Regression-based Daugava River Flood Forecasting and Monitoring

Filho

Bull. of Atmos. Sci.& Technol.

et al. 2021

The real-time warning for flash floods represents one of the major challenges for monitoring centers and civil defenses due to the short time frame in which such phenomena occur. That situation often takes place particularly in mountainous regions where high slope rate favors an increase in the velocity of the water flow and it might generate conditions to impact the water level due to potential for provoking erosion, landslides, and debris flow. An initial one new and unique prototype of hydrometeorological relationship for real-time, site-tunable, water level for flash flood prediction is presented. Time variations of the reflectivity measured by horizontal polarization radar are evaluated and correlated to generate the local estimate of the elevation of the Quitandinha River water level for the next hour. The proposal of this relationship is not intended to act as a simplified hydraulic model but to characterize if a critical elevation of the water level could be obtained from the radar reflectivity data and consequently whether the flash flooding event should be expected. We verified that the proposed relationship that tended to underestimate the water level peaks, however, qualitatively was initially able to indicate if a flood event could occur or not in the next hour. The expectation related to this initial relationship is to improve the obtained results in order to be used as a tool for decisionmaking in relation to the issuance of warnings delivered by environmental monitoring centers.

Section: Hydrometeorological Empirical Relationshipsmentioning

confidence: 99%

Real-time river level estimation based on variations of radar reflectivity—a case study of the Quitandinha River watershed, Petrópolis, Rio de Janeiro (Brazil)

Filho

Bull. of Atmos. Sci.& Technol.

et al. 2021

Engineering Applications of Computational Fluid Mechanics

“…(Kisi et al, 2012) VFS and IDM Establishing a fuzzy method for predicting the risk of flood using unfinished data sets using a compound method based on VFS and IDM (Ahmad & Simonovic, 2011) 3D FS Proposing a method to manage the risk of flood which can take uncertainty done by spatial and temporal variability and ambiguity into account (continued). (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…”

Section: Singlementioning

confidence: 99%

“…There are several researchers developing an overall objects classification but only a few concentrate on classifying flood waste. The most promising approaches among those include methods such as decision trees (Ahmad & Simonovic, 2006;Wei, 2012) and regression (Belayneh, Adamowski, Khalil, & Ozga-Zielinski, 2014;Bolshakov, 2013;Dai, Li, & Huang, 2011;Lin, Wang, & Chen, 2016). However, most of this work suffers from low accuracy (Yaseen, El-Shafie, Jaafar, Afan, & Sayl, 2015).…”

Section: Introductionmentioning

confidence: 99%

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

Fotovatikhah¹,

Herrera

Shamshirband

et al. 2018

239

139

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.

“…(). Specific parameters affecting the probability of flood occurrence have been studied extensively (Bolshakov, ; Merkuryeva et al., ; Shrestha et al., ). Other related references are given in Manna ().…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Modeling of Flood Hazard and its Risk Assessment for Eastern Region of India

et al. 2019

This article models flood occurrence probabilistically and its risk assessment. It incorporates atmospheric parameters to forecast rainfall in an area. This measure of precipitation, together with river and ground parameters, serve as parameters in the model to predict runoff and subsequently inundation depth of an area. The inundation depth acts as a guide for predicting flood proneness and associated hazard. The vulnerability owing to flood has been analyzed as social vulnerability (V S ), vulnerability to property (V P ), and vulnerability to the location in terms of awareness (V A ). The associated risk has been estimated for each area. The distribution of risk values can be used to classify every area into one of the six risk zonesnamely, very low risk, low risk, moderately low risk, medium risk, high risk, and very high risk. The prioritization regarding preparedness, evacuation planning, or distribution of relief items should be guided by the range on the risk scale within which the area under study falls. The flood risk assessment model framework has been tested on a real-life case study. The flood risk indices for each of the municipalities in the area under study have been calculated. The risk indices and hence the flood risk zone under which a municipality is expected to lie would alter every day. The appropriate authorities can then plan ahead in terms of preparedness to combat the impending flood situation in the most critical and vulnerable areas.