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Water Hazard Prediction using Machine Learning

So as to have a clear and precise forecast of flood and drought hazard is fundamental to play out a specific and multivariate analysis among the various kinds of data sets.  ...  If precautions are not taken beforehand it becomes more and more difficult to control when it occurs. This study aimed to forecast both flood and drought using Machine Learning (ML).  ...  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

Comparing Ensembles Of Decision Trees And Neural Networks For One-day-ahead Stream Flow Predict

2013 Science Park  
In this study, bagging and gradient boosting algorithms are incorporated into the model creation process for daily streamflow prediction.  ...  The results obtained in this study indicate that ensemble learning models yield better prediction accuracy than a conventional ANN model. Moreover, ANN ensembles are superior to tree-based ensembles.  ...  One of the most powerful meta-learning techniques is gradient boosting, which is a statistical method of fitting an additive model of base functions.  ... 
doi:10.9780/23218045/1172013/41 fatcat:7wszqqrygrfktj6l7qnfotbicm

A Modern Method to Improve of Detecting and Categorizing Mechanism for Micro Seismic Events Data Using Boost Learning System

Morteza Barari, Mojtaba Hosseini
2017 Civil Engineering Journal  
, 4) generating relevant models with training samples and detecting and clustering test samples and 5) extracting a cluster with the maximum candidate using boost learning.  ...  Therefor in present study, a boost learning system consisting support vector machine algorithms with linear regression, MLP Neural Network ،C4.5 decision tree and KNN near neighbourhood have been utilized  ...  tree and KNN algorithm in the form of boost learning.  ... 
doi:10.21859/cej-03098 fatcat:orqayx6bzngz3mioml72mjvlte

Annual and non-monsoon rainfall prediction modelling using SVR-MLP: An Empirical study from Odisha

Xiaobo Zhang, Sachi Nandan Mohanty, Ajaya Kumar Parida, Subhendu kumar Pani
2020 IEEE Access  
Rainfall is a natural demolishing phenomenon. On the other side, it also serves as a major source of water when conserved through proper channel.  ...  The present study employed on rain fall forecasting in annual as well as non-moon session in Odisha (India).  ...  This study shows that for annual rainfall prediction for Odisha using multiple regression analysis SVM (PUK Kernel) model showed better performance than MLP.  ... 
doi:10.1109/access.2020.2972435 fatcat:6dpt6gt5d5f6lgmydvnot3fz3m

Comparative Performance of Supervised Learning Algorithms for Flood Prediction in Kemaman, Terengganu

Nur Najihah Shaaban, Norlida Hassan, Aida Mustapha, Salama A. Mostafa
2021 Journal of Computer Science  
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  ...  Flood is one of the most destructive phenomena all over the world.  ...  Support Vector Machine (SVM) algorithms in predicting the flood occurrences based on rainfall data.  ... 
doi:10.3844/jcssp.2021.451.458 fatcat:5xfsfchfjjczvkdq6u5j4mmz7i

Analysis, Characterization, Prediction and Attribution of Extreme Atmospheric Events with Machine Learning: a Review [article]

Sancho Salcedo-Sanz, Jorge Pérez-Aracil, Guido Ascenso, Javier Del Ser, David Casillas-Pérez, Christopher Kadow, Dusan Fister, David Barriopedro, Ricardo García-Herrera, Marcello Restelli, Mateo Giuliani, Andrea Castelletti
2022 arXiv   pre-print
A number of examples is discussed and perspectives and outlooks on the field are drawn.  ...  This paper reviews the ML algorithms applied to the analysis, characterization, prediction, and attribution of the most important atmospheric EEs.  ...  Acknowledgement This research has been partially supported by the European Union, through H2020 Project "CLIMATE INTELLIGENCE Extreme events detection, attribution and adaptation design using machine learning  ... 
arXiv:2207.07580v1 fatcat:ktzhrgnlcng55cdfdasynzfrxq


E. M. Sellami, M. Maanan, H. Rhinane
2022 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The aim of this study is to propose a new approach for detection and forecasting of flash flood susceptibility in the city of Tetouan, Morocco.  ...  For this regard, support vector machine (SVM), logistic regression (LR), random forest (RF), Naïve Bayes (NB) and Artificial neural network (ANN) are used based on 1101 points (680 flood points and 421  ...  6 :Figure 7 :Figure 5 : 675 Figure 6 : RF model with another 3D view Based on the SVM, LR, RF, ANN and RF models, flood risk maps of Tetouan city are generated in a GIS environment.  ... 
doi:10.5194/isprs-archives-xlvi-4-w3-2021-305-2022 fatcat:6hluk23etvcerdcd4dnoq3yidm

Machine Learning in Dam Water Research: An Overview of Applications and Approaches

Farashazillah Yahya
2020 International Journal of Advanced Trends in Computer Science and Engineering  
ML can analyze vast volumes of data and through an ML model built from algorithms, ML can learn, recognize and produce accurate results and analysis.  ...  The result brings meaningful insights for water asset management specifically to strategize the optimal solution based on the forecast or prediction.  ...  A critical indicator reflecting a dam operation is the displacement monitoring and forecasting. A research proposed a model that applies GRP-based model [20] .  ... 
doi:10.30534/ijatcse/2020/56922020 fatcat:axnx7euckndk5il3lqyo3dtckq

Makine Öğrenimi Kullanarak Aylık Akarsu Akışı Tahmini

2020 Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi  
For the machine learning model, Support Vector Machines (SVM), Adaptive Boosting (AdaBoost), K-Nearest Neighbours (KNN) and Random Forest algorithms were considered and compared.  ...  Based on the test scores of the considered models with the hyperparameters, Random Forest based model outperforms all other models.  ...  Recently, several machine learning-based algorithms such as Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Adaptive Boosting (AdaBoost), and Random Forest have been proposed to solve the complex  ... 
doi:10.18185/erzifbed.780477 fatcat:cxfjobnorzdxdois72mjims5m4

Forecasting Peak and Appliance Level Demand Using Smart Meter Data

R. Bhavya M., U. Vasuprada, Dr. Azra Nasreen
2021 Zenodo  
In this paper, a machine learning model using the support vector machine is used to predict consumer's electricity peak demand usage, and a hybrid model comprising multilayer perceptron with k-means is  ...  The proposed SVM model performs better than the univariate ARIMA model by considering external features that affect electricity consumption.  ...  Machine learning models like Extreme Gradient boosting (XGBoost), Categorical boosting, and light gradient boosting are applied on smart meter data to inspect the power theft problems which can cause a  ... 
doi:10.5281/zenodo.4925862 fatcat:gzmaulqz65bbnekpzsw3t6rc3a

Flood Prediction Using Machine Learning Models: Literature Review

Amir Mosavi, Pinar Ozturk, Kwok-wing Chau
2018 Water  
on the various ML algorithms used in the field.  ...  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  ...  SVM is greatly popular in flood modeling; it is a supervised learning machine which works based on the statistical learning theory and the structural risk minimization rule.  ... 
doi:10.3390/w10111536 fatcat:5ewkgi4oibbkhn7nievusbyrim

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Melissa R. Allen-Dumas, Haowen Xu, Kuldeep R. Kurte, Deeksha Rastogi
2021 Frontiers in Water  
However, the effective implementation of such an approach requires the collection and curation of large amounts of disparate data, and reliable methods for modeling processes that may be co-evolutionary  ...  We also describe a vision that integrates these machine learning applications into a comprehensive watershed-to-community planning workflow for smart-cities management of urban water resources.  ...  based on SVM and ANN algorithms using Landsat 8 data.  ... 
doi:10.3389/frwa.2020.562304 fatcat:4g4x5qsljva63fzfibqyjhsdsi

A Review on the Application of Machine Learning Methods in Tropical Cyclone Forecasting

Zhen Wang, Jun Zhao, Hong Huang, Xuezhong Wang
2022 Frontiers in Earth Science  
In addition, the various predictors and advanced algorithm models are comprehensively summarized.  ...  explored in recent ML studies, which provides a new strategy to solve the difficulties in TC forecasting.  ...  AUTHOR CONTRIBUTIONS HH and ZW contributed to conception and design of the review. ZW combed through research findings and wrote the first draft of the manuscript.  ... 
doi:10.3389/feart.2022.902596 doaj:44f6dfdd2d0c4b6692785353fddd4a4b fatcat:xwuh5fcgzbhlna2afbchrsbru4

Application of machine learning algorithms for flood susceptibility assessment and risk management

R. Madhuri, S. Sistla, K. Srinivasa Raju
2021 Journal of Water and Climate Change  
In this study, five machine learning (ML) algorithms, namely (i) Logistic Regression, (ii) Support Vector Machine, (iii) K-nearest neighbor, (iv) Adaptive Boosting (AdaBoost) and (v) Extreme Gradient Boosting  ...  A geo-spatial database, with eight flood influencing factors, namely, rainfall, elevation, slope, distance from nearest stream, evapotranspiration, land surface temperature, normalised difference vegetation  ...  K-nearest neighbors KNN is a supervised, classification algorithm that works by first storing all the data and classifying new data points based on distance functions with respect to the stored data.  ... 
doi:10.2166/wcc.2021.051 fatcat:dtkvykhmvrbydouph6gtwpftee

Detecting BHP Flood Attacks in OBS Networks: A Machine Learning Prospective

2019 International Journal of Science and Applied Information Technology  
The learned model can then be utilized to single out (classify) misbehaving edge nodes based on their future performance as accurately as possible, hence disciplining them.  ...  Regression, and Support Vector Machine-Sequential Minimal Optimization (SVM-SMO) on a real dataset to identify the method(s) most appropriate to combat the BHP flood attack problem in OBS networks.  ...  The below steps clarify how Boosting algorithms, such as AdaBoost [10, 34] , work: 1) Select a base ML algorithm for learning such as a rule based classifier 2) The base algorithm learns a weak classifier  ... 
doi:10.30534/ijsait/2019/26862019 fatcat:2rhntwiwcnga7grr2cfhlqo6be
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