Improving real time flood forecasting using fuzzy inference system

Lohani, A. K.; Goel, N. K.; Bhatia, Karamjit

doi:10.1016/j.jhydrol.2013.11.021

Cited by 180 publications

(65 citation statements)

References 55 publications

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“…The most popular approaches in natural hazard modelling are frequency ratio (FR) Lee et al 2012;Tehrany et al 2015), analytical hierarchy process (AHP) (Yalcin 2008;Stefanidis & Stathis 2013;Papaioannou et al 2015), fuzzy logic (Pradhan 2011;Perera and Lahat 2014), logistic regression (LR) (Pradhan 2010;Tehrany et al 2014a), artificial neural networks (ANN) (Varoonchotikul 2003;Kia et al 2012;Lohani et al 2014) and weights-of-evidence (WoE) (Dahal et al 2008;Tehrany et al 2014c).…”

Section: Introductionmentioning

confidence: 99%

Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis

Rahmati

Zeinivand

Besharat

2015

Geomatics, Natural Hazards and Risk

340

136

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Flood is considered to be the most common natural disaster worldwide during the last decades. Flood hazard potential mapping is required for management and mitigation of flood. The present research was aimed to assess the efficiency of analytical hierarchical process (AHP) to identify potential flood hazard zones by comparing with the results of a hydraulic model. Initially, four parameters via distance to river, land use, elevation and land slope were used in some part of the Yasooj River, Iran. In order to determine the weight of each effective factor, questionnaires of comparison ratings on the Saaty's scale were prepared and distributed to eight experts. The normalized weights of criteria/parameters were determined based on Saaty's nine-point scale and its importance in specifying flood hazard potential zones using the AHP and eigenvector methods. The set of criteria were integrated by weighted linear combination method using ArcGIS 10.2 software to generate flood hazard prediction map. The inundation simulation (extent and depth of flood) was conducted using hydrodynamic program HEC-RAS for 50-and 100-year interval floods. The validation of the flood hazard prediction map was conducted based on flood extent and depth maps. The results showed that the AHP technique is promising of making accurate and reliable prediction for flood extent. Therefore, the AHP and geographic information system (GIS) techniques are suggested for assessment of the flood hazard potential, specifically in no-data regions.

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Section: Introductionmentioning

confidence: 99%

Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis

Rahmati

Zeinivand

Besharat

2015

Geomatics, Natural Hazards and Risk

340

136

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“…Scientific literature exemplifies the application of fuzzy logic in flood risk management with distinct purposes: e.g., real-time forecasting by modeling the rainfall-runoff relationship [23], flood-diversion planning [24], modeling the participation of multi-stakeholders in flood risk management decision-making processes [25], flood risk evaluation and flood risk response measures [26,27].…”

Section: Contextualizationmentioning

confidence: 99%

Basin Flood Risk Management: A Territorial Data-Driven Approach to Support Decision-Making

Santos

Tavares

2015

Water

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This paper explores the applicability of flood impact databases in the flood risk governance process. This study begins with a twofold analysis of three hydrographical basins: one analysis based on the data of a recently constructed flood-impact database for Portugal and another based on selected socioeconomic and biophysical variables that characterize the basins' territorial context. From these sets of data, two fuzzy inference systems are assembled: one for the resource criteria and another for the time criteria. When plotted, the fuzzy analysis results are associated with distinct flood risk management strategies: operational and strategic, hard and soft measure-based. The three basins differ substantially in terms of flood-impact characteristics, with impacts being distinguished in terms of human and material consequences. Socioeconomic factors seem to be more explicative of flood impacts than the biophysical contexts that generate floods. The fuzzy logic analysis suggested priorities of action: early warning and information for one of the basins (Mondego) and a less operational solution, combining structural mitigation and land-use planning, for the other two basins (Lis and Vouga). Considering the current implementation of the Floods Directive, design of flood risk maps and flood risk management plans can benefit from the integration of the presented methodology.

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“…Data-driven approach analyse the data without assuming any underlying mechanism between the variables. In recent years, data-driven techniques have achieved significant importance due to their learning ability during training stage and have been successfully applied in multiple disciplines including water resources engineering [1,[7][8][9][10][11]. Perhaps, artificial neural network is one of the most popular data-driven techniques which has been broadly used in modelling hydrological variables.…”

Section: Data-driven Modellingmentioning

confidence: 99%

Data-driven modelling for pore water pressure variation responses to rainfall

Mustafa¹,

Isa²,

Rezaur³

et al. 2015

WIT Transactions on the Built Environment

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Measurement of soil pore water pressure is always a tedious, time consuming and expensive exercise. Moreover, unavailability of any physical based or mathematical relationship to get information of pore water pressure leads researchers to perform data-driven modelling. This study presents a data-driven modelling approach to predict soil pore water pressure variations in a slope. Point measurements based time series data of soil pore water pressure variations and corresponding rainfall was used to develop the data-driven model. The model was developed using radial basis function neural network with Multi-quadric basis function. The inputs of the model consist of 5 antecedent pore water pressure, two antecedent rainfall and one current rainfall values. Trial and error procedure was adopted to obtain the appropriate number of neurons in the hidden layer. Normalization method was used to determine the spread of the basis function. Mean absolute error (MAE) and coefficient of determination (R 2 ) as statistical measures were used to evaluate the performance of the model. The results revealed that the data-driven model predicted the pore water pressure values close to the observed values. The minimum value of MAE during test stage was observed as 0.327 with a coefficient of determination R 2 = 0.975. Multi-quadric basis function was found to be suitable for the prediction of soil pore water pressure variations.

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Improving real time flood forecasting using fuzzy inference system

Cited by 180 publications

References 55 publications

Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis

Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis

Basin Flood Risk Management: A Territorial Data-Driven Approach to Support Decision-Making

Data-driven modelling for pore water pressure variation responses to rainfall

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