scholarly journals Flood Prediction Using Machine Learning, Literature Review

2018 ◽  
Author(s):  
Amir Mosavi ◽  
Pinar Ozturk ◽  
Chau Kwok-wing
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Comprehensive Evaluation ◽  
Human Life ◽  
Cost Effective ◽  
Performance Comparison ◽  
Flood Prediction ◽  
Hybridization Data ◽  
Mathematical Expressions ◽  
Prediction Systems

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literature where ML models are benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed have been particularly investigated to provide an extensive overview on various ML algorithms usage in the field. The performance comparison of ML models presents an in-depth understanding about the different techniques within the framework of a comprehensive evaluation and discussion. As the result, the paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported the most effective strategy in improvement of the ML methods. This survey can be used as a guideline for the hydrologists as well as climate scientists to assist them choosing the proper ML method according to the prediction task conclusions.

scholarly journals Flood Prediction Using Machine Learning, Literature Review

2018 ◽  
Author(s):  
Amir Mosavi ◽  
Pinar Ozturk ◽  
Kwok-wing Chau
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Comprehensive Evaluation ◽  
Human Life ◽  
Cost Effective ◽  
Performance Comparison ◽  
Flood Prediction ◽  
Hybridization Data ◽  
Mathematical Expressions ◽  
Prediction Systems

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literature where ML models are benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed have been particularly investigated to provide an extensive overview on various ML algorithms usage in the field. The performance comparison of ML models presents an in-depth understanding about the different techniques within the framework of a comprehensive evaluation and discussion. As the result, the paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported the most effective strategy in improvement of the ML methods. This survey can be used as a guideline for the hydrologists as well as climate scientists to assist them choosing the proper ML method according to the prediction task conclusions.

scholarly journals Flood Prediction Using Machine Learning Models: Literature Review

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1536 ◽  
Author(s):  
Amir Mosavi ◽  
Pinar Ozturk ◽  
Kwok-wing Chau
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Comprehensive Evaluation ◽  
Human Life ◽  
Cost Effective ◽  
Performance Comparison ◽  
Flood Prediction ◽  
Effective Strategies ◽  
Hybridization Data ◽  
Prediction Systems

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction of the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists. Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods. This survey can be used as a guideline for hydrologists as well as climate scientists in choosing the proper ML method according to the prediction task.

scholarly journals Flood Prediction using Artificial Neural Network

2021 ◽  
pp. 303-308
Author(s):  
Chaitanya Thekkunja ◽  
Dr. ShivaKumar G. S ◽  
Shubhang S Aroor
Keyword(s):  
Predictive Model ◽  
Prediction Models ◽  
Human Life ◽  
Confusion Matrix ◽  
Cost Effective ◽  
Complex Nature ◽  
Flood Prediction ◽  
Mathematical Expressions ◽  
Prediction Systems ◽  
Mlp Model

Floods are one of the foremost catastrophic natural disasters, and, thanks to their complex nature, it's tough to make a predictive model. The advanced research works on flood prediction models have contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduced property damage related to floods. In general, ML algorithms are utilized in the event of prediction systems, to mimic the complex mathematical expressions of the physical processes of floods providing better performance and cost-effective solutions. The MLP model is implemented in this system by calculating accuracy values with examining the confusion matrix parameters. The proposed system analyses the dataset using Multilayer Perceptron Classifier (MLP) algorithm to coach the predictive model, and floods are often predicted.

scholarly journals Prediction of Flash Flood using Rainfall by MLP Classifier

2020 ◽  
Vol 9 (1) ◽  
pp. 425-429
Keyword(s):  
Prediction Model ◽  
Nearest Neighbor ◽  
Weather Forecasting ◽  
Flash Flood ◽  
Human Life ◽  
Cost Effective ◽  
Rainfall Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Flood Prediction

Flood are one of the unfavorable natural disasters. A flood can result in a huge loss of human lives and properties. It can also affect agricultural lands and destroy cultivated crops and trees. The flood can occur as a result of surface-runoff formed from melting snow, long-drawn-out rains, and derisory drainage of rainwater or collapse of dams. Today people have destroyed the rivers and lakes and have turned the natural water storage pools to buildings and construction lands. Flash floods can develop quickly within a few hours when compared with a regular flood. Research in prediction of flood has improved to reduce the loss of human life, property damages, and various problems related to the flood. Machine learning methods are widely used in building an efficient prediction model for weather forecasting. This advancement of the prediction system provides cost-effective solutions and better performance. In this paper, a prediction model is constructed using rainfall data to predict the occurrence of floods due to rainfall. The model predicts whether “flood may happen or not” based on the rainfall range for particular locations. Indian district rainfall data is used to build the prediction model. The dataset is trained with various algorithms like Linear Regression, K- Nearest Neighbor, Support Vector Machine, and Multilayer Perceptron. Among this, MLP algorithm performed efficiently with the highest accuracy of 97.40%. The MLP flash flood prediction model can be useful for the climate scientist to predict the flood during a heavy downpour with the highest accuracy.

scholarly journals A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 219 ◽  
Author(s):  
Sweta Bhattacharya ◽  
Siva Rama Krishnan S ◽  
Praveen Kumar Reddy Maddikunta ◽  
Rajesh Kaluri ◽  
Saurabh Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Comprehensive Evaluation ◽  
Detection System ◽  
Human Life ◽  
Principal Component ◽  
Cyber Attacks ◽  
Classification Model ◽  
Learning Approaches ◽  
Machine Learning Model

The enormous popularity of the internet across all spheres of human life has introduced various risks of malicious attacks in the network. The activities performed over the network could be effortlessly proliferated, which has led to the emergence of intrusion detection systems. The patterns of the attacks are also dynamic, which necessitates efficient classification and prediction of cyber attacks. In this paper we propose a hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets. The dataset used in the study is collected from Kaggle. The model first performs One-Hot encoding for the transformation of the IDS datasets. The hybrid PCA-firefly algorithm is then used for dimensionality reduction. The XGBoost algorithm is implemented on the reduced dataset for classification. A comprehensive evaluation of the model is conducted with the state of the art machine learning approaches to justify the superiority of our proposed approach. The experimental results confirm the fact that the proposed model performs better than the existing machine learning models.

Machine Learning Approaches for Cardiovascular Disease Prediction

2021 ◽  
pp. 75-84
Author(s):  
Stuti Pandey ◽  
Abhay Kumar Agarwal
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Human Life ◽  
Medical Science ◽  
Second Primary ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Prediction Systems ◽  
Medical Analysis

In a human body, the heart is the second primary organ after the brain. It causes either a long-term impairment or death of a person if suffering from a cardiovascular disease. In medical science, a proper medical analysis and examination of a cardiovascular disease is very crucial, convincing, and sophisticated task for saving a human life. Data analytics rises because of the absence of sufficient practical tools for exploring the trends and unknown relationships in e-health records. It predicts and achieves information which can ease the diagnosis. This survey examines cardiovascular disease prediction systems developed by different researchers. It also reviews the trend of machine learning approaches used in the past decade with results. Related studies comprise the performance of various classifiers on distinct datasets.

scholarly journals Symptom Prediction and Mortality Risk Calculation for COVID-19 Using Machine Learning

2021 ◽  
Vol 4 ◽  
Author(s):  
Elham Jamshidi ◽  
Amirhossein Asgary ◽  
Nader Tavakoli ◽  
Alireza Zali ◽  
Farzaneh Dastan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Prediction Models ◽  
Comprehensive Evaluation ◽  
Gastrointestinal Symptoms ◽  
Risk Groups ◽  
Mortality Prediction ◽  
Healthcare Outcomes ◽  
Consciousness Disorders ◽  
Medical Histories

Background: Early prediction of symptoms and mortality risks for COVID-19 patients would improve healthcare outcomes, allow for the appropriate distribution of healthcare resources, reduce healthcare costs, aid in vaccine prioritization and self-isolation strategies, and thus reduce the prevalence of the disease. Such publicly accessible prediction models are lacking, however.Methods: Based on a comprehensive evaluation of existing machine learning (ML) methods, we created two models based solely on the age, gender, and medical histories of 23,749 hospital-confirmed COVID-19 patients from February to September 2020: a symptom prediction model (SPM) and a mortality prediction model (MPM). The SPM predicts 12 symptom groups for each patient: respiratory distress, consciousness disorders, chest pain, paresis or paralysis, cough, fever or chill, gastrointestinal symptoms, sore throat, headache, vertigo, loss of smell or taste, and muscular pain or fatigue. The MPM predicts the death of COVID-19-positive individuals.Results: The SPM yielded ROC-AUCs of 0.53–0.78 for symptoms. The most accurate prediction was for consciousness disorders at a sensitivity of 74% and a specificity of 70%. 2,440 deaths were observed in the study population. MPM had a ROC-AUC of 0.79 and could predict mortality with a sensitivity of 75% and a specificity of 70%. About 90% of deaths occurred in the top 21 percentile of risk groups. To allow patients and clinicians to use these models easily, we created a freely accessible online interface at www.aicovid.net.Conclusion: The ML models predict COVID-19-related symptoms and mortality using information that is readily available to patients as well as clinicians. Thus, both can rapidly estimate the severity of the disease, allowing shared and better healthcare decisions with regard to hospitalization, self-isolation strategy, and COVID-19 vaccine prioritization in the coming months.

scholarly journals Symptom Prediction and Mortality Risk Calculation for COVID-19 Using Machine Learning

2021 ◽  
Author(s):  
Elham Jamshidi ◽  
Amirhossein Asgary ◽  
Nader Tavakoli ◽  
Alireza Zali ◽  
Farzaneh Dastan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Prediction Models ◽  
Comprehensive Evaluation ◽  
Gastrointestinal Symptoms ◽  
Risk Groups ◽  
Mortality Prediction ◽  
Healthcare Outcomes ◽  
Consciousness Disorders ◽  
Medical Histories

ABSTRACTBackgroundEarly prediction of symptoms and mortality risks for COVID-19 patients would improve healthcare outcomes, allow for the appropriate distribution of healthcare resources, reduce healthcare costs, aid in vaccine prioritization and self-isolation strategies, and thus reduce the prevalence of the disease. Such publicly accessible prediction models are lacking, however.MethodsBased on a comprehensive evaluation of existing machine learning (ML) methods, we created two models based solely on the age, gender, and medical histories of 23,749 hospital-confirmed COVID-19 patients from February to September 2020: a symptom prediction model (SPM) and a mortality prediction model (MPM). The SPM predicts 12 symptom groups for each patient: respiratory distress, consciousness disorders, chest pain, paresis or paralysis, cough, fever or chill, gastrointestinal symptoms, sore throat, headache, vertigo, loss of smell or taste, and muscular pain or fatigue. The MPM predicts the death of COVID-19-positive individuals.ResultsThe SPM yielded ROC-AUCs of 0.53-0.78 for symptoms. The most accurate prediction was for consciousness disorders at a sensitivity of 74% and a specificity of 70%. 2440 deaths were observed in the study population. MPM had a ROC-AUC of 0.79 and could predict mortality with a sensitivity of 75% and a specificity of 70%. About 90% of deaths occurred in the top 21 percentile of risk groups. To allow patients and clinicians to use these models easily, we created a freely accessible online interface at www.aicovid.org.ConclusionsThe ML models predict COVID-19-related symptoms and mortality using information that is readily available to patients as well as clinicians. Thus, both can rapidly estimate the severity of the disease, allowing shared and better healthcare decisions with regard to hospitalization, self-isolation strategy, and COVID-19 vaccine prioritization in the coming months.Abstract Figure

scholarly journals Elderly Healthcare System for Chronic Ailments using Machine Learning Techniques – a Review

2021 ◽  
pp. 3138-3151
Author(s):  
R. L. Priya ◽  
S. Vinila Jinny
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Early Stage ◽  
Review Literature ◽  
Machine Learning Techniques ◽  
Emergency Situations ◽  
Learning Techniques ◽  
Generic Architecture ◽  
Prediction Systems ◽  
The Right

     World statistics declare that aging has direct correlations with more and more health problems with comorbid conditions. As healthcare communities evolve with a massive amount of data at a faster pace, it is essential to predict, assist, and prevent diseases at the right time, especially for elders. Similarly, many researchers have discussed that elders suffer extensively due to chronic health conditions.  This work was performed to review literature studies on prediction systems for various chronic illnesses of elderly people. Most of the reviewed papers proposed machine learning prediction models combined with, or without, other related intelligence techniques for chronic disease detection of elderly patients at an early stage to avoid emergency situations. This method provides a promising approach in the analysis of either structured or unstructured datasets to produce very substantial pattern discoveries. By defining the generic architecture for the prediction model, we reviewed various papers involved in similar fields, based on suggested methodologies and their associated outcomes. The study discussed the pros and cons of different prediction models using traditional and modern machine learning techniques.

scholarly journals Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites

Cells ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 95 ◽  
Author(s):  
Md. Mehedi Hasan ◽  
Mst. Shamima Khatun ◽  
Hiroyuki Kurata
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Prediction Models ◽  
Cell Metabolism ◽  
Cost Effective ◽  
Learning Approaches ◽  
Large Scale Assessment ◽  
Current State ◽  
Lysine Succinylation ◽  
Essential Properties

Lysine succinylation is a form of posttranslational modification of the proteins that play an essential functional role in every aspect of cell metabolism in both prokaryotes and eukaryotes. Aside from experimental identification of succinylation sites, there has been an intense effort geared towards the development of sequence-based prediction through machine learning, due to its promising and essential properties of being highly accurate, robust and cost-effective. In spite of these advantages, there are several problems that are in need of attention in the design and development of succinylation site predictors. Notwithstanding of many studies on the employment of machine learning approaches, few articles have examined this bioinformatics field in a systematic manner. Thus, we review the advancements regarding the current state-of-the-art prediction models, datasets, and online resources and illustrate the challenges and limitations to present a useful guideline for developing powerful succinylation site prediction tools.

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