The Convergence of IoT, Machine Learning, and Big Data for Advancing Flood Analytics Knowledge

Data Gathering ◽

Flood Frequency ◽

Crowd Intelligence

<p>Floods are among the most destructive natural hazard that affect millions of people across the world leading to severe loss of life and damage to property, critical infrastructure, and agriculture. Internet of Things (IoTs), machine learning (ML), and Big Data are exceptionally valuable tools for collecting the catastrophic readiness and countless actionable data. The aim of this presentation is to introduce Flood Analytics Information System (FAIS) as a data gathering and analytics system. &#160;FAIS application is smartly designed to integrate crowd intelligence, ML, and natural language processing of tweets to provide warning with the aim to improve flood situational awareness and risk assessment. FAIS has been Beta tested during major hurricane events in US where successive storms made extensive damage and disruption. The prototype successfully identifies a dynamic set of at-risk locations/communities using the USGS river gauge height readings and geotagged tweets intersected with watershed boundary. The list of prioritized locations can be updated, as the river monitoring system and condition change over time (typically every 15 minutes).&#160; The prototype also performs flood frequency analysis (FFA) using various probability distributions with the associated uncertainty estimation to assist engineers in designing safe structures. This presentation will discuss about the FAIS functionalities and real-time implementation of the prototype across south and southeast USA. This research is funded by the US National Science Foundation (NSF).</p>

Analysis of Magnitude and Frequency of Floods in the Damanganga Basin: Western India

Hydrospatial Analysis ◽

10.21523/gcj3.2021050101 ◽

2021 ◽

Vol 5 (1) ◽

pp. 1-11

Author(s):

Vitthal Anwat ◽

Pramodkumar Hire ◽

Uttam Pawar ◽

Rajendra Gunjal

Keyword(s):

Probability Distribution ◽

Flood Frequency ◽

Western India ◽

Type I ◽

Return Periods ◽

Pearson Type ◽

Kolmogorov Smirnov ◽

Anderson Darling

Flood Frequency Analysis (FFA) method was introduced by Fuller in 1914 to understand the magnitude and frequency of floods. The present study is carried out using the two most widely accepted probability distributions for FFA in the world namely, Gumbel Extreme Value type I (GEVI) and Log Pearson type III (LP-III). The Kolmogorov-Smirnov (KS) and Anderson-Darling (AD) methods were used to select the most suitable probability distribution at sites in the Damanganga Basin. Moreover, discharges were estimated for various return periods using GEVI and LP-III. The recurrence interval of the largest peak flood on record (Qmax) is 107 years (at Nanipalsan) and 146 years (at Ozarkhed) as per LP-III. Flood Frequency Curves (FFC) specifies that LP-III is the best-fitted probability distribution for FFA of the Damanganga Basin. Therefore, estimated discharges and return periods by LP-III probability distribution are more reliable and can be used for designing hydraulic structures.

Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows

Hydrology and Earth System Sciences ◽

10.5194/hess-18-353-2014 ◽

2014 ◽

Vol 18 (1) ◽

pp. 353-365 ◽

Cited By ~ 35

Author(s):

U. Haberlandt ◽

I. Radtke

Keyword(s):

Time Series ◽

Frequency Analysis ◽

Model Calibration ◽

Hydrological Model ◽

Flood Frequency ◽

Rainfall Data ◽

Peak Flows ◽

Hourly Rainfall

Abstract. Derived flood frequency analysis allows the estimation of design floods with hydrological modeling for poorly observed basins considering change and taking into account flood protection measures. There are several possible choices regarding precipitation input, discharge output and consequently the calibration of the model. The objective of this study is to compare different calibration strategies for a hydrological model considering various types of rainfall input and runoff output data sets and to propose the most suitable approach. Event based and continuous, observed hourly rainfall data as well as disaggregated daily rainfall and stochastically generated hourly rainfall data are used as input for the model. As output, short hourly and longer daily continuous flow time series as well as probability distributions of annual maximum peak flow series are employed. The performance of the strategies is evaluated using the obtained different model parameter sets for continuous simulation of discharge in an independent validation period and by comparing the model derived flood frequency distributions with the observed one. The investigations are carried out for three mesoscale catchments in northern Germany with the hydrological model HEC-HMS (Hydrologic Engineering Center's Hydrologic Modeling System). The results show that (I) the same type of precipitation input data should be used for calibration and application of the hydrological model, (II) a model calibrated using a small sample of extreme values works quite well for the simulation of continuous time series with moderate length but not vice versa, and (III) the best performance with small uncertainty is obtained when stochastic precipitation data and the observed probability distribution of peak flows are used for model calibration. This outcome suggests to calibrate a hydrological model directly on probability distributions of observed peak flows using stochastic rainfall as input if its purpose is the application for derived flood frequency analysis.

A study on selection of probability distributions for at-site flood frequency analysis in Mahanadi River Basin, India

River Flow 2014 ◽

10.1201/b17133-241 ◽

2014 ◽

pp. 1813-1819

Author(s):

N Guru ◽

R Jha

Keyword(s):

River Basin ◽

Frequency Analysis ◽

Flood Frequency ◽

Mahanadi River ◽

Selection Of

Some best‐fit probability distributions for at‐site flood frequency analysis of the Ume River

Journal of Flood Risk Management ◽

10.1111/jfr3.12640 ◽

2020 ◽

Vol 13 (3) ◽

Author(s):

Samara Kousar ◽

Abrar Raza Khan ◽

Mahmood Ul Hassan ◽

Zahra Noreen ◽

Sajjad Haider Bhatti

Keyword(s):

Frequency Analysis ◽

Flood Frequency ◽

Best Fit

Process-based flood frequency analysis in an agricultural watershed exhibiting nonstationary flood seasonality

Hydrology and Earth System Sciences ◽

10.5194/hess-23-2225-2019 ◽

2019 ◽

Vol 23 (5) ◽

pp. 2225-2243 ◽

Cited By ~ 7

Author(s):

Guo Yu ◽

Daniel B. Wright ◽

Zhihua Zhu ◽

Cassia Smith ◽

Kathleen D. Holman

Keyword(s):

Soil Moisture ◽

Frequency Analysis ◽

Extreme Rainfall ◽

Hydrologic Model ◽

Flood Frequency ◽

Agricultural Watershed ◽

Antecedent Conditions ◽

Hydrologic Change

Abstract. Floods are the product of complex interactions among processes including precipitation, soil moisture, and watershed morphology. Conventional flood frequency analysis (FFA) methods such as design storms and discharge-based statistical methods offer few insights into these process interactions and how they “shape” the probability distributions of floods. Understanding and projecting flood frequency in conditions of nonstationary hydroclimate and land use require deeper understanding of these processes, some or all of which may be changing in ways that will be undersampled in observational records. This study presents an alternative “process-based” FFA approach that uses stochastic storm transposition to generate large numbers of realistic rainstorm “scenarios” based on relatively short rainfall remote sensing records. Long-term continuous hydrologic model simulations are used to derive seasonally varying distributions of watershed antecedent conditions. We couple rainstorm scenarios with seasonally appropriate antecedent conditions to simulate flood frequency. The methodology is applied to the 4002 km2 Turkey River watershed in the Midwestern United States, which is undergoing significant climatic and hydrologic change. We show that, using only 15 years of rainfall records, our methodology can produce accurate estimates of “present-day” flood frequency. We found that shifts in the seasonality of soil moisture, snow, and extreme rainfall in the Turkey River exert important controls on flood frequency. We also demonstrate that process-based techniques may be prone to errors due to inadequate representation of specific seasonal processes within hydrologic models. If such mistakes are avoided, however, process-based approaches can provide a useful pathway toward understanding current and future flood frequency in nonstationary conditions and thus be valuable for supplementing existing FFA practices.

Maximum entropy probability distributions for flood frequency analysis

Civil Engineering Systems ◽

10.1080/02630258708970461 ◽

1987 ◽

Vol 4 (2) ◽

pp. 67-76 ◽

Cited By ~ 4

Author(s):

A. Ramachandra Rao ◽

Chang-Hsin Hsieh

Keyword(s):

Maximum Entropy ◽

Frequency Analysis ◽

Flood Frequency ◽

Flood Frequency Analysis

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

Sentiment Analysis on Social Media Big Data With Multiple Tweet Words

10.35940/ijitee.j9684.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 3429-3434 ◽

Cited By ~ 2

Keyword(s):

Machine Learning ◽

Social Media ◽

Big Data ◽

Sentiment Analysis ◽

Language Processing ◽

Sentiment Classification ◽

Support Vector ◽

Decision Tree Classifier ◽

Machine Learning Classification ◽

Tree Classifier

The main objective of this paper is Analyze the reviews of Social Media Big Data of E-Commerce product’s. And provides helpful result to online shopping customers about the product quality and also provides helpful decision making idea to the business about the customer’s mostly liking and buying products. This covers all features or opinion words, like capitalized words, sequence of repeated letters, emoji, slang words, exclamatory words, intensifiers, modifiers, conjunction words and negation words etc available in tweets. The existing work has considered only two or three features to perform Sentiment Analysis with the machine learning technique Natural Language Processing (NLP). In this proposed work familiar Machine Learning classification models namely Multinomial Naïve Bayes, Support Vector Machine, Decision Tree Classifier, and, Random Forest Classifier are used for sentiment classification. The sentiment classification is used as a decision support system for the customers and also for the business.

A Numerical Framework for Evaluating Flood Inundation Hazard under Different Dam Operation Scenarios—A Case Study in Naugatuck River

Water ◽

10.3390/w10121798 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1798 ◽

Cited By ~ 8

Author(s):

Sage Hardesty ◽

Xinyi Shen ◽

Efthymios Nikolopoulos ◽

Emmanouil Anagnostou

Keyword(s):

Critical Infrastructure ◽

Meteorological Data ◽

Hydrologic Model ◽

Flood Frequency ◽

Airborne Lidar ◽

Management Scenarios ◽

Integrative Framework ◽

River Floodplains ◽

Terrain Elevation

Worldwide, many river floodplains contain critical infrastructure that is vulnerable to extreme hydrologic events. These structures are designed based on flood frequency analysis aimed at quantifying the magnitude and recurrence of the extreme events. This research topic focuses on estimating flood vulnerability at ungauged locations based on an integrative framework consisting of a distributed rainfall–runoff model forced with long-term (37 years) reanalysis meteorological data and a hydraulic model driven by high-resolution airborne LiDAR-derived terrain elevation data. The framework is applied to a critical power infrastructure located within Connecticut’s Naugatuck River Basin. The hydrologic model reanalysis is used to derive 50-, 100-, 200-, and 500-year return period flood peaks, which are then used to drive Hydrologic Engineering Center’s River Analysis System (HEC-RAS) hydraulic simulations to estimate the inundation risk at the infrastructure location under different operation strategies of an upstream reservoir. This study illustrates the framework’s potential for creating flood maps at ungauged locations and demonstrates the effects of different water management scenarios on the flood risk of the downstream infrastructure.