Multi-criteria decision-making using GIS-AHP for air pollution problem in Igdir Province/Turkey

Sahin, F.; Kara, Mehmet Kazım; Koç, Ahmet; Şahin, Gökhan

doi:10.1007/s11356-020-09710-3

Cited by 27 publications

(16 citation statements)

References 36 publications

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“…Although there is no significant correlation between wind speed and PM 10 , this parameter has meteorological influence on the dispersion, resuspension, and horizontal transport of pollutants, provided that there are strong air currents (winds) 51,52 , which is not the case of the present study because the highest frequencies of wind speeds are between 0 -3.10 m/s 30 .…”

Section: Influence Of Wind Direction and Speed On Pm 10 Concentrationsmentioning

confidence: 58%

“…On the other hand, stratiform cloudiness increases in winter, as does relative humidity, that accompanied by drizzles in that season, help to significantly decrease the temperature and PM 10 concentrations due to wet deposition typical of the season 27 . The above explains the high negative correlation observed between temperature and relative humidity in the five monitoring stations (see Figure 7), which is a normal phenomenon because the relative humidity directly depends on temperature and pressure to determine the capacity of the air in the intake of water vapor 51 . For this reason, the higher the temperature, the lower the relative humidity, as shown in Figure 8.…”

Section: Analysis Of the Correlations With The Meteorological Variablesmentioning

confidence: 78%

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Air Quality Assessment and Pollution Forecasting using Recurrent Artificial Neural Networks in Metropolitan Lima-Peru

Cordova

Portocarrero

Salas

et al. 2021

Preprint

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The prediction of air pollution is of great importance in highly populated areas because it has a direct impact on both the management of the city's economic activity and the health of its inhabitants. In this work, the spatio-temporal behavior of air quality in Metropolitan Lima was evaluated and predicted using the recurrent artificial neural network known as Long-Short Term Memory networks (LSTM). The LSTM was implemented for the hourly prediction of PM10 based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The model was evaluated under two validation schemes: the hold-out (HO) and the blocked-nested cross-validation (BNCV). The simulation results show that periods of low PM10 concentration are predicted with high precision. Whereas, for periods of high contamination, the LSTM network with BNCV has better predictability performance. In conclusion, recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better performance to forecast this type of environmental data, and can also be extrapolated to other pollutants.

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Section: Influence Of Wind Direction and Speed On Pm 10 Concentrationsmentioning

confidence: 58%

Section: Analysis Of the Correlations With The Meteorological Variablesmentioning

confidence: 78%

Air Quality Assessment and Pollution Forecasting using Recurrent Artificial Neural Networks in Metropolitan Lima-Peru

Cordova

Portocarrero

Salas

et al. 2021

Preprint

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“…(C5) Wind speed is one of the most significant aspects to reduce the temperature of PVs for solar power plants. Determining the direction of the wind is also essential to adjust solar panels with a right angle 33,51,52 . The average wind speed map of Erzurum is demonstrated in Fig.…”

Section: Problem Description the Materials Of This Study Area Constitmentioning

confidence: 99%

“…Rain formation happens in this way. The higher the relative humidity, the more likely it is to rain 51 .…”

Section: Problem Description the Materials Of This Study Area Constitmentioning

confidence: 99%

Multi-criteria of PV solar site selection problem using GIS-intuitionistic fuzzy based approach in Erzurum province/Turkey

Türk

Koç

Şahin

2021

Sci Rep

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Renewable energy sources have been placed as the key to facilitating to provide source of electricity generation. Solar power is one of the most preferable one among renewable energy sources due to the easy to generate in suitable environment. However, there are concerns with the location of solar power plants installation which causes low efficiency and ineffective use. Hence, determining the location for the usage of solar power sources is critical to mitigating those concerns. In addition, Turkey has been focused on investments on sustainable renewable energy sources and there are few studies which work on cities to reveal potential sources. In this study, GIS and intuitionistic fuzzy set based multi-criteria decision-making method is proposed for determining the most suitable areas for solar energy power plant potential site selection in Erzurum province, Turkey. Firstly, a solar energy power plant potential site selection map is made using a GIS program along with considering ecological risks and ecological criteria. Secondly, 20 districts of Erzurum are investigated in terms of 10 criteria (slope, aspect, solar irradiation, land use, wind speed, air temperature, air pressure, air humidity, land surface temperature and transmission line) using intuitionistic fuzzy sets. In these parameters the first time we looked the land use surface. The land use surface is affected the efficiency of the solar power plant. Finally, the comparisons of two methods are done to check consistency of results obtained. The results indicate that both approaches achieve same areas as the most suitable locations for solar power plants installations for Erzurum province in Turkey. The aim of this work is first to investigate possible locations for solar power plant installation using a mapping method, GIS, and then, Intuitionistic Fuzzy is applied to the problem to obtain optimum areas for solar energy. Also, more accurate results are provided comparing results of two methods, GIS and Intuitionistic Fuzzy. The results expose that 25,065.3 km2 for solar power plant suitable for solar power plan installation.

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“…e results showed the highest potential tourists area based on the natural environmental features. With the same approach, Sahin et al [32] evaluated air pollution problems in Turkey. Alam [33] combined AHP and GIS to select the optimal accommodation for living in Chittagong City, Bangladesh.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling the Accommodation Preferences of Tourists by Combining Fuzzy-AHP and GIS Methods

Ali

Esztergár-Kiss

2021

Journal of Advanced Transportation

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Finding the place of accommodation is one of the most crucial issues during a journey. This study aims to support the decision-making of tourists for choosing the optimal accommodation by combining fuzzy analytic hierarchy process (FAHP) and geographic information system (GIS) techniques. The adopted criteria are the cost per room, the distance from the center, the level of security, the place rating, and the availability of free cancellation and breakfast. Due to some uncertainty and diversity of criteria, the FAHP approach is applied to consolidate tourists’ decisions by applying criteria weighting, while the GIS is used to overlay the weighted criteria and to visualize the ranked places of accommodation on a map. The combined technique is applied on a case study in Budapest City, where the analysis is conducted on 364 places of accommodation. The results show that half of the places are recommended for tourists, and more than fifth of the accommodations are highly recommended. Furthermore, it can be concluded that the cost per room was the highest influential criterion with 0.233 importance weight, followed by the security level with 0.205. The lowest factor affecting the choice of accommodation was the free cancellation service. It was demonstrated that the rating weight importance was 0.182, while the breakfast and the distance from the center had approximately the same importance. As a recommendation, some improvements on the accommodation, such as decreasing the cost per room, enhancing the services, or developing the quality of the places, would increase their attractiveness for tourists.

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Multi-criteria decision-making using GIS-AHP for air pollution problem in Igdir Province/Turkey

Cited by 27 publications

References 36 publications

Air Quality Assessment and Pollution Forecasting using Recurrent Artificial Neural Networks in Metropolitan Lima-Peru

Air Quality Assessment and Pollution Forecasting using Recurrent Artificial Neural Networks in Metropolitan Lima-Peru

Multi-criteria of PV solar site selection problem using GIS-intuitionistic fuzzy based approach in Erzurum province/Turkey

Modelling the Accommodation Preferences of Tourists by Combining Fuzzy-AHP and GIS Methods

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