The document compares two multicriteria decision methods - Brans' PROMETHEE and a modified version - for selecting erosion control alternatives in Argentina's Chaco area. It applies the methods to evaluate six alternatives across eight criteria in six subzones. For one subzone, the results under the original and modified methods are presented, with the modified approach incorporating criterion weights. Alternative B is the top-ranked option under the weighted method.
Methodologies used for identifying, assessing and mapping ecosystem services are diverse and frequently inconsistent and notwithstanding the examples from available literature, evident methodological gaps are still present. This paper presents an indicator based approach to assessing and mapping the multiplicity of ecosystem services provided by soils, based on available soil data for a reference depth of 100 cm. Of operational value is the fact that, within this framework, several services can be treated and mapped simultaneously, providing an efficient tool to model the heterogeneity of different soil functions, both at local and regional scale. The methodology consists of: (i) definition of soil based eco-system services, based on available soil data and on societal demands; (ii) definition of appropriate indicators and coding; and (iii) assessment and eventually mapping of soil based multiple ecosystem services. In this work we used spatial data to characterize and model the spatial heterogeneity of provisioning and regulating soil services in the case study area of alluvial plain of Emilia Romagna (Northern Italy). In order to explicitly take into account the spatial variability and the related uncertainty, and in order to exploit at best the available information, we: (i) realised a continuous coverage of basic soil properties via geostatistical simulation conditional on available 1:50,000 soil map and land use map, and (ii) derived the relevant soil properties via locally calibrated PTFs and using other available information, such as the land capability map. Results provide new insights about the composition and interrelation of multiple soil functions and services in the region and highlight the difference between soils in term of joint services provision.
Diffuse reflectance spectroscopy to estimate soil attributes of Brazilian wet...Agriculture Journal IJOEAR
Abstract— The study of soils and characterization of its attributes are continually evolving, however, for the condition of wetlands, such information is still scarce and poorly distributed. Thus, the objective of this work was to characterize spectrally the soils of a wetland area. On the study area were collected georeferenced soil samples and sent for chemical and physical analysis routine and then subjected to spectral evaluation. Were identified seven soil classes with hydromorphic characteristics in their spectral curves? The information contained in these curves then led the development of equations for soil attributes. Sand was the physical attribute of a better correlation with laboratory data and Cationic Exchange Capacity (CEC), the chemical attributes that showed better results.
Accurate estimation of runoff and sediment yield amount is not only an important task in physiographic but also important for proper watershed management. Watershed is an ideal unit for planning and management of land and water resources. Direct runoff in a catchment depends on soil type, land cover and rainfall. Of the many methods available for estimating runoff from rainfall, the curve number method (SCS-CN) is the most popular. The curve number depends upon soil and land use characteristics. This study was conducted in the Upper Cauvery Karnataka using remote sensing and GIS. SCSCN method has been used for surface runoff estimation for Eight watersheds of Upper Cauvery. The soil map and land use were created in the GIS environment, because the curve number method is used here as a distributed model. The major advantage of employing GIS in rainfall -runoff modelling is that more accurate sizing and catchment characterization can be achieved. Furthermore, the analysis can be performed much faster, especially when there is a complex mix of land use classes and different soil types. The results showed that the surface runoff ranged from 170.12-599.84 mm in the study area, when rainfall rates were received from 1042.65-1912 mm. To find the relationship between rainfall and runoff rates, The straight line equation was used, That was found there a strong correlation between Runoff and precipitation rates, The value correlation coefficient between them was 86%. The Average depth of runoff is more in watershed A4, Average runoff coefficient is less in Watershed B2, the correlation coefficient is high in A4 to a value of almost 95%. Through of these results, the study recommends take advantage of runoff rates by reserving them at collection of Watershed and then using them for agricultural purposes in the vicinity. This would be better than reserving water from the total area which is 10874.65 square kilometers, and then will evaporate or infiltrate before reaching the dam lake
Methodologies used for identifying, assessing and mapping ecosystem services are diverse and frequently inconsistent and notwithstanding the examples from available literature, evident methodological gaps are still present. This paper presents an indicator based approach to assessing and mapping the multiplicity of ecosystem services provided by soils, based on available soil data for a reference depth of 100 cm. Of operational value is the fact that, within this framework, several services can be treated and mapped simultaneously, providing an efficient tool to model the heterogeneity of different soil functions, both at local and regional scale. The methodology consists of: (i) definition of soil based eco-system services, based on available soil data and on societal demands; (ii) definition of appropriate indicators and coding; and (iii) assessment and eventually mapping of soil based multiple ecosystem services. In this work we used spatial data to characterize and model the spatial heterogeneity of provisioning and regulating soil services in the case study area of alluvial plain of Emilia Romagna (Northern Italy). In order to explicitly take into account the spatial variability and the related uncertainty, and in order to exploit at best the available information, we: (i) realised a continuous coverage of basic soil properties via geostatistical simulation conditional on available 1:50,000 soil map and land use map, and (ii) derived the relevant soil properties via locally calibrated PTFs and using other available information, such as the land capability map. Results provide new insights about the composition and interrelation of multiple soil functions and services in the region and highlight the difference between soils in term of joint services provision.
Diffuse reflectance spectroscopy to estimate soil attributes of Brazilian wet...Agriculture Journal IJOEAR
Abstract— The study of soils and characterization of its attributes are continually evolving, however, for the condition of wetlands, such information is still scarce and poorly distributed. Thus, the objective of this work was to characterize spectrally the soils of a wetland area. On the study area were collected georeferenced soil samples and sent for chemical and physical analysis routine and then subjected to spectral evaluation. Were identified seven soil classes with hydromorphic characteristics in their spectral curves? The information contained in these curves then led the development of equations for soil attributes. Sand was the physical attribute of a better correlation with laboratory data and Cationic Exchange Capacity (CEC), the chemical attributes that showed better results.
Accurate estimation of runoff and sediment yield amount is not only an important task in physiographic but also important for proper watershed management. Watershed is an ideal unit for planning and management of land and water resources. Direct runoff in a catchment depends on soil type, land cover and rainfall. Of the many methods available for estimating runoff from rainfall, the curve number method (SCS-CN) is the most popular. The curve number depends upon soil and land use characteristics. This study was conducted in the Upper Cauvery Karnataka using remote sensing and GIS. SCSCN method has been used for surface runoff estimation for Eight watersheds of Upper Cauvery. The soil map and land use were created in the GIS environment, because the curve number method is used here as a distributed model. The major advantage of employing GIS in rainfall -runoff modelling is that more accurate sizing and catchment characterization can be achieved. Furthermore, the analysis can be performed much faster, especially when there is a complex mix of land use classes and different soil types. The results showed that the surface runoff ranged from 170.12-599.84 mm in the study area, when rainfall rates were received from 1042.65-1912 mm. To find the relationship between rainfall and runoff rates, The straight line equation was used, That was found there a strong correlation between Runoff and precipitation rates, The value correlation coefficient between them was 86%. The Average depth of runoff is more in watershed A4, Average runoff coefficient is less in Watershed B2, the correlation coefficient is high in A4 to a value of almost 95%. Through of these results, the study recommends take advantage of runoff rates by reserving them at collection of Watershed and then using them for agricultural purposes in the vicinity. This would be better than reserving water from the total area which is 10874.65 square kilometers, and then will evaporate or infiltrate before reaching the dam lake
For Domestic Wastewater Treatment, Finding Optimum Conditions by Particle Swa...Agriculture Journal IJOEAR
Abstract— Performing jar test method is used for finding out optimum conditions (coagulant type, coagulant dose, pH etc.)for treatment of domestic wastewater before physicochemical process, or coagulation process. In this study, Response Surface Method (RSM) is applied to determine optimum combinations of coagulant dose and pH value in jar test. Alum, FeCl3 and FeSO4 are used as coagulant and compared with highest removal efficiency of their two responses which turbidity and chemical oxygen demand (COD).Finding equations from RSM are also evaluated with Particle Swarm Optimization (PSO) method by using Matlab Program. Alum and Ferric Chloridedose500 mg/lat pH7 found as optimum conditions for domestic wastewater treatment. COD removal for Alum and Ferric Chloride are 90% and 70%,respectively.In addition, Because of becoming low COD removal (maximum 50%) and ineffectively color removal, Ferric Sulfate coagulant found as inconvenient for treating domestic wastewater.
4 Review on Different Evapotranspiration Empirical EquationsINFOGAIN PUBLICATION
For optimal design and management of hydrologic balance and scheduling irrigation models, the need to measure Evapotranspiration is of great importance. It helps in predicting when and how much water is required for any particular irrigation scheme. Reference Evapotranspiration is a standard nomenclature defined by FAO to provide a reference frame although it is not a full proof equation. Several scientists have developed multiple equations based of three primary directions viz. temperature based methods, radiation based methods and mass – transfer methods. Here in this paper, we have carried out a review on most of the popular equations and the objective is to elucidate the advantages and drawbacks each one of them register when put into use. The reference equation for standardization considered here is FAO 56 Penman Montheith equation. Thirty other equations from the three schools have been analysed here. Statistical Regression Analysis methods and coefficient of determination (R2), Root Mean Square Error (RMSE) and index of agreement (d) are the analytical parameters those are to be used while estimating their acceptance in evaluating the throughputs
An optimal design of current conveyors using a hybrid-based metaheuristic alg...IJECEIAES
This paper focuses on the optimal sizing of a positive second-generation current conveyor (CCII+), employing a hybrid algorithm named DE-ACO, which is derived from the combination of differential evolution (DE) and ant colony optimization (ACO) algorithms. The basic idea of this hybridization is to apply the DE algorithm for the ACO algorithm’s initialization stage. Benchmark test functions were used to evaluate the proposed algorithm’s performance regarding the quality of the optimal solution, robustness, and computation time. Furthermore, the DE-ACO has been applied to optimize the CCII+ performances. SPICE simulation is utilized to validate the achieved results, and a comparison with the standard DE and ACO algorithms is reported. The results highlight that DE-ACO outperforms both ACO and DE.
2018 National Tanks Conference & Exposition: HRSC Data VisualizationAntea Group
Two of our High-Resolution Site Characterization (HRSC) Data Visualization posters featured at the 2018 NTC Conference in Louisville, KY.
1. Using Data Management and 3-Dimensional Data Visualization to Generate More Complete Conceptual Site Models and Streamline Site Closure
2. High-Resolution Site Characterization (HRSC) and 3-Dimensional Data Visualization for a Fractured Rock Site: A Path to Streamlined Closure
State of the art on Life Cycle Assessment for Solid Waste ManagementYashpujara00955
Life Cycle Assessment for Solid Waste Management- A Peer Review. LCA tool can be used as a decision-making approach for the many companies and especially LCA tool can be employed for finding the Impact assessment on Environment, Human health and vegetations.
Presentation about the use of Social Metabolism and Complex Systems Theory to analysethe water-Food-Energy Nexus with an application of MuSIASEM to the Indian Punjab.
Oral Presentation ot the Biannual COnference of the International Society for Industrial Ecology, 10 July 2015, University of Surrey, UK.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Computer model simulations are widely used in the investigation of complex hydrological systems. In particular, hydrological models are tools that help both to better understand hydrological processes and to predict extreme events such as floods and droughts. Usually, model parameters need to be estimated through calibration, in order to constrain model outputs to observed variables.
Relevant model parameters used for calibration are usually selected based on expert knowledge of the modeller or by using a local one-at-a-time (OAT) sensitivity analysis (SA). However, in case of complex models those approaches may not result in proper identification of the most sensitive parameters for model calibration. In particular local OAT SA methods are only effective for assessing the relative importance of input factors when the model is linear, monotonic, and additive, which is rarely the case for complex environmental models. In contrast Global Sensitivity Analysis (GSA)
is a formal method for statistical evaluation of relevant parameters that contribute significantly to model performance. GSA techniques explore the entire feasible space of each model parameter, and they do not require any assumptions on the model nature (such as linearity or additivity).
In this work we apply the GSA to LISFLOOD, a fully-distributed hydrological model used for flood forecasting at Pan-European scale within the European Flood Awareness System (EFAS). Two case studies are considered, snowmelt- and evapotranspiration-driven catchments, to identify sensitive parameters for both types of hydrological regimes. Results of the GSA will then be used for selecting parameters that need to be estimated during model calibration. Considering the large
number of parameters of a fully-distributed model, a two-step GSA framework is applied. First, we implement the computationally efficient screening method of Morris. This method requires a limited number of simulations and produces a qualitative ranking and selection of important factors. As a second step, we apply the variance-based method of Sobol, only to the subset of factors determined as important during the previous screening. The method of Sobol provides quantitative estimates for first order and total order sensitivity indexes of input factors.
The calibration results after the GSA will be described for both case studies and compared against those obtained by using only prior expert knowledge
An ecological assessment of food waste composting using a hybrid life cycle a...Ramy Salemdeeb
A conference paper published at the 8th Conference of the International Society for Industrial Ecology, At University of Surrey, Guildford, UK, At Surrey
CAN CROP MANAGEMENT IMPROVE EMISSIONS SAVINGS?: PRELIMINARY RESULTS OF THE OP...Bioenergy Crops
Several studies suggest that lignocellulosic energy crops for electricity production may have a better performance compared to those crops for liquid biofuels production, when assessing GHG savings with respect to fossil references. Winter cereal residues and some annual winter grasses, as dedicated energy crops, are currently being grown in Spain and harvested as bales to be burned for electricity production in biomass power plants. Previous studies of our group analyzed GHG emissions and energy balances of winter cereals for electricity production by means of Life Cycle Assessment. We selected highly productive genotypes of three annual winter cereals (rye, triticale and oat) and compared them with Spanish electricity produced using natural gas. This paper compares effects of the use of different crop management practices for rye growing in the assessment of energy balances and GHG emissions. We analyzed the effects of six different management practices consisting of two different sowing doses (suboptimal and normal) combined with three top fertilization doses (zero, 30 and 80kgN ha-1). We made a characterization analysis of biomasses to estimate the nitrogen uptake of the crops in order to compare it with the nitrogen provided by the fertilizers. This comparison evaluates if lower fertilization doses are sustainable for the soil nitrogen stocks. Our results suggest that there is trade-off between soil nitrogen and emission savings. The use of zero or low top fertilization doses (30 kg N ha-1) improves GHG emissions and energy balances even with a yield reduction. Nevertheless the use of these doses imply an annual lose in soil nitrogen stocks for the majority all of our trials. Using suboptimal sowing doses resulted in yield decreases that did not compensate the lower input consumed.
Keywords: electricity, energy balance, energy crops, greenhouse gases (GHG), life cycle assessment (LCA), sustainability criteria
For Domestic Wastewater Treatment, Finding Optimum Conditions by Particle Swa...Agriculture Journal IJOEAR
Abstract— Performing jar test method is used for finding out optimum conditions (coagulant type, coagulant dose, pH etc.)for treatment of domestic wastewater before physicochemical process, or coagulation process. In this study, Response Surface Method (RSM) is applied to determine optimum combinations of coagulant dose and pH value in jar test. Alum, FeCl3 and FeSO4 are used as coagulant and compared with highest removal efficiency of their two responses which turbidity and chemical oxygen demand (COD).Finding equations from RSM are also evaluated with Particle Swarm Optimization (PSO) method by using Matlab Program. Alum and Ferric Chloridedose500 mg/lat pH7 found as optimum conditions for domestic wastewater treatment. COD removal for Alum and Ferric Chloride are 90% and 70%,respectively.In addition, Because of becoming low COD removal (maximum 50%) and ineffectively color removal, Ferric Sulfate coagulant found as inconvenient for treating domestic wastewater.
4 Review on Different Evapotranspiration Empirical EquationsINFOGAIN PUBLICATION
For optimal design and management of hydrologic balance and scheduling irrigation models, the need to measure Evapotranspiration is of great importance. It helps in predicting when and how much water is required for any particular irrigation scheme. Reference Evapotranspiration is a standard nomenclature defined by FAO to provide a reference frame although it is not a full proof equation. Several scientists have developed multiple equations based of three primary directions viz. temperature based methods, radiation based methods and mass – transfer methods. Here in this paper, we have carried out a review on most of the popular equations and the objective is to elucidate the advantages and drawbacks each one of them register when put into use. The reference equation for standardization considered here is FAO 56 Penman Montheith equation. Thirty other equations from the three schools have been analysed here. Statistical Regression Analysis methods and coefficient of determination (R2), Root Mean Square Error (RMSE) and index of agreement (d) are the analytical parameters those are to be used while estimating their acceptance in evaluating the throughputs
An optimal design of current conveyors using a hybrid-based metaheuristic alg...IJECEIAES
This paper focuses on the optimal sizing of a positive second-generation current conveyor (CCII+), employing a hybrid algorithm named DE-ACO, which is derived from the combination of differential evolution (DE) and ant colony optimization (ACO) algorithms. The basic idea of this hybridization is to apply the DE algorithm for the ACO algorithm’s initialization stage. Benchmark test functions were used to evaluate the proposed algorithm’s performance regarding the quality of the optimal solution, robustness, and computation time. Furthermore, the DE-ACO has been applied to optimize the CCII+ performances. SPICE simulation is utilized to validate the achieved results, and a comparison with the standard DE and ACO algorithms is reported. The results highlight that DE-ACO outperforms both ACO and DE.
2018 National Tanks Conference & Exposition: HRSC Data VisualizationAntea Group
Two of our High-Resolution Site Characterization (HRSC) Data Visualization posters featured at the 2018 NTC Conference in Louisville, KY.
1. Using Data Management and 3-Dimensional Data Visualization to Generate More Complete Conceptual Site Models and Streamline Site Closure
2. High-Resolution Site Characterization (HRSC) and 3-Dimensional Data Visualization for a Fractured Rock Site: A Path to Streamlined Closure
State of the art on Life Cycle Assessment for Solid Waste ManagementYashpujara00955
Life Cycle Assessment for Solid Waste Management- A Peer Review. LCA tool can be used as a decision-making approach for the many companies and especially LCA tool can be employed for finding the Impact assessment on Environment, Human health and vegetations.
Presentation about the use of Social Metabolism and Complex Systems Theory to analysethe water-Food-Energy Nexus with an application of MuSIASEM to the Indian Punjab.
Oral Presentation ot the Biannual COnference of the International Society for Industrial Ecology, 10 July 2015, University of Surrey, UK.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Computer model simulations are widely used in the investigation of complex hydrological systems. In particular, hydrological models are tools that help both to better understand hydrological processes and to predict extreme events such as floods and droughts. Usually, model parameters need to be estimated through calibration, in order to constrain model outputs to observed variables.
Relevant model parameters used for calibration are usually selected based on expert knowledge of the modeller or by using a local one-at-a-time (OAT) sensitivity analysis (SA). However, in case of complex models those approaches may not result in proper identification of the most sensitive parameters for model calibration. In particular local OAT SA methods are only effective for assessing the relative importance of input factors when the model is linear, monotonic, and additive, which is rarely the case for complex environmental models. In contrast Global Sensitivity Analysis (GSA)
is a formal method for statistical evaluation of relevant parameters that contribute significantly to model performance. GSA techniques explore the entire feasible space of each model parameter, and they do not require any assumptions on the model nature (such as linearity or additivity).
In this work we apply the GSA to LISFLOOD, a fully-distributed hydrological model used for flood forecasting at Pan-European scale within the European Flood Awareness System (EFAS). Two case studies are considered, snowmelt- and evapotranspiration-driven catchments, to identify sensitive parameters for both types of hydrological regimes. Results of the GSA will then be used for selecting parameters that need to be estimated during model calibration. Considering the large
number of parameters of a fully-distributed model, a two-step GSA framework is applied. First, we implement the computationally efficient screening method of Morris. This method requires a limited number of simulations and produces a qualitative ranking and selection of important factors. As a second step, we apply the variance-based method of Sobol, only to the subset of factors determined as important during the previous screening. The method of Sobol provides quantitative estimates for first order and total order sensitivity indexes of input factors.
The calibration results after the GSA will be described for both case studies and compared against those obtained by using only prior expert knowledge
An ecological assessment of food waste composting using a hybrid life cycle a...Ramy Salemdeeb
A conference paper published at the 8th Conference of the International Society for Industrial Ecology, At University of Surrey, Guildford, UK, At Surrey
CAN CROP MANAGEMENT IMPROVE EMISSIONS SAVINGS?: PRELIMINARY RESULTS OF THE OP...Bioenergy Crops
Several studies suggest that lignocellulosic energy crops for electricity production may have a better performance compared to those crops for liquid biofuels production, when assessing GHG savings with respect to fossil references. Winter cereal residues and some annual winter grasses, as dedicated energy crops, are currently being grown in Spain and harvested as bales to be burned for electricity production in biomass power plants. Previous studies of our group analyzed GHG emissions and energy balances of winter cereals for electricity production by means of Life Cycle Assessment. We selected highly productive genotypes of three annual winter cereals (rye, triticale and oat) and compared them with Spanish electricity produced using natural gas. This paper compares effects of the use of different crop management practices for rye growing in the assessment of energy balances and GHG emissions. We analyzed the effects of six different management practices consisting of two different sowing doses (suboptimal and normal) combined with three top fertilization doses (zero, 30 and 80kgN ha-1). We made a characterization analysis of biomasses to estimate the nitrogen uptake of the crops in order to compare it with the nitrogen provided by the fertilizers. This comparison evaluates if lower fertilization doses are sustainable for the soil nitrogen stocks. Our results suggest that there is trade-off between soil nitrogen and emission savings. The use of zero or low top fertilization doses (30 kg N ha-1) improves GHG emissions and energy balances even with a yield reduction. Nevertheless the use of these doses imply an annual lose in soil nitrogen stocks for the majority all of our trials. Using suboptimal sowing doses resulted in yield decreases that did not compensate the lower input consumed.
Keywords: electricity, energy balance, energy crops, greenhouse gases (GHG), life cycle assessment (LCA), sustainability criteria
when will pi network coin be available on crypto exchange.DOT TECH
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Turin Startup Ecosystem 2024 - Ricerca sulle Startup e il Sistema dell'Innov...Quotidiano Piemontese
Turin Startup Ecosystem 2024
Una ricerca de il Club degli Investitori, in collaborazione con ToTeM Torino Tech Map e con il supporto della ESCP Business School e di Growth Capital
Seminar: Gender Board Diversity through Ownership NetworksGRAPE
Seminar on gender diversity spillovers through ownership networks at FAME|GRAPE. Presenting novel research. Studies in economics and management using econometrics methods.
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USDA Loans in California: A Comprehensive Overview.pptxmarketing367770
USDA Loans in California: A Comprehensive Overview
If you're dreaming of owning a home in California's rural or suburban areas, a USDA loan might be the perfect solution. The U.S. Department of Agriculture (USDA) offers these loans to help low-to-moderate-income individuals and families achieve homeownership.
Key Features of USDA Loans:
Zero Down Payment: USDA loans require no down payment, making homeownership more accessible.
Competitive Interest Rates: These loans often come with lower interest rates compared to conventional loans.
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Guaranteed Loan Program: The USDA guarantees a portion of the loan, reducing risk for lenders and expanding borrowing options.
Eligibility Criteria:
Location: The property must be located in a USDA-designated rural or suburban area. Many areas in California qualify.
Income Limits: Applicants must meet income guidelines, which vary by region and household size.
Primary Residence: The home must be used as the borrower's primary residence.
Application Process:
Find a USDA-Approved Lender: Not all lenders offer USDA loans, so it's essential to choose one approved by the USDA.
Pre-Qualification: Determine your eligibility and the amount you can borrow.
Property Search: Look for properties in eligible rural or suburban areas.
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USDA loans are an excellent option for those looking to buy a home in California's rural and suburban areas. With no down payment and flexible requirements, these loans make homeownership more attainable for many families. Explore your eligibility today and take the first step toward owning your dream home.
Abhay Bhutada Leads Poonawalla Fincorp To Record Low NPA And Unprecedented Gr...Vighnesh Shashtri
Under the leadership of Abhay Bhutada, Poonawalla Fincorp has achieved record-low Non-Performing Assets (NPA) and witnessed unprecedented growth. Bhutada's strategic vision and effective management have significantly enhanced the company's financial health, showcasing a robust performance in the financial sector. This achievement underscores the company's resilience and ability to thrive in a competitive market, setting a new benchmark for operational excellence in the industry.
1. Comparison of Brans Promethee multicriteria
decision method and Promethee modified by authors
for the optimization of an erosion control integral
plan in Chaco area in Salta province (Argentine)
J. B. Grau (1), J. M. Antón (1), F. Colombo (2), L. de los Ríos (2), J. M. Cisneros (3), A. Tarquis (1)
(1) E. T. S. I. Agrónomos- Universidad Politécnica de Madrid (Spain) (2) Escuela de Negocios- Universidad Católica de
Salta (Argentina) (3) Facultad de Agronomía y Veterinaria-Universidad Nacional de Río Cuarto (Argentina)
E-mail: j.grau@upm.es
Abstract- Chaco area is situated in the Province of Salta at
North West of Argentine. The desertification is a big problem.
In order to mitigate the problem it is necessary to take into
account not only pedologic criteria but the economical,
environmental, cultural and sociological criteria. Six sub zones
have been established following previous studies. Eight criteria
and six alternatives have been introduced in the model.
Following the results of the study carried out by a collaborative
project between UPM and UCS financed by AECID (1) were
established several initial matrix. Brans Promethee Multicriteria
Decision Method (MCDM) was applied and the authors
modified that method introducing weights like in Electre
Method.
I. INTRODUCTION
The Salta Province has 155.000 km2
and 1 million
population, it is at NW of Argentine (NOA) having latitudes
around 25ºS, it has rain from 400 to 800 mm/year. It has a
low density of population in small cities and Indian places
“puestos” or “colonias”. It has low standards for roads and it
has an environment that is “deteriorating progressively”.
Water is the most critical factor, as much for human and
animal consumption, as for the production system in general
and for the flooding and lack of appropriated infrastructures.
Besides the water, other factors have an important influence
in the erosion and progressive desertification of this region
and environment degradation. Historically the human
exploitation of natural forest to use in the railway and other
activities produced an environment degradation process.
Later on the autochthonous population followed the irrational
wood extraction an over pasture as "modus vivendi"
contributing to make the situation worse. Actually the farms
and big single-crop exploitations in some locations do not
give solution to the desertification problem. Only one integral
plan considering all factors involved and the differences
among sub zones will be an initial point to change the
direction of the desertification process.
A. Criteria, alternatives and Sub zones
The following eight criteria were defined:
Water erosion (WE): The water erosion is important. The
relative water erosion indexes figures in the decisional
matrix.
Eolian erosion (EE): Winds erode, transport and deposit
materials and are effective agents in several areas of this
region.
Implementation Facility (IF): They have been established
taking into account actors’ opinions.
Water Resources (WR): By each alternative have been
considered and the relative results have been taken into this
criterion.
Economical benefits EB): The relative economical benefits
using each alternative in a period of 25 years have been
obtained as shown in the matrix with figures from 1 to 10.
Hand power (HP): We have considered that would be
satisfactory to give employment to the majority of it
population. For that, we have considered this criterion as of
“more is better” kind.
Environmental Impacts (EI): They have been considered in
each sub zone the environmental impacts according with the
alternative adopted.
Social Acceptance (SA): The figures included in this
criterion have been obtained from the results of different
forums and meeting with institutions, organizations and
native people.
Five alternatives have been retained:
A) Autochthonous forest: mainly “Quebracho Blanco” and
“Quebracho Colorado” forest species.
B) High value forest: mainly teak, ebony, walnut tree, cherry
tree, lignum vitae, eucalyptus, etc
C) Traditional farms with extensive agriculture and
livestock mixed with autochthonous forest modified and
several foraging plants.
D) Erosion control Crop with agriculture use.
E) Erosion control crop with industrial use (biomass).
Following the experience and the local acknowledge, the area
has been divided in 6 sub zones: Las Lajitas, La Estrella,
Pichanal, Martin Hickmann, Rivadavia banda sur and Joaquín
V. Gonzalez.
II. METHODOLOGY
We have used the Preference Ranking Organization Method
(The PROMETHEE Method for Multiple Criteria Decision-
Making) by Ref. [4, 13, 14]. This is an outranking method, as
516978-1-4244-3760-3/09/$25.00 c 2009 IEEE
2. ELECTRE due to Roy [11, 12] or A.H.P. due to Saaty [15,
16, 17]. Following Ref. [4, 13, 14] two possibilities are
offered, PROMETHEE I provides a partial preorder and
PROMETHEE II a total preorder on the set of possible
alternatives. Different types of criteria have been adopted.
Type I and Type III with different threshold (m). Type I is the
usual Criterion. With this criterion if f(a) = f(b) this is
indifference between a and b. If this is not the case the
decision-maker has a strict preference for the action having
greatest value. Type III is the Criterion with Linear
Preference. Such an extension of the notion of criterion
allows the decision-maker to prefer progressively a to b for
progressively larger deviations between f(a) and f(b). The
preference increases linearly until deviation equals m, after
this value the preference is strict. For m the values 2, 4 and 6
have been taken.
The authors have modified the PROMETHEE method using
the weights of the criteria following the ELECTRE I Method
[6, 1, 2, 3, 7, 8, 9, 10]. In the case I have been adopted the
same weights for all sub zones and in the case II different
weights
Besides, some modifications have been considered in the data
of the initial matrixes.
Finally, MathCad has been used to program the calculus.
We show below, like example, the application to sub zone
"La Estrella".
EROSION AND DESERTIFICATION INTEGRAL CONTROL PLAN USING PROMETHEE
1( )SUB ZONE LA ESTRELLA ORIGIN 1
CRITERION: 1.-water erosion index 2.- eolian erosion index, 3.- Implementation
facility 4.-Water Resources, 5.- Economical Benefits, 6.- Hand power,
7.-Environmental Impacts, 8.- Social Acceptance
Indice Isubj:
más es mejor Isubj = 1
más es peor Isubj = -1
alternatives i
1 2 3 4 5
t
7
6
1
8
5
2
8
6
7
6
5
4
5
9
6
5
3
3
6
4
8
9
3
9
2
2
6
4
5
6
5
6
3
2
8
5
8
6
4
8
W
0.20
0.15
0.15
0.10
0.10
0.10
0.10
0.10
I
1
1
1
1
1
1
1
1
Alternatives:
i = 1 ....5 . with x >= 0, if not with |x|
A- functions of criterion-parameter and type elected for each criterion j:
following Ref. [4]
j = 1 type III, m=2, j = 2 type III, m=4, j=3 type III, m=4, j=4 type I j = 5 type I ,
j = 6 type III, m=6 , j=7 type III,m=6 j=8 type III, m=2
p1 x( ) if x 2
x
2
, 1,
p2 x( ) if x 4
x
4
, 1,
p4 x( ) if x 0 0, 1,( )
p3 x( ) if x 6
x
6
, 1,
p j x,( ) y x
z p1 y( ) j 1if
z p2 y( ) j 2if
z p2 y( ) j 3if
z p4 y( ) j 4if
z p4 y( ) j 5if
z p3 y( ) j 6if
z p3 y( ) j 7if
z p1 y( ) j 8if
z
x 8 7.9, 8..
10 5 0 5 10
0
0.5
1
p 2 x,( )
x
With this outranking graph,
functión of preference adopted:
P i ii, j,( ) if Ij
tj i,
tj ii,
. 0 0, p j tj i,
tj ii,
,,
A.- Results following initial methods of Ref [4]:
Índixes q(i,ii) of preferences (π(i,ii) Brans&Vincke),
giving outranking graphs according with values:
q i ii,( )
1
8
j
P i ii, j,( )
=
8
i 1 5.. ii 1 5.. qqi ii,
q i ii,( )
qq
0
0.25
0.5
0.208
0.458
0.229
0
0.281
0.094
0.469
0.448
0.281
0
0.042
0.208
0.438
0.333
0.406
0
0.5
0.458
0.354
0.156
0.021
0
=
Outgoing flow: fp i( )
1
5
ii
q i ii,( )
=
fppi
fp i( )
fpp
1.573
1.219
1.344
0.365
1.635
=
fm i( )
1
5
ii
q ii i,( )
=
fmmi
fm i( ) fmm
1.417
1.073
0.979
1.677
0.99
=
Incoming flow:
PROMETHEE II (clasification of alternatives by Total Preorder,
Each alternative obtain one value(more is better):
fd i( ) fp i( ) fm i( ) fddi
fd i( ) fdd
0.156
0.146
0.365
1.313
0.646
=
PROMETHEE I (clasification of alternatives by Partial Preorden):
pr i ii,( ) z 1
z 0 fp i( ) fp ii( )( ) fm i( ) fm ii( )( ).if
z 1 fp i( ) fp ii( )>( ) fm i( ) fm ii( )<( ).( ) fp i( ) fp ii( )>( ) fm i( ) fm ii( )( ).( ) fp i( ) fp ii( )( ) fm i( ) fm ii( )<( ).( )if
prri ii,
pr i ii,( )
Alternative E is preferred (E C A B D).
prr
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
=
B Results following the method modified by Ref [6] in order to weigh comparativment the criteria
with similar weights to ELECTRE-I:
q Preference Index (π Ref. [4]), gives outranking graph by values:
q i ii,( )
1
8
j
P i ii, j,( ) Wj
.
=
qqi ii,
q i ii,( ) 5.
i 1 5.. ii 1 5..
qq
0
1.25
2.25
1.083
2.083
0.917
0
1.188
0.438
2.063
2.479
1.813
0
0.167
0.958
2.5
2.083
1.938
0
2.375
2.583
2.167
0.688
0.083
0
=
:Outgoing flow: fp i( )
1
5
ii
q i ii,( )
=
fppi
fp i( )
fpp
1.696
1.463
1.213
0.354
1.496
=
Incoming flow: fm i( )
1
5
ii
q ii i,( )
=
fmmi
fm i( ) fmm
1.333
0.921
1.083
1.779
1.104
=
PROMETHEE II (clasification of alternatives by Total Preorder)
fd i( ) fp i( ) fm i( ) fddi
fd i( ) fdd
0.363
0.542
0.129
1.425
0.392
=
PROMETHEE I (clasification of alternatives by Partial Preorden):
pr i ii,( ) z 1
z 0 fp i( ) fp ii( )( ) fm i( ) fm ii( )( ).if
z 1 fp i( ) fp ii( )>( ) fm i( ) fm ii( )<( ).( ) fp i( ) fp ii( )>( ) fm i( ) fm ii( )( ).( ) fp i( ) fp ii( )( ) fm i( ) fm ii( )<( ).( )if
where, pr(i,ii) = 1 tell us that alternative i is preference (outranks) to alternative j, pr(i,ii) = 0 is
indifference, y pr(i,ii) = -1 are incomparable, that may be obtained by pr(ii,i) .
prri ii,
pr i ii,( )
prr
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
= Alternative B is preferred (B E A C D).
2009 7th IEEE International Conference on Industrial Informatics (INDIN 2009) 517
3. III. RESULTS
1. Sub zone Martin Hickman
1.1. ALTERNATIVE1:
Table I shows the values to Martin Hickman sub-zone. It has
been included the type of pseudo-criteria used and the
threshold (m) for the type III [4].
TABLE I
DECISIONAL MATRIX FOR MARTIN HICKMAN, ALTERNATIVE 1
Alternative WE EE IF WR EB HP EI SA
A 9 8 5 8 7 2 8 2
B 7 5 7 5 6 8 6 5
C 4 2 8 4 8 8 1 9
D 3 3 6 4 6 7 5 6
E 3 3 2 6 8 6 5 8
Weight 0,2 0,2 0,05 0,1 0,1 0,1 0,15 0,1
Type of criterion III III III I I III III III
Thresholds 2 4 4 2 2 4
Two procedures have been applied in order to obtain
alternative preorder:
A: Initial method of Ref. [4].
B: Method modified by Ref. [6].
1.1.A. Results by Ref. [4] method. The preorder is shown in
Fig. 1.
A B C E D
Fig. 1 Graph sub zone Martin Hickman, alternative 1.1A, Promethee II.
1.1. B Results following Ref. [9] method are shown in Fig. 2.
A B E C D
Fig. 2: Graphs sub zone Martin Hickman 1.1B Promethee II, modified.
1.2 ALTERNATIVE 2: Other value of criteria, same weight,
pseudocriteria and thresholds (Table II andy Fig. 3 and 4).
TABLE II
DECISIONAL MATRIX FOR MARTIN HICKMAN, ALTERNATIVE 2.
Alternative WE EE IF WR EB HP EI SA
A 9 8 5 8 7 7 8 2
B 7 5 7 5 8 8 6 5
C 7 5 8 4 8 8 6 9
D 3 3 6 4 6 7 5 6
E 3 3 8 6 8 6 5 8
Weight 0,2 0,2 0,05 0,1 0,1 0,1 0,15 0,1
Type of criterion III III III I I III III III
Thresholds 2 4 4 2 2 4
1.2. A. Results by method of Ref. [4] method. The preorder
is shown in Fig. 3.
C A B E D
Fig. 3: Graphs sub zone Martin Hickman, alternative 1.2.A . Promethee II
1.2. B. Results following Ref [9] method are shown in Fig. 4.
A C B E D
Fig. 4: Graphs sub zone Martin Hickman, alternative 1.2.B. Promethee II,
modified.
Changing the weights (0,2 – 0,15 – 0,15 – 0,10 – 0,10 – 0,10
– 0,10 – 0,10), the results are:
1.1.A
A B C E D
1.1.B
A B C E D
1.2.A
C A B E D
1.2.B
A C B E D
2. Sub zone LA ESTRELLA
2.1. ALTERNATIVE 1: Decisional matrix is shown in Table
III.
TABLE III
DECISIONAL MATRIX FOR LA ESTRELLA, ALTERNATIVE 1.
Alternative WE EE IF WR EB HP EI SA
A 7 6 1 8 5 2 8 6
B 7 6 5 4 5 9 6 5
C 3 3 6 4 8 9 3 9
D 2 2 6 4 5 6 5 6
E 3 2 8 5 8 6 4 8
Weight 0,15 0,15 0,15 0,1 0,15 0,1 0,1 0,1
Type of criterion III III III I I III III III
Thresholds 2 4 4 6 6 2
2.1A. Results by Ref [4] method. The preorder is shown in
Fig. 5.
E C A B D
Fig. 5: Graph sub zone La Estrella alternative 2.1A Promethee II
2.1B. Results following Ref [6] method are shown in Fig. 6.
E C B A D
Fig. 6: Graphs sub zone La Estrella alternative 2.1B Promethee II, modified.
2.2. ALTERNATIVE 2: Changing some criteria values and
maintaining weights (Table IV).
TABLE IV
DECISIONAL MATRIX FOR LA ESTRELLA, ALTERNATIVE 2.
Alternative WE EE IF WR EB HP EI SA
A 7 6 5 8 5 6 8 6
B 7 6 5 4 5 9 6 5
C 3 3 6 4 8 9 3 9
D 2 2 6 4 5 6 5 6
E 3 2 8 5 8 6 4 8
Weight 0,15 0,15 0,15 0,1 0,15 0,1 0,1 0,1
Type of criterion III III III I I III III III
Thresholds 2 4 4 6 6 2
2.2A. Results by Ref. [4] method. The preorder is shown in
Fig. 7.
A E C B D
Fig. 7: Graph sub zone La Estrella, alternative 2.2A, Promethee II.
2.2B.Results following Ref [6] method is shown in Fig. 8.
A E C B D
Fig. 8: Graph sub zone La Estrella alternativa 2.2B, Promethee II modified.
Changing weights (0,20 – 0,15 – 0,15 – 0,10 – 0,10 – 0,10 –
0,10 – 0,10) the results are:
2.1.A
E C A B D
518 2009 7th IEEE International Conference on Industrial Informatics (INDIN 2009)
4. 2.1.B
B E A C D
2.2.A
A E C B D
2.2.B
A B E C D
3. Sub zone RIVADAVIA SUR
3.1. ALTERNATIVA 1: Initial matrix is shown in Table V.
TABLE V
DECISIONAL MATRIX FOR RIVADAVIA SUR, ALTERNATIVE 1.
Alternative WE EE IF WR EB HP EI SA
A 8 5 1 9 5 7 9 6
B 6 6 6 5 5 8 6 5
C 3 2 2 4 8 9 1 9
D 2 2 5 4 6 7 5 6
E 3 3 8 5 8 6 4 8
Weight 0,25 0,1 0,1 0,05 0,1 0,1 0,2 0,1
Type of criterion III III III I I III III III
Thresholds 2 4 4 2 2 4
3.1A. Results following Ref [4] method is shown in Fig. 9.
E A B C D
Fig. 9: Graph sub zone Rivadavia Sur, alternative 3.1A, Promethee II.
3.1B. Results following Ref. [6] method in Fig. 10.
A B E C D
Fig. 10: Graph sub zone Rivadavia Sur, alternative 3.1B, Promethee II,
modified.
3.2. ALTERNATIVE 2: Changing some criteria values and
maintaining weights (Table VI).
TABLE VI
DECISIONAL MATRIX FOR RIVADAVIA SUR, ALTERNATIVE 2.
Alternative WE EE IF WR EB HP EI SA
A 8 5 6 9 5 7 9 6
B 6 6 6 5 5 8 6 5
C 3 2 2 4 8 9 1 9
D 2 2 5 4 6 7 5 6
E 3 3 8 5 8 6 4 8
Weight 0,25 0,1 0,1 0,05 0,1 0,1 0,2 0,1
Type of criterion III III III I I III III III
Thresholds 2 4 4 2 2 4
3.2A. Results following Ref. [4] method in shown in Fig. 11.
A E B C D
Fig. 11: Graph sub zone Rivadavia Sur, alternative 3.2A, Promethee II.
3.2B. Results following Ref. [6] is in Fig. 12.
A B E C D
Fig. 12: Graph sub zone Rivadavia Sur, alternative 3.2B, Promethee II,
modified.
Changing weights (0,2 – 0,15 – 0,15 – 0,10 – 0,10 – 0,10 –
0,10 – 0,10) the results are:
3.1.A
E A B C D
3.1.B
A B E C D
3.2.A
A E B C D
3.2.B
A B E C D
4. Sub zone PICHANAL
4.1. ALTERNATIVE 1: Initial matrix is shown in Table VII.
TABLE VII
DECISIONAL MATRIX FOR PICHANAL, ALTERNATIVE 1.
Alternative WE EE IF WR EB HP EI SA
A 6 6 1 7 5 2 8 2
B 6 5 4 4 5 8 6 5
C 3 2 9 4 8 9 1 9
D 2 2 6 4 5 7 5 6
E 3 2 8 5 8 6 4 8
Weight 0.20 0.15 0.10 0.10 0.20 0.05 0.05 0.15
Type of criterion III III III I I III III III
Thresholds 2 2 4 4 2 2 4
4.1A. Results following Ref. [4] is shown in Fig. 13.
E C B A D
Fig. 13: Graph sub zone Pichanal, alternative 4.1A, Promethee II.
4.1B. Results following Ref [6] is shown in Fig. 14.
E C A B D
Fig. 14: Graph sub zone Pichanal, alternative 4.1B, Promethee II, modified.
4.2. ALTERNATIVE 2: With other values same weights
(Table VIII).
TABLE VIII
DECISIONAL MATRIX FOR PICHANAL, ALTERNATIVE 2.
Alternative WE EE IF WR EB HP EI SA
A 6 6 5 7 5 7 8 2
B 6 5 4 4 5 8 6 5
C 5 2 9 4 8 9 1 9
D 2 2 6 4 5 7 5 6
E 3 2 8 5 8 6 4 8
Weight 0.20 0.15 0.10 0.10 0.20 0.05 0.05 0.15
Type of criterion III III III I I III III III
Thresholds 2 2 4 4 2 2 4
4.2A. Results following Ref [4] is in Fig. 15.
C A E B D
Fig. 15: Graph sub zone Pichanal, alternative 4.2A, Promethee II.
4.2B. Results following Ref [4] is shown in Fig. 16.
C E A B D
Fig. 16: Graph sub zone Pichanal, alternative 4.2B, Promethee II, modified.
With other weights (0,2 – 0,15 – 0,15 – 0,10 – 0,10 – 0,10 –
0,10 – 0,10) the results are:
4.1.A
E C B A D
4.1.B
E B C A D
4.2.A
C A E B D
4.2.B
A C E B D
5. Sub zone JOAQUIN V. GONZALEZ
5.1. ALTERNATIVE 1: Initial matrix is shown in Table IX.
2009 7th IEEE International Conference on Industrial Informatics (INDIN 2009) 519
5. TABLE IX
DECISIONAL MATRIX FOR JOAQUIN V. GONZÁLEZ, ALTERNATIVE 1.
Alternative WE EE IF WR EB HP EI SA
A 6 6 1 7 7 2 9 2
B 6 5 6 4 7 8 6 5
C 3 4 9 4 8 9 4 9
D 2 2 7 4 5 7 5 6
E 3 2 8 4 8 6 4 8
Weight 0,2 0,15 0,1 0,1 0,15 0,1 0,1 0,1
Type of criterion III III III I I III III III
Thresholds 2 4 4 2 2 4
5.1A. Results following Ref. [4] is shown in Fig. 17.
C B E A D
Fig. 17: Graph sub zone Joaquin V. Gonzalez, alternative 5.1A, Promethee
II.
5.1B. Results following Ref. [6] is in Fig. 18.
C B A E D
Fig. 18: Graph sub zone Joaquin V. Gonzalez, alternative 5.1B, Promethee II,
modified.
5.2. ALTERNATIVE 2: Other values and same weights
(Table X).
TABLE X
DECISIONAL MATRIX FOR JOAQUIN V. GONZÁLEZ, ALTERNATIVE 2.
Alternative WE EE IF WR EB HP EI SA
A 6 6 3 7 7 4 9 2
B 6 5 6 4 7 8 6 5
C 5 4 9 4 8 9 4 9
D 2 2 7 4 5 7 5 6
E 3 2 8 4 8 6 4 8
Weight 0,2 0,15 0,1 0,1 0,15 0,1 0,1 0,1
Type of criterion III III III I I III III III
Thresholds 2 4 4 2 2 4
5.2A. Results following Ref. [4] is shown in Fig. 19.
C B A E D
Fig. 19: Graph sub zone Joaquin V. Gonzalez, alternative 5.2A, Promethee
II.
5.2B. Results following Ref. [6] is in Fig. 20.
C A B E D
Fig. 20: Graph sub zone Joaquin V. Gonzalez, alternative 5.2B, Promethee II,
modified.
With other weights (0,2 – 0,15 – 0,15 – 0,10 – 0,10 – 0,10 –
0,10 – 0,10) the results are:
5.1.A
C B E A D
5.1.B
C B A E D
5.2.A
C B A E D
5.2.B
C B A E D
6. Sub zone LAS LAJITAS:
6.1. ALTERNATIVE 1: Initial matrix is shown in Table XI.
TABLE XI
DECISIONAL MATRIX FOR LAS LAJITAS, ALTERNATIVE 1.
Alternative WE EE IF WR EB HP EI SA
A 3 6 1 3 3 3 4 2
B 3 4 3 3 5 8 3 5
C 2 3 9 4 8 9 1 9
D 2 2 6 4 5 7 1 6
E 2 2 8 4 8 6 1 8
Weight 0,2 0,05 0,1 0,2 0,2 0,15 0,05 0,1
Type of criterion III III III I I III III III
Thresholds 2 4 4 6 6 2
6.1A. Results following Ref. [4] method is in Fig. 21.
C E D B A
Fig. 21: Graph sub zone Las Lajitas, alternative 6.1A, Promethee II.
6.1B. Results following Ref. [6] method is shown in Fig. 22.
C E D B A
Fig. 22: Graph sub zone Las Lajitas, alternative 6.1B, Promethee II,
modified.
6.2. ALTERNATIVE 2: Other values and same weights
(Table XII).
TABLE XII
DECISIONAL MATRIX FOR LAS LAJITAS, ALTERNATIVE 2.
Alternative WE EE IF WR EB HP EI SA
A 3 6 1 3 3 2 4 2
B 3 4 3 3 5 8 3 5
C 2 3 9 4 8 9 1 9
D 2 2 6 4 5 7 1 6
E 2 2 8 4 8 6 1 8
Weight 0,2 0,05 0,1 0,2 0,2 0,15 0,05 0,1
Type of criterion III III III I I III III III
Thresholds 2 4 4 6 6 2
6.2A. Results following Ref. [4] method is shown in Fig. 23.
C E D B A
Fig. 23: Graph sub zone Las Lajitas, alternative 6.2A, Promethee II.
6.2B. Results following Ref. [6] method is in Fig. 24.
C E D B A
Fig. 24: Graph sub zone Las Lajitas, alternative 6.2B, Promethee II,
modified.
Changing weights (0,2 – 0,15 – 0,15 – 0,10 – 0,10 – 0,10 –
0,10 – 0,10) the results are:
6.1.A
C E D B A
6.1.B
C E B D A
6.2.A
C E D B A
6.2.B
C E B D A
IV. CONCLUSIONS
Following the results mentioned above (Table XIII), we can
obtain as conclusions that the PROMETHEE method is a
very useful tool to elaborate a erosion control integral Plan. It
is robust as we have confirmed changing a little the relative
preference. Besides, with both methods similar results have
been obtained.
520 2009 7th IEEE International Conference on Industrial Informatics (INDIN 2009)
6. TABLE XIII
SUMMARY RESULTS OF PROMETHEE METHODS APPLICATION TO EROSION
CONTROL PLANS IN SALTA PROVINCE (ARGENTINE).
Subzone Martin
Hickman
La
Estrella
Rivadavia
Banda Sur
Pichanal J. V.
González
Las
LajitasMethod
PROMETHEE, applying variable weights in each sub zone
1.A A E E E C C
1.B A E A E C C
2.A C A A C C C
2.B A A A C C C
PROMETHEE, applying the same weights in each sub zone
1.A A E E E C C
1.B A B A E C C
2.A C A A C C C
2.B A A A A C C
Note: 1.A and 2.A : Following the initial method of Ref. [4], 1.B and 2.B:
Following method modified by the authors [6].
For this purposes, we would recommend to use Promethee II
modified using the ELECTRE I weights. Besides, with usual
criterion and type III pseudocriterion have been obtained the
best results. We could recommend to Salta Government the
following actions:
Las Lajitas: extensive farming and livestock. If it is only
farming it could be with crop rotation. The livestock should
be with natural forestry and foraging plants.
La Estrella: We can combine Autochthonous and high value
forestry with biomass production.
Pichanal: Similar to Las Lajitas.
Martin Hickman: Autochthonous forestry, combined with
some crop rotation and livestock like Las Lajitas.
Rivadavia Banda Sur: Similar to La Estrella.
Joaquin V. Gonzalez: Similar to Las Lajitas combined in
some areas with high value forestry.
ACKNOWLEDGMENT
We thank to “Agencia Española para la Cooperación
Internacional y el Desarrollo” (AECID) by the financing
support of the project A/013294/07 titled "ELABORACION
DE UN PLAN INTEGRAL DE LUCHA CONTRA LA
DESERTIZACION Y LA EROSION EN EL CHACO
SALTEÑO (ARGENTINA)"
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