Estimation of flood land use/land cover mapping by regional modelling of flood hazard at sub-basin level case study: Marand basin

Mousavi, Seid Mohamad; Roostaei, Shahram; Rostamzadeh, Hashem

doi:10.1080/19475705.2018.1549112

Cited by 40 publications

(23 citation statements)

References 18 publications

(18 reference statements)

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“…Urban catchments have very low permeability and high rainfall-runoff conversion rates. Furthermore, micro-topography effects can cause localized drainage system failures (Mousavi et al 2019). Masoudian and Theobald (2011) studied the effect of topography on flood parameters, such as maximum flood discharge and time to peak.…”

Section: Selection Of Criteria and Risk Factorsmentioning

confidence: 99%

Assessing urban flood disaster risk using Bayesian network model and GIS applications

Shen

Wang

et al. 2019

Geomatics, Natural Hazards and Risk

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With global climate change, cities face the challenge of increasing flood disaster caused by heavy rainfall, and the prediction and assessment of flood disaster risk is a crucial step towards risk mitigation and adaptation planning. In this study, a method combining Bayesian network (BN) model and geographic information system (GIS), which can capture the potential relationships between factors impacting flood disaster and has capacity of quantifying uncertainty and utilizing both data and knowledgebased sources, was proposed to assess flood disaster risk. The proposed methodology was applied in a case study to assess flood disaster risk and to diagnose the reason for flood disaster in Zhengzhou City, and the results were validated by comparing with actual situation. The results show that that the relative error of very-low, low, moderate, high and very-high risk predicted by the proposed model is 12.57%, 13.21%, 2.23%, 19.63% and 21.65%, respectively, which demonstrates the discriminative power of the established model. Based on the spatial distribution of different risk levels, it can be recognized that the flood disaster risk in Zhengzhou City is decreasing from the middle to the surroundings. The results provide some basis for the field control and management of urban flood disaster.

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Section: Selection Of Criteria and Risk Factorsmentioning

confidence: 99%

Assessing urban flood disaster risk using Bayesian network model and GIS applications

Shen

Wang

et al. 2019

Geomatics, Natural Hazards and Risk

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“…SITS have been employed to deal with several tasks in multiple domains ranging from ecology [2], agriculture [3], land management planning [4], and forest and natural habitat monitoring [5,6]. Among these fields, Land Use/Land cover (LULC) mapping has received large attention in the last years [7][8][9][10], since it provides essential components on which further indicators can be built on [11]. As example, an accurate mapping of croplands and crop types is the cornerstone of agricultural monitoring systems, as it allows providing information on food production for developing countries or global market.…”

Section: Introductionmentioning

confidence: 99%

Object-Based Multi-Temporal and Multi-Source Land Cover Mapping Leveraging Hierarchical Class Relationships

et al. 2020

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European satellite missions Sentinel-1 (S1) and Sentinel-2 (S2) provide at high spatial resolution and high revisit time, respectively, radar and optical images that support a wide range of Earth surface monitoring tasks, such as Land Use/Land Cover mapping. A long-standing challenge in the remote sensing community is about how to efficiently exploit multiple sources of information and leverage their complementarity, in order to obtain the most out of radar and optical data. In this work, we propose to deal with land cover mapping in an object-based image analysis (OBIA) setting via a deep learning framework designed to leverage the multi-source complementarity provided by radar and optical satellite image time series (SITS). The proposed architecture is based on an extension of Recurrent Neural Network (RNN) enriched via a modified attention mechanism capable to fit the specificity of SITS data. Our framework also integrates a pretraining strategy that allows to exploit specific domain knowledge, shaped as hierarchy over the set of land cover classes, to guide the model training. Thorough experimental evaluations, involving several competitive approaches were conducted on two study sites, namely the Reunion island and a part of the Senegalese groundnut basin. Classification results, 79% of global accuracy on the Reunion island and 90% on the Senegalese site, respectively, have demonstrated the suitability of the proposal.

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“…. The Dempster-Shafer theory of evidence has been introduced by Dempster [106], and the EBF method has been applied to other natural hazards such as flooding [107] and landslide [108]. Its relevance in natural hazard modeling is that it can accept uncertainty and can integrate information from multiple sources of evidence [109].…”

Section: Evidential Belief Function (Ebf)mentioning

confidence: 99%

Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping

et al. 2021

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The survival of humanity is dependent on the survival of forests and the ecosystems they support, yet annually wildfires destroy millions of hectares of global forestry. Wildfires take place under specific conditions and in certain regions, which can be studied through appropriate techniques. A variety of statistical modeling methods have been assessed by researchers; however, ensemble modeling of wildfire susceptibility has not been undertaken. We hypothesize that ensemble modeling of wildfire susceptibility is better than a single modeling technique. This study models the occurrence of wildfire in the Brisbane Catchment of Australia, which is an annual event, using the index of entropy (IoE), evidential belief function (EBF), and logistic regression (LR) ensemble techniques. As a secondary goal of this research, the spatial distribution of the wildfire risk from different aspects such as urbanization and ecosystem was evaluated. The highest accuracy (88.51%) was achieved using the ensemble EBF and LR model. The outcomes of this study may be helpful to particular groups such as planners to avoid susceptible and risky regions in their planning; model builders to replace the traditional individual methods with ensemble algorithms; and geospatial users to enhance their knowledge of geographic information system (GIS) applications.

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Estimation of flood land use/land cover mapping by regional modelling of flood hazard at sub-basin level case study: Marand basin

Cited by 40 publications

References 18 publications

Assessing urban flood disaster risk using Bayesian network model and GIS applications

Assessing urban flood disaster risk using Bayesian network model and GIS applications

Object-Based Multi-Temporal and Multi-Source Land Cover Mapping Leveraging Hierarchical Class Relationships

Application of an Ensemble Statistical Approach in Spatial Predictions of Bushfire Probability and Risk Mapping

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