scholarly journals Method for forecasting the flood risk in a tropical country

2020 ◽  
Vol 20 (6) ◽  
pp. 2261-2274
Author(s):  
Adriana Marquez ◽  
Bettys Farias ◽  
Edilberto Guevara
Keyword(s):  
Flood Risk ◽  
Mass Conservation ◽  
Mathematical Structure ◽  
Exceedance Probability ◽  
Rainfall Runoff ◽  
Tropical Country ◽  
Effective Precipitation ◽  
Proposed Model ◽  
Novel Method ◽  
Life Parameter

Abstract In this study, a novel method for forecasting the flood risk in a tropical country is proposed, called CIHAM-UC-FFR. The method is based on the rainfall–runoff process. The CIHAM-UC-FFR method consists of three stages: (1) calibration and validation for the effective precipitation model, called CIHAM-UC-EP model, (2) calibration of forecasting models for components of the CIHAM-UC-EP model, (3) proposed model for forecasting of gridded flood risk called CIHAM-UC-FR. The CIHAM-UC-EP model has a mathematical structure derived from a conceptual model obtained by applying the principle of mass conservation combined with the adapted principle of Fick's law. The CIHAM-UC-FR model is a stochastic equation based on the exceedance probability of the forecast effective precipitation. Various scenarios are shown for a future time where the flood risk is progressively decreased as the expected life parameter of the hydraulic structure is increased.

scholarly journals Assessment of Flood Risk Exposure for the Foshan-Zhongshan Region in Guangdong Province, China

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1159
Author(s):  
Qi Zhang ◽  
Wei Jian ◽  
Edmond Yat Man Lo
Keyword(s):  
Flood Risk ◽  
Pearl River Delta ◽  
Risk Exposure ◽  
Economic Losses ◽  
Flood Inundation ◽  
Rainfall Runoff ◽  
Proposed Model ◽  
Rainfall Runoff Model ◽  
The Pearl River ◽  
Runoff Model

Floods have caused 20% of the worldwide economic losses resulting from catastrophe events over 2008 to 2018. In China, the annual flood economic losses have exceeded CNY 100 billion from 1990 to 2010, which is equivalent to 1% to 3% of China’s Gross Domestic Product (GDP). This paper presents a rainfall-runoff model coupled with an inundation estimation to assess the flood risk for a basin within the Foshan-Zhongshan area of the Pearl River Delta (PRD) region in China. A Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) model was constructed for the crisscrossing river network in the study basin where the West and North Rivers meet, using publicly accessible meteorological, hydrological and topographical datasets. The developed model was used to analyze two recent flood events, that in July 2017 with large upstream river inflows, and in June 2018 with high local rainfall. Results were further used to develop the needed river rating curves within the basin. Two synthetic events that consider more severe meteorological and hydrological conditions were also analyzed. These results demonstrate the capability of the proposed model for quick assessment of potential flood inundation and the GDP exposure at risk within the economically important PRD region.

scholarly journals The FLOod Probability Interpolation Tool (FLOPIT): A Simple Tool to Improve Spatial Flood Probability Quantification and Communication

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 666
Author(s):  
Mahkameh Zarekarizi ◽  
K. Joel Roop-Eckart ◽  
Sanjib Sharma ◽  
Klaus Keller
Keyword(s):  
Flood Risk ◽  
Water Surface ◽  
The United States ◽  
Surface Elevation ◽  
Exceedance Probability ◽  
Water Surface Elevation ◽  
Inundation Maps ◽  
Probability Maps ◽  
Flood Probability ◽  
Floodplain Development

Understanding flood probabilities is essential to making sound decisions about flood-risk management. Many people rely on flood probability maps to inform decisions about purchasing flood insurance, buying or selling real-estate, flood-proofing a house, or managing floodplain development. Current flood probability maps typically use flood zones (for example the 1 in 100 or 1 in 500-year flood zones) to communicate flooding probabilities. However, this choice of communication format can miss important details and lead to biased risk assessments. Here we develop, test, and demonstrate the FLOod Probability Interpolation Tool (FLOPIT). FLOPIT interpolates flood probabilities between water surface elevation to produce continuous flood-probability maps. FLOPIT uses water surface elevation inundation maps for at least two return periods and creates Annual Exceedance Probability (AEP) as well as inundation maps for new return levels. Potential advantages of FLOPIT include being open-source, relatively easy to implement, capable of creating inundation maps from agencies other than FEMA, and applicable to locations where FEMA published flood inundation maps but not flood probability. Using publicly available data from the Federal Emergency Management Agency (FEMA) flood risk databases as well as state and national datasets, we produce continuous flood-probability maps at three example locations in the United States: Houston (TX), Muncy (PA), and Selinsgrove (PA). We find that the discrete flood zones generally communicate substantially lower flood probabilities than the continuous estimates.

scholarly journals A reliable rainfall–runoff model for flood forecasting: review and application to a semi-urbanized watershed at high flood risk in Italy

2016 ◽  
Vol 48 (3) ◽  
pp. 726-740 ◽  
Author(s):  
Daniele Masseroni ◽  
Alessio Cislaghi ◽  
Stefania Camici ◽  
Christian Massari ◽  
Luca Brocca
Keyword(s):  
Flood Risk ◽  
Climatic Conditions ◽  
Rainfall Runoff ◽  
Number Of Factors ◽  
Wide Range ◽  
Median Volume ◽  
Flood Estimation ◽  
High Flood ◽  
Rainfall Runoff Model ◽  
Flooding Events

Many rainfall–runoff (RR) models are available in the scientific literature. Selecting the best structure and parameterization for a model is not straightforward and depends on a broad number of factors, including climatic conditions, catchment characteristics, temporal/spatial resolution and model objectives. In this study, the RR model ‘Modello Idrologico Semi-Distribuito in continuo’ (MISDc), mainly developed for flood simulation in Mediterranean basins, was tested on the Seveso basin, which is stressed several times a year by flooding events mainly caused by excessive urbanization. The work summarizes a compendium of the MISDc applications over a wide range of catchments in European countries and then it analyses the performances over the Seveso basin. The results show a good fit behaviour during both the calibration and the validation periods with a Nash–Sutcliffe coefficient index larger than 0.9. Moreover, the median volume and peak discharge errors calculated on several flood events were less than 25%. In conclusion, we can be assured that the reliability and computational speed could make the MISDc model suitable for flood estimation in many catchments of different geographical contexts and land use characteristics. Moreover, MISDc will also be useful for future support of real-time decision-making for flood risk management in the Seveso basin.

scholarly journals A Novel Method to Detect Inner Emotion States of Human using Artificial Neural Networks

2020 ◽  
Vol 8 (6) ◽  
pp. 5820-5825
Keyword(s):  
Physiological Signals ◽  
Classification Rate ◽  
Discrete Emotion ◽  
Novel Approach ◽  
Proposed Model ◽  
Depression Detection ◽  
Novel Method ◽  
Artificial Neural ◽  
Ann Classifier ◽  
Performance Rate

Human computer interaction is a fast growing area of research where in the physiological signals are used to identify human emotion states. Identifying emotion states can be done using various approaches. One such approach which gained interest of research is through physiological signals using EEG. In the present work, a novel approach is proposed to elicit emotion states using 3-D Video-audio stimuli. Around 66 subjects were involved during data acquisition using 32 channel Enobio device. FIR filter is used to preprocess the acquired raw EEG signals. The desired frequency bands like alpha, delta, beta and theta are extracted using 8-level DWT. The statistical features, Hurst exponential, entropy, power, energy, differential entropy of each bands are computed. Artificial Neural network is implemented using Sequential Keras model and applied on the extracted features to classify in to four classes (HVLA, HVHA, LVHA and LVLA) and eight discrete emotion states like clam, relax, happy, joy, sad, fear, tensed and bored. The performance of ANN classifier found to perform better for 4- classes than 8-classes with a classification rate of 90.835% and 74.0446% respectively. The proposed model achieved better performance rate in detecting discrete emotion states. This model can be used to build applications on health like stress / depression detection and on entertainment to build emotional DJ.

Strategies to Predict E-Commerce Inventory and Order Planning

2021 ◽  
pp. 212-226
Author(s):  
Mohammad Anwar Rahman ◽  
Laura Casanovas
Keyword(s):  
Rapid Growth ◽  
Mathematical Structure ◽  
Electronic Market ◽  
Customer Expectation ◽  
Online Marketplace ◽  
Customer Order ◽  
Main Challenge ◽  
Proposed Model ◽  
Potential Demand ◽  
Demand Projection

This study examines the characteristics of a prediction model for businesses in the online marketplace by considering the market trend, prior sales and decision maker's preference on potential demand estimate. With the rapid growth of the electronic market, the main challenge for online sellers is the ability to analyze customer expectation, market data, and sales information to make the accurate procurement decision. The proposed model integrates a mathematical structure for a target season sale comprising upcoming demand projection by seller's internal team, data from past sales and the overall trend of seller's e-brand to determine the online customer demand. The study proposed a newsvendor model as a tool for sellers to make the instantaneous decision of ordering merchandise from the supplier when the quick response to the customer order is a priority for electronic market. Results of the study provide insights into the procurement dynamics and implications of the e-commerce inventory plan.

scholarly journals Augmented Context-Based Conceptual User Modeling for Personalized Recommendation System in Online Social Networks

2020 ◽  
Vol 14 (3) ◽  
pp. 1-19
Author(s):  
Ammar Alnahhas ◽  
Bassel Alkhatib
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Recommendation System ◽  
Contextual Information ◽  
Detailed Comparison ◽  
Personalized Recommendation ◽  
User Models ◽  
Proposed Model ◽  
Recommendation Algorithms ◽  
Novel Method

As the data on the online social networks is getting larger, it is important to build personalized recommendation systems that recommend suitable content to users, there has been much research in this field that uses conceptual representations of text to match user models with best content. This article presents a novel method to build a user model that depends on conceptual representation of text by using ConceptNet concepts that exceed the named entities to include the common-sense meaning of words and phrases. The model includes the contextual information of concepts as well, the authors also show a novel method to exploit the semantic relations of the knowledge base to extend user models, the experiment shows that the proposed model and associated recommendation algorithms outperform all previous methods as a detailed comparison shows in this article.

Lake Kinneret watershed contamination transports - a GIS based hydrological model

2003 ◽  
Vol 48 (10) ◽  
pp. 63-70 ◽  
Author(s):  
A. Ostfeld ◽  
A. Pries
Keyword(s):  
Hydrological Model ◽  
Lake Kinneret ◽  
Water Quantity ◽  
Rainfall Runoff ◽  
Input Output ◽  
Runoff Water ◽  
Gis Database ◽  
Proposed Model ◽  
Physical Response ◽  
Contaminants Transport

This paper describes the efforts and current achievements of developing a GIS based hydrological model for flow and contaminants transport within Lake Kinneret watershed. The proposed model is built of hydrological “input-output” physical response blocks for routing rainfall-runoff water quantity and quality in sub-watersheds, coupled further with a delineated GIS database. An illustrative example of the model capabilities is demonstrated.

scholarly journals A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Zhijian Wang ◽  
Likang Zheng ◽  
Wenhua Du ◽  
Wenan Cai ◽  
Jie Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Weight Coefficient ◽  
Convergence Condition ◽  
Intelligent Fault Diagnosis ◽  
Wind Turbine Gearbox ◽  
Time Frequency ◽  
Proposed Model ◽  
Novel Method ◽  
Relationship Of

In the era of big data, data-driven methods mainly based on deep learning have been widely used in the field of intelligent fault diagnosis. Traditional neural networks tend to be more subjective when classifying fault time-frequency graphs, such as pooling layer, and ignore the location relationship of features. The newly proposed neural network named capsules network takes into account the size and location of the image. Inspired by this, capsules network combined with the Xception module (XCN) is applied in intelligent fault diagnosis, so as to improve the classification accuracy of intelligent fault diagnosis. Firstly, the fault time-frequency graphs are obtained by wavelet time-frequency analysis. Then the time-frequency graphs data which are adjusted the pixel size are input into XCN for training. In order to accelerate the learning rate, the parameters which have bigger change are punished by cost function in the process of training. After the operation of dynamic routing, the length of the capsule is used to classify the types of faults and get the classification of loss. Then the longest capsule is used to reconstruct fault time-frequency graphs which are used to measure the reconstruction of loss. In order to determine the convergence condition, the three losses are combined through the weight coefficient. Finally, the proposed model and the traditional methods are, respectively, trained and tested under laboratory conditions and actual wind turbine gearbox conditions to verify the classification ability and reliable ability.

A scuffing model considering additive depletion in boundary lubrication

2019 ◽  
Vol 72 (3) ◽  
pp. 267-272
Author(s):  
Bora Lee ◽  
Yonghun Yu ◽  
Yong-Joo Cho
Keyword(s):  
Boundary Lubrication ◽  
Mass Conservation ◽  
Additive Concentration ◽  
Contact Conditions ◽  
Sliding Bearings ◽  
Content Type ◽  
Proposed Model ◽  
Lubrication Condition ◽  
Scuffing Failure ◽  
Sliding Distance

Purpose This paper aims to propose a new scuffing model caused by the depletion of additives in boundary lubrication condition. Design/methodology/approach The differential equation governing the distribution of additive content in the fluid film was used. This formula was derived from the principle of mass conservation of additives considering the consumption due to surface adsorption of wear particles. The occurrence of scuffing was determined by comparing the wear rate of the oxide layer with the oxidation rate. Findings If the additive becomes depleted while sliding, the scuffing failure occurs even at a low-temperature condition below the critical temperature. The critical sliding distance at which scuffing failure occurred was suggested. The experimental data of the existing literature and the theoretical prediction using the proposed model are shown to be in good agreement. Originality/value It is expected to be used in the design of oil supply grooves for sliding bearings operating under extreme conditions or in selecting the minimum initial additive concentration required to avoid scuffing failure under given contact conditions.

Microblog Sentiment Analysis Using User Similarity and Interaction-Based Social Relations

2020 ◽  
Vol 17 (3) ◽  
pp. 39-55
Author(s):  
Chuanmin Mi ◽  
Xiaoyan Ruan ◽  
Lin Xiao
Keyword(s):  
Sentiment Analysis ◽  
Social Relations ◽  
Rapid Development ◽  
Support Vector ◽  
Sina Weibo ◽  
User Similarity ◽  
Proposed Model ◽  
Novel Method ◽  
Polarity Classification ◽  
Similarity Information

With the rapid development of information technology, microblog sentiment analysis (MSA) has become a popular research topic extensively examined in the literature. Microblogging messages are usually short, unstructured, contain less information, creating a significant challenge for the application of traditional content-based methods. In this study, the authors propose a novel method, MSA-USSR, in which user similarity information and interaction-based social relations information are combined to build sentiment relationships between microblogging data. They make use of these microblog–microblog sentiment relations to train the sentiment polarity classification classifier. Two Sina-Weibo datasets were utilized to verify the proposed model. The experimental results show that the proposed method has a better sentiment classification accuracy and F1-score than the content-based support vector machine (SVM) method and the state-of-the-art supervised model known as SANT.

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