A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Filters
Comparative Performance of Supervised Learning Algorithms for Flood Prediction in Kemaman, Terengganu
2021
Journal of Computer Science
Preserved Fulltext
Because the flooding uncertainties and the urgency to prepare for disaster management, three specific technique approaches are compared in this study to predict the flood occurrence based on historical ...
This prediction experiment will be conducted using three variations algorithms, which are Decision Tree, Naive Bayes and Support Vector Machine. ...
This project is set to predict flood based on historical rainfall data using a data mining approach. ...
doi:10.3844/jcssp.2021.451.458
fatcat:5xfsfchfjjczvkdq6u5j4mmz7i
Machine Learning based IoT Flood Rediction Using Data Modeling and Decision Support System
2023
International Journal on Recent and Innovation Trends in Computing and Communication
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2024; you can also visit the original URL.
The file type is application/pdf.
., have recently experienced severe and devastating natural disasters. One of the biggest causes of the destruction in Asian nations like china, India, Bangladesh, Sri Lanka, etc. is the flood. ...
An essential step in supplying data for climate impact studies and evaluations of hydrological processes is rainfall prediction. ...
The findings suggest that utilizing the right data science based on ensemble machine learning can provide early warning of flood severity approaches. Opportunity projects will utilize. Fig. 1.1. ...
doi:10.17762/ijritcc.v11i9.9318
fatcat:zqv7k2snxfftbeqpkh32vg3c34
Machine Learning and Urban Drainage Systems: State-of-the-Art Review
2021
Water
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
This review paper provides a state-of-the-art review of ML-based UDS modeling/application based on three categories: (1) operation (real-time operation control), (2) management (flood-inundation prediction ...
Additionally, some potential issues and future directions are recommended for three research topics defined in this study to extend UDS modeling/applications based on ML technology. ...
Meanwhile, the CNN is based on image data and can consider a spatiotemporal distribution for the type of flood-inundation (e.g., flood depth and area, and flood duration) prediction. ...
doi:10.3390/w13243545
fatcat:l3vq6vefkfaybh4nl22zzk5kte
Comparing Performance of Machine Learning Algorithms in a Flood Prediction Model with Real Data Sets
2019
International Journal of Advanced Trends in Computer Science and Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Thus, for this research, three machine learning methods which are Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) is chosen for flood prediction model. ...
Flood is one of an unforeseen and often sudden event or a situation that causes severe damage, destruction and human suffering which requires help by requesting to national or international level. ...
METHODOLOGY Machine learning approach plays a major role in prediction. ...
doi:10.30534/ijatcse/2019/2381.42019
fatcat:da2m5xqghfcknopclsst2ilzqi
A Review on Machine Learning-Based Neural Network Techniques for Flood Prediction
2022
VFAST Transactions on Software Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2024; you can also visit the original URL.
The file type is application/pdf.
Floods are unexpected. A few subjective techniques exist in the literature for the prediction of the danger level of floods caused by natural events. ...
This SLR is based on papers ranging from 2015 to 2021 and provides a combination of different algorithms and procedures based on artificial intelligence in the context of how these techniques assist in ...
Future work In this study, we provided an SLR based on ANN machine learning and avoided a significant portion of flood prediction based on geography. ...
doi:10.21015/vtse.v10i1.835
fatcat:3huarracuffedo5fn62soczxdu
Cloud Based Application for Prediction of Natural Disasters
2021
International Journal for Research in Applied Science and Engineering Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In Machine learning concept of random forest regression is used so that it can predict accurate result compare to other modules based on the result we have proposed the model for natural disasters detection ...
Artificial Intelligence can be used to analysis the data which can be used in prediction of warning for future events & create awareness for the situation. ...
We proposed the model that predicts a natural disaster based on a machine learning approach, our model used the concept of random forest regression & we also observe random forest regression algorithm ...
doi:10.22214/ijraset.2021.36414
fatcat:k3xi6kgrrzgtnj6vbco46ws44q
Evaluation on NTM based Predictive Analytics on Rainfall and Flood Disaster Management
2020
International journal of modern trends in science and technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this Research article, we presented a new approach for predicting the flood through the advanced Machine learning Algorithm which is one among the Neural networks class that outperforms itself in best ...
On Comparing to the Previous Researches its observed that the Neural Turing networks have been performing the prediction of the rainfall and flood-based disasters for the consecutive year counts of 10,15 ...
RESEARCH METHODOLOGY Based on the previous researchers all the researches have been taken with the evaluation parameters of the machine learning algorithm, Neural Turing Networks. ...
doi:10.46501/ijmtst061016
fatcat:et5xtkye25ckfk4pruuylmic6m
USE OF MULTIVARIATE MACHINE LEARNING ANALYSIS TECHNIQUES FOR FLOOD RISK PREVENTION
2018
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
<br> This work propose strategies and case studies on the application on machine learning algorithms on floods events prediction. ...
Machine learning is interesting for predictions because it adapts the resolution strategies to the features of the data. ...
Figure 5 .1 proposes a model for the flood prediction of Seveso rivers, that matches this two categories of input into a Machine Learning prediction algorithm based on ANN and Support Vector Machines. ...
doi:10.5194/isprs-archives-xlii-3-w4-549-2018
fatcat:t3o6wo4olffwbpwusocsmd4cwa
FLOOD PREDICTION AND ASSESSMENT PLATFORM A MULTI-MODEL APPROACH
2020
International Journal of Engineering Applied Sciences and Technology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
So here we propose a flood prediction system that uses machine learning models to deliver chances of flood based on location and time queries. ...
Based on the results, actions that need to be taken can also be predicted. The model is trained on a comprehensive data set expanding over years. ...
SVM is greatly popular in flood modelling; it is a supervised learning machine which works based on the statistical learning theory and the structural risk minimization rule. ...
doi:10.33564/ijeast.2020.v04i12.046
fatcat:53w5vbhgwjf6vcs2wnq6wvrxy4
Water Hazard Prediction using Machine Learning
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
This study aimed to forecast both flood and drought using Machine Learning (ML). ...
AI is intriguing for forecasts since it adjusts the goal methodologies to the highlights of the data set. ...
This paper is based on the flood hazards caused by Climatic changes around. This paper expels recent machine learning algorithms and predictive analysis. ...
doi:10.35940/ijitee.a4245.119119
fatcat:ox7jb4alhnh3paw65a6idmuzlq
IoT-enabled Flood Severity Prediction via Ensemble Machine Learning Models
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Research outcomes indicate that ensemble learning provides a more reliable tool to predict flood severity levels. ...
This paper entails a new approach for the prediction of water level in association with flood severity using the ensemble model. ...
METHODOLOGY Various ML techniques have been used to predict the severity of flood disasters based on sensor data [49] . ...
doi:10.1109/access.2020.2986090
fatcat:qinirs64ljcy5ehoqbf2esfzky
Intelligent Decision Support System for Disaster Managers using Machine Learning
2020
International Journal of Advanced Trends in Computer Science and Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Cuddalore Flood Management DSS (CFMDSS) is an intelligent DSS developed with the state of art machine learning algorithms with high accuracy and precision which makes this process much simpler. ...
The flood-prone areas are neatly mapped in the web-based GIS which can be extensively visualized and accessed free of cost. ...
The Machine Learning Model is a complex mathematical and condition based artefact that is created by a machine learning algorithm that takes input and gives output based on the training given to it. ...
doi:10.30534/ijatcse/2020/95942020
fatcat:dcuiyddflffuzja2kaqdumkvtu
Computational Machine Learning Approach for Flood Susceptibility Assessment Integrated with Remote Sensing and GIS Techniques from Jeddah, Saudi Arabia
2022
Remote Sensing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
This study aims to demonstrate the predictive ability of four ensemble algorithms for assessing flood risk. ...
The 141 flood locations have been identified in the research area based on the interpretation of aerial photos, historical data, Google Earth, and field surveys. ...
Acknowledgments: Authors would like to acknowledge all support provided by the Interdisciplinary Research Centre for Membranes and Water Security (IRC-MWS) and Interdisciplinary Research Center for Intelligent ...
doi:10.3390/rs14215515
fatcat:lvbxnlsiqjgx3bhfhvo7sayyji
A Survey of DDOS Attacks Using Machine Learning Techniques
2020
E3S Web of Conferences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Within this article the process is supported on the latest dataset containing the current form of DDoS attacks including (HTTP flood, SIDDoS). ...
DDOS stands for Distributed Denial Of Service attacks. ...
and predict unknown stats information on learned data. ...
doi:10.1051/e3sconf/202018401052
fatcat:gpaxt3wuvzbcdhsj5uo3yj5n7y
Flash Flood Susceptibility Modelling Using Soft Computing-Based Approaches: From Bibliometric to Meta-Data Analysis and Future Research Directions
2024
Water
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2024; you can also visit the original URL.
The file type is application/pdf.
The results of the meta-data analysis indicated that hybrid models were the most frequently used prediction models. ...
In recent years, there has been a growing interest in flood susceptibility modeling. ...
On comparing the standard models, in many cases, the machine learning model exhibited higher prediction accuracy than the expert-based methods. For example, Nachappa et al. ...
doi:10.3390/w16010173
fatcat:v6nx2hhzqzfjtg6ukp7q62sog4
« Previous
Showing results 1 — 15 out of 11,401 results