Application of the coupled TOPSIS–Mahalanobis distance for multi-hazard-based management of the target districts of the Golestan Province, Iran

Sheikh, Vahedberdi; Kornejady, Aiding; Ownegh, Majid

doi:10.1007/s11069-019-03617-0

Cited by 33 publications

(17 citation statements)

References 71 publications

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“…After the flood, six towns and about 70 villages along the river were affected. Also, rainfall reached 500 mm at some stations reached, which means more than 75% of annual rainfall occurred in 24 h (Sheikh et al, 2019). The depth of the water reached over 50 cm both in and outside of the city, the landslide caused rock falling from the mountain and block some of the roads.…”

Section: Methodsmentioning

confidence: 99%

Development of a method for flood detection based on Sentinel‐1 images and classifier algorithms

Sharifi

2021

Water & Environment J

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Floods are one of the most devastating natural disasters in the world, displacing millions of people each year and causing severe damage to people's lives and infrastructure. It is the most widespread hydrological hazard worldwide that affects water management, nature protection, economic activities, hydromorphological alterations on ecosystem services and human health. Real‐time monitoring systems play a key role in flood risk reduction and disaster response decisions. Studies have shown that using earth observation data for flood monitoring and timely actions based on good quality information reduces damages. In this paper, after thresholding, a machine learning algorithm and an object‐based classification method were used to classify the SAR data. Thresholding helps detect regions in the flooded areas. A comparison of the results showed that the machine learning algorithm obtained significant results. Because of the results obtained, the usefulness of Sentinel‐1 images as a baseline data for the improvement of the methodological guide is appreciated and should be used as a new source to monitor the flood risks.

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Section: Methodsmentioning

confidence: 99%

Development of a method for flood detection based on Sentinel‐1 images and classifier algorithms

Sharifi

2021

Water & Environment J

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“…The mean annual temperature, relative humidity, and precipitation are 14.16°C, 68.19%, and 597.13 mm, respectively. Despite considerable forest areas and relatively high precipitation, this watershed experiences frequent flooding events and a high rate of land degradation and sediment load (Sheikh et al, 2019). It is believed that land use planning and optimization can alleviate these threats.…”

Section: Methodsmentioning

confidence: 99%

Land use optimization through bridging multiobjective optimization and multicriteria decision‐making models (case study: Tilabad Watershed, Golestan Province, Iran)

Sheikh

Salmani

Mahiny

et al. 2021

Natural Resource Modeling

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This study aims to present an efficient methodology for land use optimization based on minimization of runoff and sediment and maximization of economic benefits, occupational opportunities, and land use suitability in the Tilabad watershed in northeast of Iran. The land use map of the area was prepared using the Landsat satellite images and field surveys. The amounts of runoff and sediment were estimated via SWAT model. The TOPSIS multicriteria decision‐making (MCDM) approach was applied on the results of the multiobjective optimization (MOO) based on non‐dominated sorting genetic algorithm II (NSGA II) to choose the final optimal solution among the Pareto solutions front generated by MOO. The results indicated that the area of agriculture and rangelands should decrease, and the area of forests should increase to achieve the defined objectives. Overall, results indicated that integration of MOO and MCDM provides an efficient procedure for land use optimization in a complex watershed. Recommendations for Resource Managers The optimization of land use allocation across a complex watershed requires the combined application of several models and techniques to achieve a sustainable decision‐making process. By optimization of land use patterns according to the final solution, the surface runoff and sediment load of the watershed will decline, while the economic profit and land use suitability will improve. Despite using a maximization objective function, the land‐based job opportunities might decrease across a watershed by optimization of land use allocation, but this can be considered as an opportunity for provision of manpower to other socioeconomic sectors.

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“…It worked with similarity indexes of hazard maps and showed multi-hazard assessment. In Sheikh et al [47], TOPSIS-Mahalanobis distance, TOPSIS, and simple additive weight (SAW) methods were combined. Although the TOPSIS method was criticized for using only a geometric distance, it was mentioned that it gives more clear results for natural hazards.…”

Section: Background On Multi-hazard Assessmentmentioning

confidence: 99%

Use of Mamdani Fuzzy Algorithm for Multi-Hazard Susceptibility Assessment in a Developing Urban Settlement (Mamak, Ankara, Turkey)

Yanar

Kocaman

Gökçeoğlu

2020

IJGI

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Urban areas may be affected by multiple hazards, and integrated hazard susceptibility maps are needed for suitable site selection and planning. Furthermore, geological–geotechnical parameters, construction costs, and the spatial distribution of existing infrastructure should be taken into account for this purpose. Up-to-date land-use and land-cover (LULC) maps, as well as natural hazard susceptibility maps, can be frequently obtained from high-resolution satellite sensors. In this study, an integrated hazard susceptibility assessment was performed for a developing urban settlement (Mamak District of Ankara City, Turkey) considering landslide and flood potential. The flood susceptibility map of Ankara City was produced in a previous study using modified analytical hierarchical process (M-AHP) approach. The landslide susceptibility map was produced using the logistic regression technique in this study. Sentinel-2 images were employed for generating LULC data with the random forest classification method. Topographical derivatives obtained from a high-resolution digital elevation model and lithological parameters were employed for the production of landslide susceptibility maps. For the integrated hazard susceptibility assessment, the Mamdani fuzzy algorithm was considered, and the results are discussed in the present study. The results demonstrate that multi-hazard susceptibility assessment maps for urban planning can be obtained by combining a set of expert-based and ensemble learning methods.

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Application of the coupled TOPSIS–Mahalanobis distance for multi-hazard-based management of the target districts of the Golestan Province, Iran

Cited by 33 publications

References 71 publications

Development of a method for flood detection based on Sentinel‐1 images and classifier algorithms

Development of a method for flood detection based on Sentinel‐1 images and classifier algorithms

Land use optimization through bridging multiobjective optimization and multicriteria decision‐making models (case study: Tilabad Watershed, Golestan Province, Iran)

Use of Mamdani Fuzzy Algorithm for Multi-Hazard Susceptibility Assessment in a Developing Urban Settlement (Mamak, Ankara, Turkey)

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