Physical and Anthropogenic Factors Related to Landslide Activity in the Northern Peloponnese, Greece

Skilodimou, Hariklia D.; Bathrellos, George D.; Koskeridou, Efterpi; Soukis, Konstantinos; Ρόζος, Δ.

doi:10.3390/land7030085

Cited by 91 publications

(48 citation statements)

References 39 publications

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“…The statistical analysis proved that October contained the most flood events (42). High values of flood occurrences were observed in March (35), in June (11) and in November (10). In October, the number of flood events was four-times higher than in June and November.…”

Section: Temporal Distribution Of Flood Eventsmentioning

confidence: 86%

“…October was the month with the most records (34) in this type of damage ( Figure 9). Additionally, high values were observed in March (12) and June (11). The maximum value (10) of the property damages was recorded in 2005 ( Figure 8).…”

Section: Temporal Distribution Of Damagesmentioning

confidence: 92%

“…These areas are located in the central, southern, south-eastern and coastal part of the study area and create favorable conditions for flooding. The proposed method estimates the localization of sites prone to flood, and it may be used for flood hazard assessment mapping and for flood risk management.Keywords: historic flood data; old topographic maps; GIS; temporal and spatial distribution of flood events; marshy areas and lakes; flood hazard assessment Land 2018, 7, 106 2 of 18 capable of changing landforms and triggering natural hazards, which in some cases control human activities [1][2][3][4][5][6][7][8][9][10][11].Floods are physical phenomena active in geological time and the result of excess runoff. When rivers overtop their banks, the excess water goes to the floodplain.…”

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confidence: 99%

“…Keywords: historic flood data; old topographic maps; GIS; temporal and spatial distribution of flood events; marshy areas and lakes; flood hazard assessment Land 2018, 7, 106 2 of 18 capable of changing landforms and triggering natural hazards, which in some cases control human activities [1][2][3][4][5][6][7][8][9][10][11].…”

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confidence: 99%

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Temporal and Spatial Analysis of Flood Occurrences in the Drainage Basin of Pinios River (Thessaly, Central Greece)

Bathrellos

Skilodimou

Soukis

et al. 2018

Land

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Historic data and old topographic maps include information on historical floods and paleo-floods. This paper aims at identifying the flood hazard by using historic data in the drainage basin of Pinios (Peneus) River, in Thessaly, central Greece. For this purpose, a catalogue of historical flood events that occurred between 1979 and 2010 and old topographic maps of 1881 were used. Moreover, geomorphic parameters such as elevation, slope, aspect and slope curvature were taken into account. The data were combined with the Geographical Information System to analyze the temporal and spatial distribution of flood events. The results show that a total number of 146 flood events were recorded in the study area. The number of flood events reaches its maximum value in the year 1994, while October contains the most flood events. The flood occurrences increased during the period 1990–2010. The flooded area reaches its maximum value in the year 1987, and November is the month with the most records. The type of damages with the most records is for rural land use. Regarding the class of damages, no human casualties were recorded during the studied period. The annual and monthly distribution of the very high category reaches the maximum values, respectively, in the year 2005 and in June. The analysis of the spatial distribution of the floods proves that most of the occurrences are recorded in the southern part of the study area. There is a certain amount of clustering of flood events in the areas of former marshes and lakes along with the lowest and flattest parts of the study area. These areas are located in the central, southern, south-eastern and coastal part of the study area and create favorable conditions for flooding. The proposed method estimates the localization of sites prone to flood, and it may be used for flood hazard assessment mapping and for flood risk management.

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Section: Temporal Distribution Of Flood Eventsmentioning

confidence: 86%

Section: Temporal Distribution Of Damagesmentioning

confidence: 92%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Temporal and Spatial Analysis of Flood Occurrences in the Drainage Basin of Pinios River (Thessaly, Central Greece)

Bathrellos

Skilodimou

Soukis

et al. 2018

Land

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“…It is an essential step in coordinating urban development and rational utilization of land resources to analyze the driving factors of land use changes in an area [3]. Through the dynamic landscape changes reflected by satellite images, policymakers, land planners, and investors or others can make decisions on further actions according to the development characteristics and trends of the region [4][5][6][7]. From the perspectives of multidisciplinary and nature, it will also support the protection of coastal urban environment and biodiversity, and the impacts of the control and reduction of the disasters on the local area.…”

Section: Introductionmentioning

confidence: 99%

Satellite Image-Based Methods of Spatiotemporal Analysis on Sustainable Urban Land Use Change and the Driving Factors: A Case Study in Caofeidian and the Suburbs, China

et al. 2019

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As a typical rapid-development seaport area in coastal cities, such as Caofeidian, the study on the spatiotemporal changes of urban land use and its surrounding rural areas is valuable and significant in reference to the future urban planning and land policies in similar coastal areas of China or other countries. Based on satellite images, this research processes images in different years for summarizing the changes of vegetation, urban areas, and water areas in Caofeidian and the suburbs. This research aims to summarize the experience of the coastal city in the process of sustainable development by analyzing the dynamic trends and driving factors of land use spatial and temporal changes in the target area so that it provides a reference for the long-term development of the city. Meanwhile, it also hopes to give support for refining and improving the spatiotemporal analysis method for sustainable urban land use through the experiment. Due to the appearance of the results of the abnormal data, in the experiment process, this article adopts a comparative experiment to avoid the error of the analysis result and to find out the reason. The results show that the urban area for construction increased rapidly in the past twenty years, which is mainly affected by factors, such as economic development, policy guidance, environmental awareness, and environmental protection measures, especially guided by policies. Thus, coastal cities should stretch the planning of sustainable development from the three aspects combining with local characteristics. Besides, phenological phenomena and crops harvest time tremendously affect the images and calculation. The selection of remotely-sensed images should fully consider the characteristics of urban and rural locations, especially the impact of local phenological phenomena. The results of the analysis provide reference value and support for sustainable urban land management and development in the study area and other coastal cities.

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Landslide risk assessment and management using hybrid machine learning‐based empirical models

Yang,

Jiang,

Arabameri

et al. 2023

Geological Journal

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Landslides are a prevalent geologic phenomenon that substantially threatens human life and infrastructure, resulting in considerable loss and destruction. The practice of landslide susceptibility mapping is crucial for the mitigation of risks connected with this natural disaster. This work aims at investigating the influence of varying sample sizes on the precision of landslide susceptibility modelling using a case study conducted in the Alamout basin, Iran. The researchers used a machine learning methodology based on tree algorithms to construct a model for predicting the likelihood of landslides. Additionally, they adopted a multi‐scenario strategy to address the inherent uncertainty associated with the input data. The integration of the naive Bayes tree (NBTree), random forest (RF), logistic model tree (LMT) and J48 algorithms was performed. The modelling process included using 20 predictive parameters across four distinct scenarios. Four models, labelled S1, S2, S3 and S4, were used in this study. These models utilized 25%, 50%, 75% and 100% of the available inventory data. The research presented in this study is distinguished by using a tree‐based methodology for landslide susceptibility modelling and incorporating a multi‐scenario strategy to address the inherent uncertainty associated with the input data. The findings indicated that the augmentation of the sample size improved the precision of the models. The efficacy of using a multi‐scenario strategy in enhancing the dependability of the model is also underscored. Among the 20 input elements used in the modelling process, it was seen that slope angle accounted for the highest relative significance, constituting 25.60% of the overall influence. Following more closely, distance to fault contributed significantly, with a relative importance of 23.40%. Additionally, rainfall and elevation exhibited notable contributions, with relative volumes of 7.91% and 5.50%, respectively. All four landslide models showed adequate learning and forecasting ability throughout the training and testing phases. During the testing phase, the true skill score (TSS) values exhibited a range of 0.631–0.804, while the area under the receiver operating characteristic curve values showed a range of 0.745–0.921. The susceptibility maps indicated that a significant portion of the region exhibits moderate to very high susceptibility zones, with the northern and eastern sectors displaying greater landslide values than the western region. The model's performance showed improvement from S1 to S4 in both the training and testing phases. The performance of the models exhibited the following trend: in scenario 1, the RF model outperformed the J48, LMT and NBTree models; in scenario 2, the RF model surpassed the NBTree and LMT models, while being on par with the J48 model; in scenarios 3 and 4, the RF model showed superior performance compared to the NBTree, J48 and LMT models. Therefore, the RF model proved to be the most effective among the models evaluated. The findings derived from this research have the potential to serve as valuable references for the purposes of land‐use planning and catastrophe risk management.

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Physical and Anthropogenic Factors Related to Landslide Activity in the Northern Peloponnese, Greece

Cited by 91 publications

References 39 publications

Temporal and Spatial Analysis of Flood Occurrences in the Drainage Basin of Pinios River (Thessaly, Central Greece)

Temporal and Spatial Analysis of Flood Occurrences in the Drainage Basin of Pinios River (Thessaly, Central Greece)

Satellite Image-Based Methods of Spatiotemporal Analysis on Sustainable Urban Land Use Change and the Driving Factors: A Case Study in Caofeidian and the Suburbs, China

Landslide risk assessment and management using hybrid machine learning‐based empirical models

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