Analytic Hierarchy Process Model Research Articles

Systematic Evaluation and Optimization of Unmanned Aerial Vehicle Tilt Photogrammetry Based on Analytic Hierarchy Process

In the data acquisition and processing of Unmanned Aerial Vehicle(UAV) oblique photography monomer modeling, it is hard to balance the model quality and the production efficiency. This research applied the Analytic Hierarchy Process (AHP) evaluation method in the field of systems engineering to the management and decision of UAV oblique photography monomer modeling. Firstly, the AHP model is constructed by the expert survey method, and the relative weights of 6 evaluation indicators in the first hierarchies and the comprehensive ones of 3 in the second hierarchies are calculated. Then, each index data of different photo modeling schemes were collected and processed through experiments, and the AHP model was used to systematically evaluate each modeling scheme. Finally, the quadratic function is constructed with two variables, the number of photos and the AHP system score, meanwhile obtaining the optimal scheme by calculation. This is a useful attempt to apply the scientific evaluation method in the field of systems engineering to the production management of UAV aerial surveys. In this way, the efficiency of internal and external data collection and processing can be maximally improved while guaranteeing modeling accuracy.

Gully erosion vulnerability modelling, estimation of soil loss and assessment of gully morphology: a study from cratonic part of eastern India.

A highly visible form of soil erosion is gully, a significant geomorphological feature, resulting from water erosion and causing land degradation and deterioration. In arid and semi-arid environment, gully erosion is conceived as an important source of sediment supply washing out the top fertile soil and exposing lower soil layers. The present study is conducted on the lateritic terrain of Rupai watershed of eastern plateau fringe of India, where water erosion is a serious concern. In order to prepare a gully erosion vulnerability mapping, the analytical hierarchy process (AHP) model coupled with geospatial technology is adopted taking into account thirteen bio-physical factors. It is revealed that around 49% area of the watershed belongs to high to very high gully erosion vulnerability zone (GEVZ) followed by moderate risk zone of 31.64%. This model is validated performing an accuracy assessment, which is calculated to be 90.91%, and the value of Kappa co-efficient is 0.86. It is imperative to estimate the average annual soil loss alongside of delineating GEVZ; thus, the revised universal soil loss equation (RUSLE) model is used with geospatial technology. It unveils that the average estimated soil loss of the watershed varies from < 15 to 431 t ha-1 y-1. Around 29% of the study area experiences high to very high (57 to > 147 t ha-1 y-1) soil erosion risk, where 68% area endures low level of soil erosion risk (< 15 t ha-1 y-1). The study of gully morphology depicts gully depth ranging from < 1 to 5m (small to medium gully) with V and U shapes. Results obtained from this study may help in planning and management of land use and soil erosion conservation.

Landslide susceptibility mapping by integrating analytical hierarchy process, frequency ratio, and fuzzy gamma operator models, case study: North of Lorestan Province, Iran.

Identifying landslide-prone areas is an essential step in assessing landslide risk and reducing landslide damage. In this paper, GIS-based spatial analysis has been used to prepare the landslide susceptibility (LS) map in the north of Lorestan province in western Iran. For this purpose, three main criteria and their sub-criteria were identified as causative factors including geology and topography (i.e., distance from the fault, lithology, slope, aspect, and elevation), climate (i.e., rainfall and distance from the river), and environmental parameters (i.e., distance from the road, land-cover, NDVI). One hundred thirty-six known landslides were randomly divided into training ([Formula: see text] 70%) and validation ([Formula: see text] 30%) datasets. This study is based on the integration of popular analytic hierarchy process (AHP), frequency ratio (FR), and the fuzzy gamma operator (FGO) techniques. AHP was utilized to prioritize causal factors and fuzzy technique was applied in two stages of factor map fuzzification and calculation of sub-criteria maps and then overlap of fuzzified map layers. The fuzzy membership (FM) values were determined based on the FR method, which was normalized between the ranges of 0 and 1. Finally, LS zoning maps were estimated in five susceptibility classes (very low, low, moderate, high, and very high). Validation processes by comparing the three output maps with the layer of validation landslides in the study area and area under receiver operating characteristic curve confirm that the gamma value of 0.9 (AUC = 0.88) offers a more accurate LS map compared to other gamma values. The results of this study will be reliable for landslide risk reduction strategies.

Flood vulnerability of a few areas in the foothills of the Western Ghats: a comparison of AHP and F-AHP models.

Flooding is one of the most destructive natural catastrophes that can strike anywhere in the world. With the recent, but frequent catastrophic flood events that occurred in the narrow stretch of land in southern India, sandwiched between the Western Ghats and the Arabian Sea, this study was initiated. The goal of this research is to identify flood-vulnerable zones in this area by making the local self governing bodies as the mapping unit. This study also assessed the predictive accuracy of analytical hierarchy process (AHP) and fuzzy-analytical hierarchy process (F-AHP) models. A total of 20 indicators (nine physical-environmental variables and 11 socio-economic variables) have been considered for the vulnerability modelling. Flood-vulnerability maps, created using remotely sensed satellite data and geographic information systems, was divided into five zones. AHP and F-AHP flood vulnerability models identified 12.29% and 11.81% of the area as very high-vulnerable zones, respectively. The receiver operating characteristic (ROC) curve is used to validate these flood vulnerability maps. The flood vulnerable maps, created using the AHP and F-AHP methods, were found to be outstanding based on the area under the ROC curve (AUC) values. This demonstrates the effectiveness of these two models. The results of AUC for the AHP and F-AHP models were 0.946 and 0.943, respectively, articulating that the AHP model is more efficient than its chosen counterpart in demarcating the flood vulnerable zones. Decision-makers and land-use planners will find the generated vulnerable zone maps useful, particularly in implementing flood mitigation plans.

Improved AHP Model and Neural Network for Consumer Finance Credit Risk Assessment

With the rapid expansion of the consumer financial market, the credit risk problem in borrowing has become increasingly prominent. Based on the analytic hierarchy process (AHP) and the long short-term memory (LSTM) model, this paper evaluates individual credit risk through the improved AHP and the optimized LSTM model. Firstly, the characteristic information is extracted, and the financial credit risk assessment index system structure is established. The data are input into the AHP-LSTM neural network, and the index data are fused with the AHP so as to obtain the risk level and serve as the expected output of the LSTM neural network. The results of the prewarning model after training can be used for financial credit risk assessment and prewarning. Based on LendingClub and PPDAI data sets, the experiment uses the AHP-LSTM model to classify and predict and compares it with other classification methods. Experimental results show that the performance of this method is superior to other comparison methods in both data sets, especially in the case of unbalanced data sets.

Analysis and Evaluation of Enterprise Performance Appraisal Index Based on Fuzzy AHP Model

The decisive and decision-making challenges of business investment are for the good development of enterprises. In order to have a better market advantage of chemical granules for enterprises, the analytic hierarchy process (AHP) is constructed to analyze the company's performance. Under the environment of state-owned enterprises and foreign enterprises competing for market resources together, building a performance evaluation system suitable for the development stage of the company can adjust the planning strategy and analyze its own disadvantages in time. When analyzing the effective combination of performance appraisal and enterprise development, as well as the effective communication between employees and enterprise managers, feedback information is obtained through the experimental results. Under this analysis condition, this paper describes the feasibility and soundness of the AHP model system for enterprises, and what favorable decision-making methods can be provided for later development. Based on the selection of performance evaluation indicators, AHP analysis can clearly get the weight of each indicator and the correlation between them, in order to improve and build a new system of evaluation indicators. This paper concludes the following: (1) based on the analysis of enterprise performance appraisal indicators, using the AHP model evaluation method to calculate the indicators, the optimized indicators for the performance appraisal of the comprehensive and efficient significantly improved. (2) Comparing the traditional AHP calculation with the optimized model calculation, the results show that under the optimized calculation, the index is more concise and the fluctuation is reduced, which reduces the burden on administrators. (3) In the membership degree of index evaluation grade, the membership degree with an excellent grade is 0.9, which shows that the index plays a very important role in evaluation. (4) According to the development indicators of enterprises in recent years, it is found that the growth rate of resources is growing slowly, the demand for the talent market is still in a stable state as always, and its financial resources have increased by 120% in 2016.

A Quantitative Trading Strategy Based on A Position Management Model

With the rapid development of economic globalization, various financial products have appeared in the domestic and foreign financial markets. How to adopt ideal trading strategies to meet the demands of market traders for high returns and low risks has become the focus of investors and the research goal of scholars. In order to solve this problem, this paper establishes a quantitative trading strategy based on the position management model.First, we performed price forecasting for gold and bitcoin based on the Time-series ARIMA method. A differential autoregressive moving average model was developed for the price data of gold and bitcoin at different cycle times, respectively, and used to predict the price of the next trading day. Error analysis was performed with the actual prices, and it was found that the prediction error was the smallest when the data was 60 days, and the relative error of the average prediction value (APV) could be controlled at 0.003016.Then, we establish a quantitative trading strategy based on the position management model. We use the Apriori algorithm of Association rules to study the rising and falling rules of gold and bitcoin assets. According to the rule of " High throw bargain - hunting" in the investment market, we established a position management model and achieved dynamic and stable returns. After the model is established, we continue to introduce the evaluation indexes of the investment value of financial assets, among which the first-class indexes are profitability and safety. We use the Analytic Hierarchy Process (AHP) to determine the weight of each evaluation index, and allocate the daily trading investment through the ratio of two asset evaluation indexes. This quantitative trading strategy, based on the position management model of AHP, can not only stabilize the income but also avoid risks, reaching a quantitative trading strategy with an annualized rate of return of 25%. On September 10th, 2021, the accumulated income could reach 223,640.58 USD.Further, we evaluate the profitability and risk resistance of the strategy using Principal component analysis. Model validation was performed by varying the parameter values and selecting the parameters that yielded a locally optimal solution, which was found to be consistent with our initial parameters and was proof of the optimal solution of the model.Finally, we conducted a sensitivity analysis of the model. The two variable parameters of initial commission fluctuation and investment principal of gold and bitcoin are varied up and down respectively, and the results show that as the initial commission increases or the principal decreases, the number of trades under this strategy gradually decreases and the trading return gradually decreases, and the sensitivity curve shows that the model is sensitive and meets expectations.

Resilience Capacity Evaluation for the Safety Management System of Power Grid Enterprise Based on AHP-MEE Model

With the rapid development of urbanization and modernization, people’s requirements for electric power are also consequently increasing. It is thus essential to ensure the sustainable and stable supply of power and maintain the safety performance of the power grid. Constructing a modern safety management system (SMS) can effectively improve power enterprises’ ability to respond to emergencies. To effectively evaluate the ability of electric power enterprises to resist risk impact, and the recovery and optimization ability after impact, this paper makes a systematic investigation of the SMS of power enterprises. It evaluates the safety resilience capabilities of the SMS from four basic elements of stability, redundancy, efficiency, and adaptability. A multilevel evaluation system of electric power enterprises’ safety resilience capacity consists of four first-level and 25 second-level indices. Then, an analytic hierarchy process (AHP) and matter-element extension (MEE) joint model—the AHP-MEE evaluation model of the resilience capacity of power enterprises—is proposed. A case study of electric power enterprise safety resilience evaluation is conducted using the proposed resilience evaluation index system and assessment model. The results showed that the provided model agrees with the actual situation and verified the index system and model for electric power enterprise safety reliability and applicability. Improved enterprise safety resilience recommendations are proposed accordingly based on the evaluation results. This work proposes a robust safety resilience capacity evaluation method in the face of upcoming new risks, thus building a good working foundation to construct a new power system with new energy sources.

Optimization and Allocation Method of Regional Groundwater Pollution Investigation Based on Analytic Hierarchy Process Model

In urban construction, various heavy metals and pollutants have become an important source of urban pollution, and groundwater has become its main pollution object. In order to investigate and analyze the water pollution in the main urban area of HeFei, this article firstly analyzes the whole process through the investigation of groundwater status and characteristics and simulates each step effectively using the analytic hierarchy process (AHP). Then, taking the nine districts of HeFei as the actual investigation site and research object, a systematic and comprehensive investigation and simulation of groundwater pollution in the HeFei area was carried out. The results showed that the weight of chemical pollution is 0.450, environmental factors 0.241, and domestic pollution 0.309 in the target layer of HeFei’s main urban area, while the sample distribution weight of each unit was the highest in Baohe District and the lowest in Chaohu City. Finally, the countermeasures for regional water pollution are put forward.

Achievement Prediction of English Majors Based on Analytic Hierarchy Process and Genetic Algorithm

The prediction and analysis of student achievement aim to realize personalized guidance for students and improve student achievement and teacher’s teaching achievement. Student achievement is affected by many factors such as family environment, learning conditions, and individual performance. Traditional prediction methods often ignore that different factors have different effects on the same student’s score, and different students have different effects on the same factor, so the model constructed cannot realize personalized analysis and guidance for students. Therefore, this paper proposes a prediction model based on the analytic hierarchy process and genetic algorithm. Firstly, according to the relationship among different levels, the analytic hierarchy process (AHP) model is established. Then, a k-means clustering algorithm is used to process the experimental data. Secondly, in order to get rid of the negative impact of the randomness of the initial threshold and weight on model prediction accuracy, which leads to the prediction result falling into a local minimum, a genetic algorithm is proposed to find the optimal initial threshold and weight of model first. Finally, a prediction model based on the BP neural network is established to predict students’ scores, which proves that the prediction effect is good. The experiment was conducted with English major students in a university as the research object. Experimental results show that compared with traditional data mining methods, the proposed method has better prediction accuracy.

Designing a MCDM Model for Selection of an Optimal ERP Software in Organization

Among the growth of the Industrial Revolution 4.0 and moving towards 5.0, the application of science and technology is a decisive factor in catching up with new business trends. In particular, the application of the enterprise resources planning (ERP) towards enterprise management is extremely important. ERP systems help businesses save time and effort in managing projects and utilizing resources. Instead of manual handling, the processes are carried out automatically. At the same time, the system operates correctly and efficiently which avoids errors. In this work, the author explored the application of a fuzzy multicriteria decision making (FMCDM) method including Fuzzy Analytic Hierarchy Process model (FAHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model for the evaluation of various ERP software (mainly warehouse and inventory management module). First, the fuzzy AHP model is applied to calculate the weights of criteria, and then, the obtained weights are used in the TOPSIS method for ranking of the alternatives. This study would assist decision makers in an ERP implementation environment in deciding a suitable ERP software for various industries.

Generating Landslide Susceptibility Maps Using Mathematical Models and UAV data: The Case of Çankırı Region in Türkiye

Landslides are natural disasters that affect not only residential areas but alos forest ecosystems. In order to determine the areas with high landslide risk and take necessary measures in risky areas, landslides susceptible should analyzed and susceptible map (LSM) should be developed in advance. In this study, a LSM was produced for two study areas with different sizes including Çankırı province and in the Ilısılık Village of Çankırı in Türkiye. Analytical Hierarchy Process (AHP) and Logistic Regression Modeling (LRM) methods were used to generate LSM based on the main factors including elevation, slope, lithology, distance to faults - streams and roads. For Çankırı province, 30 m resolution Digital Elevation Model (DEM) was used to produce the map while one-meter resolution Digital Terrain Model (DTM), generated by using Unmanned Aerial Vehicle (UAV), was used for Ilısılık Village. As a result of the study, AHP model success was calculated as 73.9% and 91.7% for Çankırı and Ilısılık, respectively, considering the previous landslides occurred in the region. On the other hand, LRM model success was 75.2% and 93.1%, respectively. It was also indicated that DTM data is advantageous to DEM data by offering a more precise and detailed usage opportunity. The sensitivity is revealed more clearly and effectively in precision planning studies such as risk mapping of natural disasters that requires special measurement in small areas.

Business Strategy Analysis of Cocoa Agribusiness Commodity Export Company at Agro Tama

Cocoa, which is known as one of the people's plantations, is one of the commodities that has a positive contribution during the Covid-19 pandemic. The positive results of macro data related to the contribution of agriculture, especially cocoa to Indonesia's income during the pandemic, in fact experienced problems in raw materials which experienced a gradual decline in productivity in recent years, and also affected the productivity of several domestic cocoa companies. The decline in raw materials and the COVID-19 pandemic that has occurred since the end of 2019 also added to other problems faced by agricultural commodity export companies, including Agro Tama. Agro Tama is a domestic export company located in Makassar City which has been established for more than 20 years and contributes to the Indonesian economy through the export of cocoa beans. This study aims to analyze the company's internal and external factors, namely Strength, Weakness, Opportunity and Threat to produce an alternative strategy that is right for the company's condition. The results of the SWOT analysis show that the company's condition is in quadrant III strategy, namely WO (Weakness Opportunity) and produces 4 alternative strategies which are then analyzed again using the Analytical Hierarchy Process model. The results of the AHP analysis resulted in a proposed priority strategy, namely optimizing the company's export commodity diversification, with the main objective of the analysis model being the company's business development during the pandemic with a weight of 0.303.

Google’s app store policy change and the responses elicited from mobile content firms: A case study on Korea’s app store ecosystem

The current app store market is dominated by major platform firms that control mobile operating systems, such as Apple iOS and Google Android. While Apple has maintained strict control over its app store, Google has opted for a more lenient policy, until March 2022, when it would enforce its proprietary billing systems onto all paid and in-app purchases. This study examines the impact of Google’s policy change on local Korean markets, especially the app store market, which features local app stores with a small but notable presence. By analyzing the responses of 18 managers, each of whom works for a different mobile content firm, this study examines specifically the probable strategies that mobile content firms can implement in response to Google’s in-app purchase policy change. Based on the analytic hierarchy process (AHP) model, this study finds that Korea’s local mobile content firms are strongly inclined toward diverging away from Google’s in-app purchase policy, but only if there will be sufficient governmental protection in tandem with cooperation from other industry firms. This study is one of the first to examine the responses to this policy change; thus, this research presents an array of meaningful implications to a timely and sensitive issue.

Potensi dan Strategi Pengembangan Duku Bacan (lansium domesticum corr.) Di Desa Amasing Kali Kabupaten Halmahera Selatan

ABSTRACTThe main objectives of the present study were to identify the potency to develop the Duku Bacan commodity and to formulate the development strategy of the Duku Bacan Cultivation in the Amasing village of South Halmahera Regency. The samples were collected from 51 Duku growersusing the Purposive Random Sampling method. The data were then analyzed descriptively and quantitatively. The quantitative statistical analyses include the analysis of potency (the density index of Duku Bacan trees, the plant productivity of Duku Bacan, and the potency analysis of Duku Bacan plant), Location Quotient (LQ) analysis, and trading margin analysis. Meanwhile, the qualitative analyses consist of the analyses of land suitability, institutional system, and marketing system of Duku. The SWOT analysis was applied to formulate the development strategy, whilst the priority of the development strategy for Duku Bacan was analyzed using the Analytical Hierarchy Process (AHP) model. The findings revealed that the age of Duku growers was classified into the productive age group with the average educational background level was elementary-school. The traditional farming system was predominantly applied by the growers with low or the absence of a technology input. The potency of Duku Bacan plants in Amasing Kali Village of South Halmahera Regency was identified from the yield that reached up to 2,381 tonnes/hectare with the potency of 176,2 Kg per year. The LQ &gt;1 means that Duku Bacan plant is considered the priority local commodity. The lands in Amasing Kali village are suitable for growing Duku and there are 2 (two) marketing channels for Duku developed in this village. The development strategy of Duku Bacan cultivation in Amasing Kali village based on the scale of priority include the enhancement of the roles of Local Government, the establishment of farmer group institution, the increase of farmer capacity, the optimization of duties and roles of extension workers, the increase of plant production, the optimization of potential land use, strengthening the agribusiness system of Duku and promotion, and also the utilization of economic agencies.Keywords: Duku Bacan, potency, strategy, development

A spherical fuzzy AHP model for contractor assessment during project life cycle

Abstract Measurement of construction performance is essential to a clear image of the present situation. This monitoring by the management team is necessary to identify locations where performance is exceptionally excellent or poor and to identify the primary reasons so that the lessons gained may be exported to the firm and its progress strengthened. This research attempts to construct an integrated mathematical model utilizing one of the recent methodologies for dealing with the fuzzy representation of experts’ knowledge and judgment considering hesitancy called spherical fuzzy analytic hierarchy process (SFAHP) method to assess the contractor’s performance per the project performance parameters (cost, schedule, quality, leadership, and change management). At the same time, most project control systems are currently applied through software like Primavera P6 or MS Project. These look at a project’s cost and schedule status by following the earned value analysis for finding the performance. Based on decision makers’ preferences, the analytic hierarchy process (AHP) may be used to arrive at the optimum conclusion. AHP approaches are discussed, including AHP, grey-AHP, fuzzy-AHP, and SFAHP weights comparison. Calculation results showed that the spherical fuzzy approach differs significantly from the other approaches where it considers the decision maker’s hesitation when making linguistic multicriteria decisions and then, as a result, recommends applying periodically for performance measurement. This model can be viewed as a valuable way to help the decision-making stakeholders in the construction sector do the best job about critical issues at a suitable time.

Use of Multi-Criteria Decision Analysis (MCDA) for Mapping Erosion Potential in Gulf of Mexico Watersheds

The evaluation of soil erosion is often assessed using traditional soil-loss models such as the Revised Universal Soil-Loss Equation (RUSLE) and the Soil and Water Assessment Tool (SWAT). These models provide quantitative outputs for sediment yield and are often integrated with geographic information systems (GIS). The work described here is focused on transitioning towards a qualitative assessment of erosion potential using Multi-Criteria Decision Analysis (MCDA), for improved decision-support and watershed-management prioritization in a northern Gulf of Mexico coastal watershed. The foundation of this work conceptually defined watershed erosion potential based on terrain slope, geomorphology, land cover, and soil erodibility (as defined by the soil K-factor) with precipitation as a driver. These criteria were evaluated using a weighted linear combination (WLC) model to map generalized erosion potential. The sensitivity of individual criteria was accessed with the one-at-a-time (OAT) method, which simply removed one criterion and re-evaluated erosion potential. The soil erodibility and slope were found to have the most influence on erosion-potential modeling. Expert input was added through MCDA using the Analytical Hierarchy Process (AHP). The AHP allows for experts to rank criteria, providing a quantitative metric (weight) for the qualitative data. The individual AHP weights were altered in one-percent increments to help identify areas of alignment or commonality in erosion potential across the drainage basin. These areas were used to identify outliers and to develop an analysis mask for watershed management area prioritization. A comparison of the WLC, AHP, ensembled model (average of WLC and AHP models), and SWAT output data resulted in visual geographic alignment between the WLC and AHP erosion-potential output with the SWAT sediment-yield output. These observations yielded similar results between the qualitative and quantitative erosion-potential assessment approaches, with alignment in the upper and lower ranks of the mapped erosion potentials and sediment yields. The MCDA, using the AHP and ensembled modeling for mapping watershed potential, provided the advantage of more quickly mapping erosion potential in coastal watersheds for improved management of the environmental resources linked to erosion.

Using AHP and Time Series Model to Make a Better Forest Harvest Plan

Nowadays, climate change is the biggest threat to the sustainable development of our society. How to deal with greenhouse gas emissions is an essential prerequisite to solve this problem. Forest carbon sequestration is an important method to offset the release of carbon dioxide. So the balance between the value of forest products and the value of allowing trees to continue growing is closely related to the amount of carbon storage. Therefore, we are expected to find an optimal management strategy that is suitable for all the forests with different factors such as economic benefits, locations and residents’ satisfaction, so as to improve forest carbon sequestration as much as possible. Three models are established: Model I: Maximum the Carbon Sequestration; Model II: Score System via Analytic Hierarchy Process (AHP) and Entropy-Weight Method; Model III: Time Series Analysis Model and Comparison. For Model I, we aim at maximizing the carbon sequestration of a forest and collect a series of biological parameters throughout the America. We start with describing the relationship between forest growing stock and carbon sequestration via the conversion ratio. Afterwards, we introduce the S-size Logistic curve to fit the growing stock volume and set different harvest and plant rate to observe the change of growing stock volume so that find an optimal harvest or plant rate to maximize the carbon sequestration amount. The transition point is found as 4% for harvest rate and also 4% for plant rate. We note that although the values of these two parameters are identical, it would arouse a significant difference because they are completely different process for a forest. For Model II, we take citizen attitude and timber value into consideration besides carbon sequestration. With the existing data in Model I and the data of new factors from literatures, we construct a score system of different strategies through analytic hierarchy process and entropy method. As a result, the optimal harvest rate decreases although timber value is included, because the local citizens interviewed in the literature showed a high-level anti-harvest attitude, which is consistent with the output of our model. For Model III, we establish a time series of carbon sequestration (or growing stock, they can be interconverted without difficulty). After recognizing the specific type (ARIMA type) of this time series by solving difference equation, the data in the subsequent years can be predicted. Then, we make the varication of the autocorrelation coefficient and partial autocorrelation coefficient (ACF and PACF) the ensure that the result is reliable and dose make sense. The models constructed in this paper make full use of the data obtained from website and literatures to describe and visualize the results. They are validated by sensitivity analysis and can be widely applied to evaluate the different situations. At the end, we provide a two-page newspaper for the authorities and local residents. According to the output in this paper, we finally draw a conclusion that an appropriate harvest rate is advantageous for both forest and society, and this phenomenon is called tending felling or reproduction felling, which is widely applied in managing forest.

Application of fuzzy TOPSIS in assessing performance of agro-processed organic crops: A case of Sustainable Agriculture Tanzania (SAT) in Morogoro

It is a common practice for organisation to carry out assessment exercises regarding performance of their organisational activities and processes. This paper assesses the market performance of five agro-processed crops at Sustainable Agriculture Tanzania (SAT) against some criteria. Experts at SAT supplied useful information by responding through a questionnaire. The fuzzy TOPSIS model was applied in the methodology to rank the processed products. For the sake of comparing results, the fuzzy analytical hierarchy process (AHP) model was also applied to rank the products. It was found that three products maintained their positions in the two models while the other two products (alternatives) exchanged their positions. It was further suggested that more efforts have to go for lower market performing products by looking upon means to improve on their corresponding low weight criteria/sub-criteria.

Environmental Fragility Zoning Using GIS and AHP Modeling: Perspectives for the Conservation of Natural Ecosystems in Brazil

The degradation of natural ecosystems triggers global environmental, economic, and social problems. To prevent this, it is necessary to identify the aptitude of priority areas for conservation or use by considering land fragility from multiple environmental and spatial perspectives. We applied the concept of environmental fragility to a hydrographic basin in southeastern Brazil that establishes (i) potential fragility levels according to slope, soil classes, geological domains, drainage hierarchy, and rainfall information using an algebraic map, and (ii) emerging fragility levels via the addition of the land-use parameters. The methodological approach involved the integration of the analytic hierarchy process (AHP) and weighted linear combination (WLC) into a geographic information system (GIS). The medium and slightly low fragility classes predominated in terms of potential (~60%), and emerging (~70%) environmental fragility models used to model the basin. The model indicated that high and extremely high potential fragilities were concentrated in the upper basin, a region that is considered a global biodiversity hotspot. The areas with high/extremely high classes of emerging fragility in the upper basin decreased, indicating that the natural cover classes and land-use types are not in danger. We also introduce acceptable conservation practices for land management and use according to the environmental fragility categories established in the present work. The methodology applied in this study can be replicated in other global ecoregions. It provides low-cost territorial and environmental zoning and flexible replication and can be adjusted by administrators who are interested in land-use planning.