scholarly journals Analytical Hierarchy Process Tool in Google Earth Engine platform : A case study of a tropical landfill site suitability

2021 ◽  
Author(s):  
Soham Bhattacharya ◽  
Surajit ghosh ◽  
Soumya Bhattacharyya
Keyword(s):  
Analytical Hierarchy Process ◽  
Google Earth ◽  
Suitability Analysis ◽  
Cloud Platform ◽  
Scientific Methods ◽  
East Kolkata Wetlands ◽  
Open Dumping ◽  
Google Earth Engine ◽  
Alternative Sites ◽  
Hierarchy Process

Abstract Kolkata being a metropolitan city in India, has its main Municipal Solid Waste dumpsite situated at Dhapa just adjacent to the East Kolkata Wetlands Ramsar site. The current prevalent situation at Dhapa is open dumping leading to various contaminations and hazards putting forth the need to look for alternative sites where the landfiilling operation can be shifted to using scientific methods. A User Interface (UI) based Analytical hierarchy process (AHP) tool has been developed within the Google Earth Engine (GEE) cloud platform to find out the alternative dumping sites using geospatial layers. AHP function is not available as a native algorithm or developed by any researcher in GEE. The tool has three major functionalities, of which the first one handles the UI elements. The AHP procedure is within another function, and the last function integrates the AHP coefficients to the layers generating the final suitability layer. Users can also upload comparison matrix as GEE asset in the form CSV file which gets automatically integrated into the AHP to calculate the coefficients and consistency ratio to generate the spatial suitability layers. This approach showcases a generalized AHP function within the GEE environment, which has been done for the first time. The tool is designed in the cloud platform which is dynamic, robust and suitable for use in various AHP-based suitability analysis in environmental monitoring and assessment.

Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: In a case study of the Lake Tana sub-basin in northwestern Ethiopia

2021 ◽  
Vol 13 (1) ◽  
pp. 1668-1688
Author(s):  
Azemeraw Wubalem ◽  
Gashaw Tesfaw ◽  
Zerihun Dawit ◽  
Belete Getahun ◽  
Tamrat Mekuria ◽  
...  
Keyword(s):  
Land Use Planning ◽  
Analytical Hierarchy Process ◽  
Flood Hazard ◽  
Google Earth ◽  
Information Value ◽  
Lake Tana ◽  
Ahp Model ◽  
Flood Susceptibility ◽  
Susceptibility Modeling ◽  
Hierarchy Process

Abstract The flood is one of the frequently occurring natural hazards within the sub-basin of Lake Tana. The flood hazard within the sub-basin of Lake Tana causes damage to cropland, properties, and a fatality every season. Therefore, flood susceptibility modeling in this area is significant for hazard reduction and management purposes. Thus, the analytical hierarchy process (AHP), bivariate (information value [IV] and frequency ratio [FR]), and multivariate (logistic regression [LR]) statistical methods were applied. Using an intensive field survey, historical document, and Google Earth Imagery, 1,404-flood locations were determined, classified into 70% training datasets and 30% testing flood datasets using a subset within the geographic information system (GIS) environment. The statistical relationship between the probability of flood occurrence and 11 flood-driving factors was performed using the GIS tool. The flood susceptibility maps of the study area were developed by summing all weighted aspects using a raster calculator. It is classified into very low, low, moderate, high, and very high susceptibility classes using the natural breaks method. The accuracy and performance of the models were evaluated using the area under the curve (AUC). As the result indicated, the FR model has better performance (AUC = 99.1%) compared to the AHP model (AUC = 86.9%), LR model (AUC = 81.4%), and IV model (AUC = 78.2%). This research finds out that the applied methods are quite worthy for flood susceptibility modeling within the study area. In flood susceptibility modeling, method selection is not a serious challenge; the care should tend to the input parameter quality. Based on the AUC values, the FR model is comparatively better, followed by the AHP model for regional land use planning, flood hazard mitigation, and prevention purposes.

scholarly journals CLOUD ERP SOFTWARE SUITABILITY ANALYSIS FOR AUTOMOTIVE COMPONENTS SMALL AND MEDIUM INDUSTRY USING ANALYTICAL HIERARCHY PROCESS

2019 ◽  
Vol 10 (2) ◽  
pp. 1
Author(s):  
Mas'ud Adhi Saputra ◽  
Yunita Prabowo ◽  
Wahjoe Witjaksono
Keyword(s):  
Enterprise Resource Planning ◽  
Analytical Hierarchy Process ◽  
Resource Planning ◽  
Company Performance ◽  
Suitability Analysis ◽  
Automotive Component ◽  
Implementation Costs ◽  
Erp Software ◽  
Hierarchy Process ◽  
Small And Medium Industry

Small and Medium Industry (SMI) is a form of business activity that is not only limited to the business of buying and selling, but there are activities of production process, simple organizational management, and cooperation with other parties. SMI has limited in terms of cost and management of human resources in improving company performance. With the tight competition of the automotive component industry, there needs a strategy to manage SMIs effectively and efficiently. Information Technology (IT) plays a very significant role in actualizing that. Many of automotive component SMIs in Indonesia still run the companies in conventional ways. Though of course, IT is able to make company management better, through the use of Enterprise Resource Planning (ERP) software. The problem statement in this study is the selection of cloud-based ERP software that is suitable for the automotive component SMI. With the cloud-based ERP, automotive component SMIs have a centralized system. To reduce implementation costs, recommended ERP software is open source. In determining the priority of cloud-based ERP software criteria using the Analytical Hierarchy Process (AHP) method. AHP is used to compare three candidates for cloud-based ERP software, namely Odoo, Idempiere and xTuple. The results obtained that Odoo ranks highest compared to two other software candidates. This shows that Odoo is considered to be able to meet the needs of automotive component SMIs for the needs of cloud-based ERP software.

scholarly journals Land Suitability Analysis of Urban Growth in Seremban Malaysia, Using GIS Based Analytical Hierarchy Process

2017 ◽  
Vol 198 ◽  
pp. 1128-1136 ◽  
Author(s):  
Maher M. Aburas ◽  
Sabrina H.O. Abdullah ◽  
Mohammad F. Ramli ◽  
Zulfa H. Asha’ari
Keyword(s):  
Urban Growth ◽  
Analytical Hierarchy Process ◽  
Land Suitability ◽  
Suitability Analysis ◽  
Land Suitability Analysis ◽  
Hierarchy Process

scholarly journals Comparison of Statistical and Analytical Hierarchy Process Methods on Flood Susceptibility Mapping: In a Case Study of the Tana Sub-basin in Northwestern Ethiopia

2021 ◽  
Author(s):  
Azemeraw Wubalem ◽  
Gashaw Tesfaw ◽  
Zerihun Dawit ◽  
Belete Getahun ◽  
Tamirat Mekuria ◽  
...  
Keyword(s):  
Analytical Hierarchy Process ◽  
Frequency Ratio ◽  
Flood Hazard ◽  
Area Under The Curve ◽  
Google Earth ◽  
Information Value ◽  
Susceptibility Map ◽  
Flood Hazards ◽  
Flood Susceptibility ◽  
Hierarchy Process

Abstract The sub-basin of Lake Tana is one of the most flood-prone areas in northwestern Ethiopia, which is affected by flood hazards. Flood susceptibility modeling in this area is essential for hazard reduction purposes. For this, the analytical hierarchy process (AHP), bivariate, and multivariate statistical methods were used. Using an intensive field survey, historical record, and Google Earth Imagery, 1404 flood locations were determined which are classified into 70% training datasets and 30% testing flood datasets using subset in the GIS tool. The statistical relationship between the probability of flood occurrence and eleven flood-driving factors is performed using the GIS tool. Then, the flood susceptibility map of the area is developed by summing all weighted factors using a raster calculator and classified into very low, low, moderate, high, and very high susceptibility classes using the natural breaks method. The results for the area under the curve (AUC) are 99.1% for the frequency ratio model is better than 86.9% using AHP, 81.4% using the logistic regression model, and 78.2% using the information value model. Based on the AUC values, the frequency ratio (FR) model is relatively better followed by the AHP model for regional flood use planning, flood hazard mitigation, and prevention purposes.

scholarly journals Efficient Identification of Corn Cultivation Area with Multitemporal Synthetic Aperture Radar and Optical Images in the Google Earth Engine Cloud Platform

2019 ◽  
Vol 11 (6) ◽  
pp. 629 ◽  
Author(s):  
Fuyou Tian ◽  
Bingfang Wu ◽  
Hongwei Zeng ◽  
Xin Zhang ◽  
Jiaming Xu
Keyword(s):  
Synthetic Aperture Radar ◽  
Large Scale ◽  
Census Data ◽  
Google Earth ◽  
Synthetic Aperture ◽  
Cloud Platform ◽  
Optical Images ◽  
Google Earth Engine ◽  
Sentinel 2 ◽  
Aperture Radar

The distribution of corn cultivation areas is crucial for ensuring food security, eradicating hunger, adjusting crop structures, and managing water resources. The emergence of high-resolution images, such as Sentinel-1 and Sentinel-2, enables the identification of corn at the field scale, and these images can be applied on a large scale with the support of cloud computing technology. Hebei Province is the major production area of corn in China, and faces serious groundwater overexploitation due to irrigation. Corn was mapped using multitemporal synthetic aperture radar (SAR) and optical images in the Google Earth Engine (GEE) cloud platform. A total of 1712 scenes of Sentinel-2 data and 206 scenes of Sentinel-1 data acquired from June to October 2017 were processed to composite image metrics as input to a random forest (RF) classifier. To avoid speckle noise in the classification results, the pixel-based classification result was integrated with the object segmentation boundary completed in eCognition software to generate an object-based corn map according to crop intensity. The results indicated that the approach using multitemporal SAR and optical images in the GEE cloud platform is reliable for corn mapping. The corn map had a high F1-Score of 90.08% and overall accuracy of 89.89% according to the test dataset, which was not involved in model training. The corn area estimated from optical and SAR images was well correlated with the census data, with an R2 = 0.91 and a root mean square error (RMSE) of 470.90 km2. The results of the corn map are expected to provide detailed information for optimizing crop structure and water management, which are critical issues in this region.

scholarly journals Evaluation of Sustainable Site Selection Using Analytical Hierarchy Process for Hospitals: Karbala City as A Case Study

2018 ◽  
Vol 7 (4.20) ◽  
pp. 347
Author(s):  
Hussein Ali Mohammed ◽  
Mohammed Neamah Ahmed ◽  
Aymen J. Alsaad
Keyword(s):  
Environmental Factors ◽  
Analytical Hierarchy Process ◽  
Site Selection ◽  
Multiple Alternative ◽  
Suitable Site ◽  
The Government ◽  
Alternative Sites ◽  
Hierarchy Process ◽  
Cost Factors

Site selection for a hospital location is one of the pivotal strategy- related decisions taken by the government. The selection of a suitable site for a hospital requires consideration of multiple alternative solutions and assessment factors. The present study aims at determine the optimum site out of three alternative sites to build a new hospital in Kerbala city. The main sustainability factors are; urban factors (including size, accessibility, restrictions, availability), environmental factors (including geomorphology, hydrology, vegetation, climate, other environmental factors) and economic factors (including service and utilities, cost) factors. The Analytical Hierarchy Process (AHP) as a multi criteria decision support system was adopted to find the weights of each factor and reach to select the most suitable site from three alternative sites. The results showed the site number (2) was the most sustainable site to construct the hospital project, where the alternative site records a biggest normal index of 0.419.  

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