient deficiencies were projected to become highly conducive for date palm cultivation. These results could inform strategic planning by government and agricultural organizations by identifying areas to cultivate this nutritionally important crop in the future to support the alleviation of micronutrient deficiencies.
Publisher: MDPI AG
Date: 24-11-2017
Publisher: Informa UK Limited
Date: 18-04-2019
Publisher: MDPI AG
Date: 05-07-2021
DOI: 10.3390/RS13132638
Abstract: Large damages and losses resulting from floods are widely reported across the globe. Thus, the identification of the flood-prone zones on a flood susceptibility map is very essential. To do so, 13 conditioning factors influencing the flood occurrence in Brisbane river catchment in Australia (i.e., topographic, water-related, geological, and land use factors) were acquired for further processing and modeling. In this study, artificial neural networks (ANN), deep learning neural networks (DLNN), and optimized DLNN using particle swarm optimization (PSO) were exploited to predict and estimate the susceptible areas to the future floods. The significance of the conditioning factors analysis for the region highlighted that altitude, distance from river, sediment transport index (STI), and slope played the most important roles, whereas stream power index (SPI) did not contribute to the hazardous situation. The performance of the models was evaluated against the statistical tests such as sensitivity, specificity, the area under curve (AUC), and true skill statistic (TSS). DLNN and PSO-DLNN models obtained the highest values of sensitivity (0.99) for the training stage to compare with ANN. Moreover, the validations of specificity and TSS for PSO-DLNN recorded the highest values of 0.98 and 0.90, respectively, compared with those obtained by ANN and DLNN. The best accuracies by AUC were evaluated in PSO-DLNN (0.99 in training and 0.98 in testing datasets), followed by DLNN and ANN. Therefore, the optimized PSO-DLNN proved its robustness to compare with other methods.
Publisher: Wiley
Date: 27-06-2019
DOI: 10.1111/ECOG.04530
No related grants have been discovered for Farzin Shabani.