A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction

Khalaf, Mohammed; Hussain, Abir Jaafar; Al‐Jumeily, Dhiya; Baker, Thar; Keight, Robert; Lisboa, Paulo J. G.; Fergus, Paul; Kafri, Ala S. Al

doi:10.1109/cec.2018.8477904

Cited by 28 publications

(13 citation statements)

References 24 publications

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“…Machine learning has also been employed to forecast wind gusts (Sallis et al, 2011), severe hail (Gagne et al, 2017) and excessive rainfall (Nayak and Ghosh, 2013). In contrast, only a minor part of the body of literature focuses its attention on the identification or classification of events (Nayak and Ghosh, 2013, Khalaf et al, 2018, and Alipour et al, 2020Richman et al, 2016, for droughts;and Kim et al, 2019 for tropical cyclones). However, classification of events to distinguish between extreme and nonextreme events is essential to support the development of effective parametric risk transfer instruments.…”

Section: Introductionmentioning

confidence: 99%

The potential of machine learning for weather index insurance

Cesarini

Figueiredo

Monteleone

et al. 2021

Nat. Hazards Earth Syst. Sci.

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Abstract. Weather index insurance is an innovative tool in risk transfer for disasters induced by natural hazards. This paper proposes a methodology that uses machine learning algorithms for the identification of extreme flood and drought events aimed at reducing the basis risk connected to this kind of insurance mechanism. The model types selected for this study were the neural network and the support vector machine, vastly adopted for classification problems, which were built exploring thousands of possible configurations based on the combination of different model parameters. The models were developed and tested in the Dominican Republic context, based on data from multiple sources covering a time period between 2000 and 2019. Using rainfall and soil moisture data, the machine learning algorithms provided a strong improvement when compared to logistic regression models, used as a baseline for both hazards. Furthermore, increasing the amount of information provided during the training of the models proved to be beneficial to the performances, increasing their classification accuracy and confirming the ability of these algorithms to exploit big data and their potential for application within index insurance products.

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Section: Introductionmentioning

confidence: 99%

The potential of machine learning for weather index insurance

Cesarini

Figueiredo

Monteleone

et al. 2021

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…The internal linear prediction does not predict the signal; rather, it just computes the coefficients of the input signal to be transmitted. The transmission of LPCs of a signal requires less bandwidth and storage compared with the original signal, thus saving the useful bandwidth and memory space [4].…”

Section: Internal Linear Predictionmentioning

confidence: 99%

“…Different estimation techniques are used to extract the optimal information signal from a noise contaminated signal when both the information and noise signal and have overlapping spectrum. In [4], machine learning techniques are employed for prediction purpose. In literature the Kalman filter is one of the best estimation tools among them [5].…”

Section: Introductionmentioning

confidence: 99%

Compensation of Data Loss Using ARMAX Model in State Estimation for Control and Communication Systems Applications

et al. 2021

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Compensation of data loss in the state estimation plays an indispensable role in efficient and stable control and communication systems. However, accurate compensation of data loss in the state estimation is extremely challenging issue. To cater this challenging issue, two techniques such as the open-loop Kalman filter and the compensating closed-loop Kalman filter have emerged. The closed-loop technique compensates for the missing data using the autoregressive model. However, the autoregressive model used only past measurements for data loss compensation. Considering only one parameter, i.e., the past measurements, is insufficient and leads to inaccurate state estimation. Thus, in this work, autoregressive moving average with exogenous inputs model considers three parameters, i.e., the past measurements, the input signal, and the sensor noise, simultaneously to compensate data loss in state estimation. To endorse the effectiveness and applicability of the proposed model, a standard mass-spring-damper is employed in the case study. Simulation results show that the proposed model outperforms the existing autoregressive models in terms of performance parameters.

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“…The authors used previous seasons as a training dataset and a single season as a testing dataset. Naïve Bayes algorithm is also applied as an underlying classifier for predictions in [18], whereas in [19], the optimal results of prediction are achieved with random forest classifier. The authors in [20] describe the developed Hybrid Fuzzy Support Vector Machine (HFSVM) model for analyzing the outcomes of basketball competitions.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Selecting a Validation Method in Machine Learning on Predicting Basketball Game Outcomes

2020

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Interest in sports predictions as well as the public availability of large amounts of structured and unstructured data are increasing every day. As sporting events are not completely independent events, but characterized by the influence of the human factor, the adequate selection of the analysis process is very important. In this paper, seven different classification machine learning algorithms are used and validated with two validation methods: Train&Test and cross-validation. Validation methods were analyzed and critically reviewed. The obtained results are analyzed and compared. Analyzing the results of the used machine learning algorithms, the best average prediction results were obtained by using the nearest neighbors algorithm and the worst prediction results were obtained by using decision trees. The cross-validation method obtained better results than the Train&Test validation method. The prediction results of the Train&Test validation method by using disjoint datasets and up-to-date data were also compared. Better results were obtained by using up-to-date data. In addition, directions for future research are also explained. Symmetry 2020, 12, 431 2 of 15The aim of this paper is, through the comparison of the classification machine learning algorithms in predicting basketball game outcomes, to define which algorithm, validation method, and data preparation method produces better prediction results.This paper demonstrates what impact the different validation methods have on the prediction accuracy when using different ML algorithms. Moreover, the impact of selecting a validation method on the prediction results when applying ML to the disjoint datasets or the up-to-date data is revealed, thereby enabling the formation of recommendations for the most appropriate combination of the ML algorithm and validation method, depending on the available datasets.After this introduction and the overview of sport outcome-related researches, the second chapter provides the basic information about classification machine learning algorithms and the validation methods applied in this research. The third chapter describes the data acquisition and data preparation procedures. The research results are presented and discussed in the fourth chapter, and the conclusions are given at the end of paper. Related Literature ReviewThe most common algorithm in predicting outcomes in sports are neural networks coupled with the Train&Test validation method. The authors of [1] used a variety of neural networks and Train&Test validation for predicting game outcomes in the National Basketball Association (NBA) league, with the best results of more than 70%. In [2], the authors used 37 algorithms in the Waikato Environment for Knowledge Analysis (WEKA) and Train&Test validation method. The result with the best yield was 72.8%, showing that the best classifiers have 5% better precision than the referent classifier, which favors the team with the better rating. The authors of [3] used logistic regression, Naïve Bayes, Support Vector Machine (S...

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A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction

Cited by 28 publications

References 24 publications

The potential of machine learning for weather index insurance

The potential of machine learning for weather index insurance

Compensation of Data Loss Using ARMAX Model in State Estimation for Control and Communication Systems Applications

The Impact of Selecting a Validation Method in Machine Learning on Predicting Basketball Game Outcomes

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