A Real Time Multivariate Robust Regression Based Flood Prediction Model Using Polynomial Approximation for Wireless Sensor Network Based Flood Forecasting Systems

Seal, Victor; Raha, Arnab; Maity, Shovan; Mitra, Souvik; Mukherjee, Amitava; Naskar, Mrinal Kanti

doi:10.1007/978-3-642-27317-9_44

Cited by 6 publications

(6 citation statements)

References 7 publications

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“…(Kalayathankal & Singh, 2010) Fuzzy soft set theory Proposing a flood model based on a fuzzy method including simulation of unfamiliar relations among hydrological and meteorological parameters (Jiang et al, 2009) FCA, SFC and FSM Using the fuzzy similarity method (FSM), fuzzy comprehensive assessment (FCA) and simple fuzzy classification (SFC) in assessment of flood risk in Malaysia (Mishra et al, 2007) FP-IFTIP Improving flood diversion planning using FP-IFTIP (Wang et al, 2012) FP-IVFSP Managing the municipal solid waste by employing an interval-valued fuzzy-stochastic programing (IVFSP) methodology (Berenguer, Sempere-Torres, & Hürlimann, 2015) FL Proposing a method to predict rainfall debris flow that can be used in the framework of debris flow early warning systems at partial measure (Lin, Chen, & Peng, 2012) Fuzzy-rule-based (FRB) To develop a FRB risk assessment model for debris flows Regression (Bolshakov, 2013) LR Application of linear and symbolic regression to forecast and monitor river floods (Gartner, Cannon, & Santi, 2014) LR Using linear regression analyses for expanding two models to forecast the size of sinter deposited due to post-fire debris flow and sediment-laden flooding. (Seal et al, 2012) PR Introducing a model to be used in wireless sensor network (WSN) for forecasting floods in rivers to provide reliable and timely warnings (Yu, Chen, & Chang, 2006) SVR Real-time flood stage forecasting using SVR (Dai et al, 2011) Two-stage SVR Enhancing the analysis accuracy in optimizing the municipal solid waste management system through coupling the SVR with inexact mixed-integer linear programming (Bovis & Jakob, 1999) Multiple regression The study of the pattern of debris supply condition to forecast the activities of debris flow (Chevalier, 2013) LogR Determining debris-flow risk focusing on statistical morpho-fluvial susceptibility models and magnitude-frequency relationships Hybrid Soft computing (See & Openshaw, 1999) ANFIS Developing a new method for assessing the water level of a river and early flood warning system based on soft computing method (Kant et al, 2013) ANFIS Water level forecasting using multi-objective evolutionary neural network (MOENN) (Bazartseren, Hildebrandt, & Holz, 2003) ANFIS To compare three approaches of water level forecasting (Turan & Yurdusev, 2014) GF Reliable river flow forecasting (Wu & Chau, 2006) GF, GA, ANN Comparison between three models in flood forecasting Machine learning (Merz, Kreibich, & Lall, 2013) Data Mining Applying a three data-mining approach in flood damage analysis…”

Section: Singlementioning

confidence: 99%

Survey of computational intelligence as basis to big flood management: challenges, research directions and future work

Fotovatikhah¹,

Herrera

Shamshirband

et al. 2018

Engineering Applications of Computational Fluid Mechanics

227

137

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Flooding produces debris and waste including liquids, dead animal bodies and hazardous materials such as hospital waste. Debris causes serious threats to people's health and can even block the roads used to give emergency aid, worsening the situation. To cope with these issues, flood management systems (FMSs) are adopted for the decision-making process of critical situations. Nowadays, conventional artificial intelligence and computational intelligence (CI) methods are applied to early flood event detection, having a low false alarm rate. City authorities can then provide quick and efficient response in post-disaster scenarios. This paper aims to present a comprehensive survey about the application of CI-based methods in FMSs. CI approaches are categorized as single and hybrid methods. The paper also identifies and introduces the most promising approaches nowadays with respect to the accuracy and error rate for flood debris forecasting and management. Ensemble CI approaches are shown to be highly efficient for flood prediction.

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Section: Singlementioning

confidence: 99%

Survey of computational intelligence as basis to big flood management: challenges, research directions and future work

Fotovatikhah¹,

Herrera

Shamshirband

et al. 2018

Engineering Applications of Computational Fluid Mechanics

227

137

View full text Add to dashboard Cite

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“…The mathematical formulation of the Flood forecasting scheme is designed based on the geometrical distribution of wireless sensor nodes [14][15][16][17][18][19][20]. Fig.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: The Critical Evaluation Of the Research Study In Tablementioning

confidence: 99%

“…WSN based Statistical Model is designed to predict the extreme event of floods [17]. The authors [17,26] had proposed the WSN based scheme to forecast extreme events of flood in case of disaster circumstance. The hydrological modeling technique is used to predict floods based on numerical weather prediction [28].…”

Section: The Critical Evaluation Of the Research Study In Tablementioning

confidence: 99%

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A Flood Forecasting Model based on Wireless Sensor and Actor Networks

Bakhsh¹,

Ahmed²,

Shahzad³

et al. 2020

IJACSA

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Flood forecasting is a challenging area of research that can help to save precious lives by timely intimating about the flood possibilities. The role of advancements in computing and allied technologies has moved the research towards a new horizon. Researchers from all over the world are using Artificial Neural Networks (ANN), Global Information Systems (GIS), and Wireless Sensor Networks (WSN) based schemes for flash flood forecasting and hydrological risk analysis. ANN and GIS-based solutions are much costly whereas the analysis and prediction using WSN require much less cost for infrastructure deployment. It will enable the use of flood prediction mechanisms in the third world and poor countries. New variation in storage capacity can be a vital source to eliminate or reduce the chance of flood. By considering this observation, it is proposed to develop a generic flood prediction scheme that can manage the system as per resources and environmental conditions. A heterogeneous WSN has considered where powerful Collector Nodes (CN) continuously takes values from member sensor nodes in the region. CN transmits the alerts to Administration Unit (AU) when threshold values are crossed. It is proposed to save the threshold values from all water sources like storage capacity, water inflow, and outflow in the repository for decision making. Moreover, environmental parameters including water level, humidity, temperature, air pressure, rainfall, soil moisture, etc. are considered for the analysis of flood prediction. We have also considered the evaluation of these parameters in specified boundary values that were not considered in existing schemes. In this research study, we have used Arc GIS and NS2 simulation tools to analyze the parameters and predict the probability of the occurrence of a flood.

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“…Most of these studies focused on the use of geospatial, statistical and mixed methods in analyzing qualitative, quantitative and remotely sensed data types to examine flood vulnerability, risk, assessment of impact and flood prediction and modeling. For example, studies by Alfieri et al, (2012); Rozalis et al, (2010); Biondi et al, (2013); Seal et al, (2012) Azua et al,(2019) and Nkwunonwo (2015); Adelekan, 2010;Emmanuel, 2016;Eze, Vogel and Ibrahim, 2018;Itopa, 2018; Ikusemanran among others have done so much with regards to flood disaster. However, this particular study is different from most of the afore mentioned in terms of the approach and method of flood risk delineation adopted.…”

Section: Introductionmentioning

confidence: 99%

Delineation of Urban Flood Risk Areas Using Geospatial Technique

Yunus¹

2021

FJS

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The threat posed by urban flooding in most cities of the world is becoming alarming especially within the recent decades. This makes it necessary to Identify and delineate flood risk areas within cities in order to curb it menace. This study employs geospatial technique to delineate flood risk areas within Kano metropolis with a view to mitigating its impact on lives and properties. Digital Elevation Model (ASTER DEM 30m) was used to derive excess surface run-off attributes including flow direction and accumulation. Based on these attributes, flood risk areas were determined and delineated using buffer distances of 500 meters. World View image (30 cm spatial resolution) was used to identify the landuses at risk. The result from the analysis delineated flood risk areas at varying exposure levels (i.e high, moderate and low).It was evident that flood risk level within the metropolis corresponds to the pattern of surface run-off flow accumulation areas. Settlements and farmlands found within high accumulation areas along the floodplains of River Jakara (in the North and North-eastern part) and Kano-Zaria road (southern part) are at higher risk than those found on low accumulation areas. The study concluded that excess surface run-off flow direction and accumulation are among the fundamental factors determining the risk to urban flooding. The study recommends that with the ongoing level of urban development and impervious surface expansion, urban planners and policy makers should make use of the flow direction and accumulation maps in determining safer places for future developments.

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A Real Time Multivariate Robust Regression Based Flood Prediction Model Using Polynomial Approximation for Wireless Sensor Network Based Flood Forecasting Systems

Cited by 6 publications

References 7 publications

Survey of computational intelligence as basis to big flood management: challenges, research directions and future work

Survey of computational intelligence as basis to big flood management: challenges, research directions and future work

A Flood Forecasting Model based on Wireless Sensor and Actor Networks

Delineation of Urban Flood Risk Areas Using Geospatial Technique

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