scholarly journals Using ordered weight averaging (OWA) for multicriteria soil fertility evaluation by GIS (case study: southeast Iran)

2016 ◽  
Author(s):  
Marzieh Mokarram ◽  
Majid Hojati
Keyword(s):  
Fuzzy Logic ◽  
Soil Fertility ◽  
Geographical Information Systems ◽  
Geographical Information ◽  
Weighted Averaging ◽  
Risk Level ◽  
Soil Parameters ◽  
Fars Province ◽  
Risk Levels ◽  
Distance Weighted

Abstract. The Multi-criteria Decision Analysis (MCDA) and the Geographical Information Systems (GIS) are used to provide more accurate decisions for decision makers in order to evaluate the effective factors of the natural science. One of the popular algorithms of the multi-criteria analysis is the Ordered Weighted Averaging (OWA). The OWA procedure depends on some parameters which can be specified by means of the fuzzy logic. The aim of this study is to take the advantage of incorporating the fuzzy logic into GIS-based soil fertility analysis by OWA in the west of Shiraz, Fars province, Iran. In fact, different soil fertility maps with different risk level are prepared in the present study. This study introduces a method for farmers in case of make balance between their budget and their farm soil parameters. A farmer can accept more risk it can use more areas for farming and also the amount of needed budget increases too. For determining the soil fertility maps, the OWA parameters such as potassium (K), phosphor (P), copper (Cu), iron (Fe), manganese (Mn), organic carbon (OC) and zinc (Zn) were used. After generating the interpolation maps with the Inverse Distance Weighted (IDW), the fuzzy maps were generated by the membership functions for each parameter. Finally, by utilizing OWA, six fertility maps with different risk levels (degrees of uncertainty) were made. The results show that by decreasing the risk (no trade-off), increasing the risk, more area within the study area was suitable in terms of the soil fertility. Therefore, using OWA can generate many maps with different risk levels. This leads to different managements based on different financial conditions of farmers.

scholarly journals Using ordered weight averaging (OWA) for multicriteria soil fertility evaluation by GIS (case study: southeastern Iran)

2016 ◽  
Author(s):  
Marzieh Mokarram ◽  
Majid Hojati
Keyword(s):  
Soil Fertility ◽  
Geographical Information Systems ◽  
Multicriteria Analysis ◽  
Geographical Information ◽  
Weighted Averaging ◽  
Risk Level ◽  
Accurate Information ◽  
Fars Province ◽  
Risk Levels ◽  
Wide Range

Abstract. The Multicriteria Decision Analysis (MCDA) and Geographical Information Systems (GIS) are used to provide accurate information on Pedogenic processes and facilitate the work of decision makers. So, MCDA and GIS, can provide a wide range of decision strategies or scenarios in some procedures. One of the popular algorithm of multicriteria analysis is Ordered Weighted Averaging (OWA). The OWA procedure depends on some parameters, which can be specified by means of fuzzy. The aim of this study is to take the advantage of the incorporation of fuzzy into GIS-based soil fertility analysis by OWA in west Shiraz, Fars province, Iran. For the determination of soil fertility maps, OWA parameters such as potassium (K), phosphor (P), copper (Cu), iron (Fe), manganese (Mn), organic carbon (OC) and zinc (Zn) were used. After generated interpolation maps with Inverse Distance Weighted (IDW), fuzzy maps for each parameter were generated by the membership functions. Finally, with OWA six maps for fertility with different risk level were made. The results show that with decreasing risk (no trade-off), almost all of the parts of the study area were not suitable for soil fertility. While increasing risk, more area was suitable in terms of soil fertility in the study area. So using OWA can generate many maps with different risk levels that lead to different management due to the different financial conditions of farmers.

scholarly journals Assessment of Switchgrass-Based Bioenergy Supply Using GIS-Based Fuzzy Logic and Network Optimization in Missouri (U.S.A.)

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4516
Author(s):  
Huynh Truong Gia Nguyen ◽  
Erik Lyttek ◽  
Pankaj Lal ◽  
Taylor Wieczerak ◽  
Pralhad Burli
Keyword(s):  
Land Use ◽  
Fuzzy Logic ◽  
Network Optimization ◽  
Urban Areas ◽  
Geographical Information Systems ◽  
Large Scale ◽  
Spatial Information ◽  
Geographical Information ◽  
Negative Consequences ◽  
Analytical Approaches

Bioenergy has been globally recognized as one of the sustainable alternatives to fossil fuels. An assured supply of biomass feedstocks is a crucial bottleneck for the bioenergy industry emanating from uncertainties in land-use changes and future prices. Analytical approaches deriving from geographical information systems (GIS)-based analysis, mathematical modeling, optimization analyses, and empirical techniques have been widely used to evaluate the potential for bioenergy feedstock. In this study, we propose a three-phase methodology integrating fuzzy logic, network optimization, and ecosystem services assessment to estimate potential bioenergy supply. The fuzzy logic analysis uses multiple spatial criteria to identify suitable biomass cultivating regions. We extract spatial information based on favorable conditions and potential constraints, such as developed urban areas and croplands. Further, the network analysis uses the road network and existing biorefineries to evaluate feedstock production locations. Our analysis extends previous studies by incorporating biodiversity and ecologically sensitive areas into the analysis, as well as incorporating ecosystem service benefits as an additional driver for adoption, ensuring that biomass cultivation will minimize the negative consequences of large-scale land-use change. We apply the concept of assessing the potential for switchgrass-based bioenergy in Missouri to the proposed methodology.

scholarly journals Brief Communication: A case study of risk assessment for facilities associated with earthquake-induced liquefaction potential in Kimhae City, South Korea 

2021 ◽  
Author(s):  
Sang-Soo Jeon ◽  
Daeyang Heo ◽  
Sang-Seung Lee
Keyword(s):  
Geographical Information Systems ◽  
Standard Penetration Test ◽  
Geographical Information ◽  
Liquefaction Potential ◽  
Risk Levels ◽  
Potential Index ◽  
Secondary Damage ◽  
Yangsan Fault ◽  
The Nakdong River ◽  
High Level

Abstract. Liquefaction causes secondary damage after earthquakes; however, liquefaction related phenomena were rarely reported until after the Mw = 5.4 November 15, 2017 Pohang earthquake in Korea. Both the Mw = 5.8 September 12, 2016 Gyeongju earthquake and Mw = 5.4 November 15, 2017 Pohang earthquake occurred in the fault zone of Yangsan City (located in the south-eastern part of Korea), and both of these earthquakes induced liquefaction. Moreover, they demonstrated that Korea is not safe against the liquefaction induced by earthquakes. In this study, estimations and calculations were performed based on the distances between the centroids of administrative districts and an epicenter located at the Yangsan Fault, the peak ground accelerations (PGAs) induced by Mw = 5.0 and 6.5 earthquakes, and a liquefaction potential index (LPI) calculated based on groundwater level and standard penetration test results from 274 locations in Kimhae City (adjacent to the Nakdong river and across the Yangsan Fault). Then, a kriging method using geographical information systems was used to evaluate the liquefaction effects on the risk levels of facilities. The results indicate that a Mw = 5.0 earthquake induces a small and low level of liquefaction, resulting in slight risk for facilities, but a Mw = 6.5 earthquake induces a large and high level of liquefaction, resulting in a severe risk for facilities.

scholarly journals Environmental, climatic and host population risk factors of human cystic echinococcosis in southwest of Iran

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Mohammad Amin Ghatee ◽  
Koorosh Nikaein ◽  
Walter Robert Taylor ◽  
Mehdi Karamian ◽  
Hasan Alidadi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Environmental Factors ◽  
Cystic Echinococcosis ◽  
Geographical Information Systems ◽  
Urban Setting ◽  
Geographical Information ◽  
Annual Rainfall ◽  
Fars Province ◽  
Susceptible Population ◽  
Close Proximity

Abstract Background Cystic echinococcosis (CE), a worldwide zoonotic disease, is affected by various biological and environmental factors. We investigated dog/livestock populations, climatic and environmental factors influencing the distribution of human CE cases in Fars province, southwest Iran. Methods We mapped the addresses of 266 hospitalised CE patients (2004–2014) and studied the effects of different temperature models, mean annual rainfall and humidity, number of frosty days, slope, latitude, land covers, close proximity to nomads travel routes, livestock and dog densities on the occurrence of CE using geographical information systems approach. Data were analyzed by logistic regression. Results In the multivariate model predicting CE, living in an urban setting and densities of cattle and dogs were the most important CE predictors, sequentially. Dry (rained) farm, density of camel and sheep, close proximity to nomads travel routes, humidity, and slope also were considered as the determinants of CE distribution, when analyzed independently. Slope had a negative correlation with CE while temperature, frost days and latitude were not associated with CE. Conclusions In our study, an urban setting was the most important risk factor and likely due to a combination of the high density of key life cycle hosts, dogs and livestock, a large human susceptible population and the high number of abattoirs. Farmland and humidity were highly suggestive risk factors and these conditions support the increased survival of Echinococcus granulosus eggs in the soil. These findings support the development of strategies for control of disease. More research is needed test optimal interventions.

scholarly journals Ένα παράδειγμα σύζευξης γεωγραφικών συστημάτων πληροφοριών (Γ.Σ.Π) με μεθόδους τεχνητής νοημοσύνης (ασαφής λογική & νευρωνικά δίκτυα): εφαρμογή στη γεωμορφολογία.

2005 ◽  
Vol 38 ◽  
pp. 101
Author(s):  
Θ. ΓΚΟΥΡΝΕΛΟΣ ◽  
Ν. ΕΥΕΛΠΙΔΟΥ ◽  
Α. ΒΑΣΙΛΟΠΟΥΛΟΣ
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Information Systems ◽  
Geographical Information Systems ◽  
Spatial Database ◽  
Risk Index ◽  
Geographical Information ◽  
Erosion Risk

In this paper we are studying the erosional procedures on the basis of Geographical Information Systems (GIS) and Artificial Intelligence (Al) methods. More precisely we use fuzzy logic rules to estimate the erosion risk index for the surface rocks and a model of neural networks to spatially categorise the erosion risk index. The described procedure is applied at Zakynthos island, where a complete spatial database already exists.

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