F.M. Torres-Bejarano; A.C. Torregroza-Espinosa; E. Martinez-Mera; D. Castañeda-Valbuena; M.P. Tejera-Gonzalez
Abstract
Ciénaga de Mallorquín is a coastal lagoon designated as a RAMSAR site due to its ecological regional and international importance. In this work, the environmental fluid dynamics code explorer modeling system was implemented to determine the spatio-temporal distribution of temperature, dissolved ...
Read More
Ciénaga de Mallorquín is a coastal lagoon designated as a RAMSAR site due to its ecological regional and international importance. In this work, the environmental fluid dynamics code explorer modeling system was implemented to determine the spatio-temporal distribution of temperature, dissolved oxygen, chemical oxygen demand and nutrient levels, and assess the trophic status of Ciénaga de Mallorquín. The model was set up with field measurement data taken during transition period and wet season, and secondary information obtained from local authorities and environmental agencies. The results of model simulations were calibrated and verified by the root mean square error method, achieving a consistent fit for all considered variables. Average velocities were between 0.006 m/s and 0.013 m/s during the analyzed periods. The temperature was higher in the wet season than in the transition period (29°C and 31.5°C, respectively). The dissolved oxygen was similar in both periods (6.6 and 6.7 mg/L). NO3 concentrations were higher during the transition period (3.28 mg/L), with a minimum of 1.76 mg/L and a maximum of 5.09 mg/L. The lowest NO3 concentrations were found in the area influenced by the connection with the Caribbean Sea. PO4 concentrations in the wet season were lower than in the transition period (0.20 mg/L). Finally, Ciénaga de Mallorquín exhibits high productivity levels with Trophic State Index > 50 and temporal variations of mesotrophic to eutrophic. The use of Trophic State Index is useful for the management of water body eutrophication and productivity, making it particularly important in aquatic ecosystems.
M. Camara; N.R.B. Jamil; A.F.B. Abdullah; R.B. Hashim
Abstract
Predicting land use change is an indispensable aspect in identifying the best development and management of land resources and their potential. This study used certified land-use maps of 1997, 2006, and 2015 combined with ancillary data such as road networks, water bodies and slopes, obtained from the ...
Read More
Predicting land use change is an indispensable aspect in identifying the best development and management of land resources and their potential. This study used certified land-use maps of 1997, 2006, and 2015 combined with ancillary data such as road networks, water bodies and slopes, obtained from the Department of Agriculture and the Department of Surveying and Mapping in Malaysia, respectively. The prediction of future land use changes in the Selangor River basin in Malaysia was performed using the Cellular Automata Markov model. The transition probability matrices were computed using the land use conditions of the periods 1997-2006, 2006-2015, 1997-2015. The performance of the model was very good in its overall ability to simulate the actual land use map of 2015, with the index values of 0.92% and 0.97%, respectively for Kappa for no information and Kappa for grid-cell level location which indicated the reliability of the model to successfully simulate land use changes in 2024 and 2033. Based on the expected results, the future urban area will grow faster (33%) over the next two decades, leading to a decline in forest area that is expected to lose 8% of its total space during these periods. Agricultural land will increase to 4%, while water bodies will change slightly increasing to 1%, and other areas of land use will likely become reservoirs of water, topsoil or new green spaces shrinking at 30%. Given the importance of knowledge of future land use in addressing the problems of uncontrolled development on environmental quality, this study could be valuable for land use planners of the river basin largely covered by natural forest. The study however, suggests future research to integrate geospatial techniques with biophysical and socio-economic factors in simulating land use trends.
A. Masih
Abstract
In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single ...
Read More
In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and regression tree using M5 algorithm. The prediction of Sulphur dioxide was based on atmospheric pollutants and meteorological parameters. While, the model performance was assessed by using four evaluation measures namely Correlation coefficient, mean absolute error, root mean squared error and relative absolute error. The results obtained suggest that 1) homogenous ensemble classifier random forest performs better than single base statistical and machine learning algorithms; 2) employing single base classifiers within bagging as base classifier improves their prediction accuracy; and 3) heterogeneous ensemble algorithm voting have the capability to match or perform better than homogenous classifiers (random forest and bagging). In general, it demonstrates that the performance of ensemble classifiers random forest, bagging and voting can outperform single base traditional statistical and machine learning algorithms such as linear regression, support vector machine for regression and multilayer perceptron to model the atmospheric concentration of sulphur dioxide.
G.C.B. Paclibar; E.R. Tadiosa
Abstract
Non-native plants that can cause adverse effects are otherwise known as invasive alien plant species which pose a major threat to plant biodiversity conservation and sustainability. This study is dedicated to determine the plant diversity and to assess the vulnerability of Quezon Protected Landscape, ...
Read More
Non-native plants that can cause adverse effects are otherwise known as invasive alien plant species which pose a major threat to plant biodiversity conservation and sustainability. This study is dedicated to determine the plant diversity and to assess the vulnerability of Quezon Protected Landscape, Southern Luzon, the Philippines to invasive alien plant species. Data from 90 10x10 m randomly established plots using the quadrat method showed that there are 318 plant species wherein 208 are native, 100 are non-native, and 10 are invasive. Results from the association of the physicochemical factors and the presence of invasive alien plant species through Spearman rho test revealed that most of the physicochemical factors have significant association except percent slope and hill shade. Soil pH, aspect and number of non-native plants show positive association while soil moisture, leaf litter thickness, elevation, species richness, species evenness, plot species diversity index, and the number of native plants signify negative association. Differences between the plots of with and without invasive alien plant species in physicochemical factors indicate that most of the physicochemical factors have a significant difference between plots of with and without invasive alien plant species except percent slope, hill shade, and aspect. Lastly, the MaxEnt model exemplifies that the most suitable predicted conditions for invasive alien plant species are at the edges of boundary and buffer zones. This study implies that most of the physicochemical factors are linked to the presence of invasive alien plant species and Quezon Protected Landscape has a low vulnerability to invasive alien plant species invasion.
G. Elkiran; V. Nourani; S.I. Abba; J. Abdullahi
Abstract
ABSTRACT: In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve oxygen concentration at the downstream of Mathura ...
Read More
ABSTRACT: In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve oxygen concentration at the downstream of Mathura City in India. The data used are dissolved oxygen, pH, biological oxygen demand and water temperature at upper, middle and downstream of the river. To predict outlet of dissolved oxygen of the river in each station, considering different input combinations as i) 11 inputs parameters for all three locations except, dissolved oxygen at the downstream ii) 7 inputs for middle and downstream except dissolved oxygen, at the target location and lastly iii) 3 inputs for downstream location. To determine the accuracy of the model, root mean square error and determination coefficient were employed. The simulated results of dissolved oxygen at three stations indicated that, multi-linear regression is found not to be efficient for predicting dissolved oxygen. In addition, both artificial intelligence models were found to be more capable and satisfactory for the prediction. Adaptive neuro fuzzy inference system model demonstrated high prediction ability as compared to feed forward neural network model. The results indicated that adaptive neuro fuzzy inference system model has a slight increment in performance than feed forward neural network model in validation step. Adaptive neuro fuzzy inference system proved high improvement in efficiency performance over multi-linear regression modeling up to 18% in calibration phase and 27% in validation phase for the best models.
S.M. Tajbakhsh; H. Memarian; K. Moradi; A.H. Aghakhani Afshar
Abstract
The change of land use/land cover has been known as an imperative force in environmental alteration, especially in arid and semi-arid areas. This research was mainly aimed to assess the validity of two major types of land change modeling techniques via a three dimensional approach in Birjand urban watershed ...
Read More
The change of land use/land cover has been known as an imperative force in environmental alteration, especially in arid and semi-arid areas. This research was mainly aimed to assess the validity of two major types of land change modeling techniques via a three dimensional approach in Birjand urban watershed located in an arid climatic region of Iran. Thus, a Markovian approach based on two suitability and transition potential mappers, i.e. fuzzy analytic hierarchy process and artificial neural network-multi layer perceptron was used to simulate land use map. Validation metrics, quantity disagreement, allocation disagreement and figure of merit in a three-dimensional space were used to perform model validation. Utilizing the fuzzy-analytic hierarchy processsimulation of total landscape in the target point 2015, quantity error, the figure of merit and allocation error were 2%, 18.5% and 8%, respectively. However, Artificial neural network-multi layer perceptron simulation led to a marginal improvement in figure of merit, i.e. 3.25%.
M. Rafiei; P.J. Sturm
Abstract
The aim of this study is to investigate the problems caused by discharge of polluted air from tunnels into the environment with a specific focus on residential areas. In city tunnels, portal or stacks, pollutant management is a big challenge. Nowadays, air quality management, particularly in urban tunnels, ...
Read More
The aim of this study is to investigate the problems caused by discharge of polluted air from tunnels into the environment with a specific focus on residential areas. In city tunnels, portal or stacks, pollutant management is a big challenge. Nowadays, air quality management, particularly in urban tunnels, is considered as a part of the ventilation system design. The goal is to see the environmental impacts beforehand. From environmental aspects, preventive measures are required either inside or outside the tunnel in some cases. Niayesh tunnel in Tehran is taken as a case for proving the objectives presented in this study. Concentration of carbon monoxide at the vicinity of the portals is calculated using the proper dispersion simulation. The results of dispersion modeling for the assumed worst case of ventilation can help to understand the environmental impact of ventilation. The worst traffic emissions for a congested traffic scenario,are selected as an emission source for dispersion modeling. According to the traffic condition and fleet composition, the crucial emission extracted from the tunnel is carbon monoxide. Therefore, the performed simulation only focuses on carbon monoxide dispersion modeling. From the other side, carbon monoxide is taken as a demonstration pollutant, because it is inert and chemical reactions can be neglected in short-term considerations. A lagrangian model composed of Graz Lagrangian Model and Graz Mesoscale Model is used for flow-field and dispersion calculations.
S.M. Tajbakhsh; H. Memarian; A. Kheyrkhah
Abstract
The proper use of natural resources can preserve these valuable assets. In line with the management of natural resources, land use optimization can be highly useful. The aim of the present study is to propose an appropriate integrative model for optimized allocation of lands for surface runoff and sediment ...
Read More
The proper use of natural resources can preserve these valuable assets. In line with the management of natural resources, land use optimization can be highly useful. The aim of the present study is to propose an appropriate integrative model for optimized allocation of lands for surface runoff and sediment load minimization and net income maximization in Bayg watershed, Iran. In this study, five categories of land uses, i.e. irrigated orchard, rangeland, irrigated farming, rainfed farming and almond orchard were spatially optimized to minimize surface runoff and sediment yield and to increase net income by integrating three approaches: weighted goal programming, analytic hierarchy process and multi-objective land allocation algorithm. To achieve the target levels in this work, the acreages of almond orchard and rainfed farming should be reduced by 100% and 37.32% respectively, and irrigated farming acreage should be increased by 138.53%. Through these alterations in the land use acreage, the sediment load will be reduced by 16.78% and net income will be improved by 72.52%. However, runoff volume will be increased by 0.22%. Results indicated that weighted goal programming satisfied 96% and 46% of the target levels of sediment load and net income respectively, but failed to reduce runoff volume. Therefore, it is necessary for managers to control runoff using the strategies related to runoff harvesting, especially on steep slopes. Generally, it can be concluded that a combination of the techniques weighted goal programming, analytic hierarchy process and multi-objective land allocation is highly capable to optimize land use and land covers based on the conflicting objectives.
J.C. Paquit; R.I.P. Rama
Abstract
The potential effect of invasive plant species on biodiversity is one of most important subject of inquiry at present. In many parts of the world, the alarming spread of these plants has been documented. Knowing that climate exerts a dominant control over the distribution of plant species, predictions ...
Read More
The potential effect of invasive plant species on biodiversity is one of most important subject of inquiry at present. In many parts of the world, the alarming spread of these plants has been documented. Knowing that climate exerts a dominant control over the distribution of plant species, predictions can therefore be made to determine which areas the species would likely spread under a climate change scenario and that is what this study aims to tackle. In the current study, a total of 211 species occurrence points were used to model the current and projected suitability of Piper aduncum in Bukidnon, Philippines using Maxent. Results revealed that the suitability of the species was determined primarily by climatic factors with Bio 18 (precipitation of the warmest quarter) as the strongest influencing variable with a mean percent contribution of 22.1%. The resulting model was highly accurate based on its mean test Area Under Curve that is equal to 0.917. Current prediction shows that suitable areas for Piper are concentrated along the southern portion of Bukidnon. Only 9% of the province is suitable for the species at present but is predicted to increase to 27% because of climate change. The central and southwestern parts of the province are the areas of high threat for invasion by Piper.
M. Tajbakhsh; H. Memarian; Y. Shahrokhi
Abstract
Mashhad City, according to the latest official statistics of the country is the second populated city after Tehran and is the biggest metropolis in the east of Iran. Considering the rapid growth of the population over the last three decades, the city’s development area has been extended, significantly. ...
Read More
Mashhad City, according to the latest official statistics of the country is the second populated city after Tehran and is the biggest metropolis in the east of Iran. Considering the rapid growth of the population over the last three decades, the city’s development area has been extended, significantly. This significant expansion has impacted natural lands on suburb and even some parts e.g. rangelands and agricultural area have been transited to urban land uses. The study was aimed at analyzing and simulating land use changes in Mashhad, Iran. The work needs a model to simulate land use changes among multiple categories and combine spatial and temporal changes during the projection period. Thus, Cellular Automata-Markov model was chosen to meet this target. In this work, the projected time period corresponded to the final 20-year vision period of all-round development of Iran for the target point of 2025 based on a long-term plan. Multi criteria evaluation approach integrated along with analytic hierarchy process were employed for preparing suitability maps for the five land uses, i.e. urban continuous patches, urban discontinuous patches, rural patches, agricultural lands, and range lands. Having applied the matrices utilized in model calibration, the best kappa coefficient proved to be associated with the land use maps dated 1996 and 2002. The Kappa index of quantity and allocation agreement was determined to be 0.9189 and 0.9529, respectively, which established an almost perfect agreement between simulated and observed land uses according to the year 2015. Change detection results showed that with the physical expansion of urban continuous patches, range lands and agricultural lands mostly transited to urban discontinuous patches and eventually were promoted to urban continuous texture. These developments or gains in urbanized patches will lead to some loses in agricultural lands and rangelands of the suburb in 2025. In addition, the analysis of projected land use map indicated that over the upcoming years, the development of the city in northern front, especially in northwestern region will be more intense with a higher speed in comparison with the other regions.
