Integrated machine learning methods with resampling algorithms for flood susceptibility prediction

Dodangeh, Esmaeel; Choubin, Bahram; Eigdir, Ahmad Najafi; Nabipour, Narjes; Panahi, Mehdi; Shamshirband, Shahaboddin; Mosavi, Amir

doi:10.1016/j.scitotenv.2019.135983

Cited by 189 publications

(66 citation statements)

References 70 publications

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“…There is no straightforward guideline for splitting the training and testing data in machine learning modeling [38][39][40][41][42][43][44][45][46]. For instance, the study of Choubin [47] used a total of 63% of their data for model development, whereas Qasem et al, [48] utilized 67% of data, Asadi et al, [41], Samadianfard et al, [49,50], and Dodangeh et al, [51] used 70%, and Zounemat-Kermani et al, [52] implemented 80% of total data to develop their models. Thus, to develop the studied models for PE estimation, we divided the data into training (70%) and testing (30%).…”

Section: Resultsmentioning

confidence: 99%

Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

et al. 2020

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Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters.

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Section: Resultsmentioning

confidence: 99%

Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

et al. 2020

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“…It is a widely utilized validation technique for analyzing the predictive ability of a model [39]. A model is determined to be perfect, very good, good, moderate and poor if the AUC values were 1.0-0.9, 0.9-0.8, 0.8-0.7, 0.7-0.6 and 0.6-0.5, respectively [43].…”

Section: Plos Onementioning

confidence: 99%

Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models

et al. 2020

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Infectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID-19) has caused a global health emergency. Thus, identification of regions with high risk for COVID-19 outbreak and analyzing the behaviour of the infection is a major priority of the governmental organizations and epidemiologists worldwide. The aims of the present study were to analyze the risk factors of coronavirus outbreak for identifying the areas having high risk of infection and to evaluate the behaviour of infection in Fars Province, Iran. A geographic information system (GIS)-based machine learning algorithm (MLA), support vector machine (SVM), was used for the assessment of the outbreak risk of COVID-19 in Fars Province, Iran whereas the daily observations of infected cases were tested in the-polynomial and the autoregressive integrated moving average (ARIMA) models to examine the patterns of virus infestation in the province and in Iran. The results of the disease outbreak in Iran were compared with the data for Iran and the world. Sixteen effective factors were selected for spatial modelling of outbreak risk. The validation outcome reveals that SVM achieved an AUC value of 0.786 (March 20), 0.799 (March 29), and 86.6 (April 10) that displays a good prediction of outbreak risk change detection. The results of the third-degree polynomial and ARIMA models in the province revealed an increasing trend with an evidence of turning, demonstrating extensive quarantines has been effective. The general trends of virus infestation in Iran and Fars Province were similar, although a more volatile growth of the infected cases is expected in the province. The results of this study might assist better programming COVID-19 disease prevention and control and gaining sorts of predictive capability would have wide-ranging benefits.

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“…The data are collected by 3S technologies in a huge volume. Thus, artificial intelligence technologies (machine learning algorithms and deep learning algorithms) and big data technologies are applied to deal with data processing issues [12][13][14]. These new technologies have the ability to deal with data with a high non-linear correlation.…”

Section: Establishment Of An Early Warning System For Flood-hazardsmentioning

confidence: 99%

“…The established warning system can be used to predict future disasters, provide relevant design principles for infrastructure construction and reinforce and transform aging infrastructure in a timely manner. It will also provide scientific decision-making support for comprehensive natural hazards risk management and emergency plans for disaster prevention [14,[49][50][51][52][53]. For example, seasonal European early warning systems have been developed and have been running in a preoperational mode since mid-2018 under the EU-funded Enhancing Emergency Management and Response to Extreme Weather and Climate Events project [48].…”

Section: Establishment Of An Early Warning System For Flood-hazardsmentioning

confidence: 99%

“…Information science and artificial intelligence technologies can provide effective means of dealing with these data. Dodangeh et al [14] utilized machine learning methods to predict flood susceptibility. Thus, the use of 3S technologies integrated with information science and artificial intelligence technologies (ISAIT) to construct an EWS can increase the accuracy of hazard occurrence prediction.…”

Section: Introductionmentioning

confidence: 99%

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Lesson Learned from Catastrophic Floods in Western Japan in 2018: Sustainable Perspective Analysis

Lin

Zhang

et al. 2020

Water

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Natural hazards have a significant impact on the sustainable development of human society. This paper reports on the catastrophic floods in western Japan in 2018. Continuous rainfall resulted in catastrophic floods, leading to 212 deaths, damage to more than 2000 houses and 619 geological disasters in 31 prefectures. The causes and contributing factors of these catastrophic floods are analyzed. The analysis of the causes of typical natural hazards provides an important lesson for hazard prevention and management. To adapt to climate change and prevent natural hazards in the future, the preliminary investigation and sustainable perspective analysis in this paper suggest the importance of the construction of a spongy city and the establishment of an early warning system with the help of information science and artificial intelligence technologies (ISAIT); we also highlight the urgent need to improve and strengthen the management of infrastructure.

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Integrated machine learning methods with resampling algorithms for flood susceptibility prediction

Cited by 189 publications

References 70 publications

Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

Assessment of the outbreak risk, mapping and infection behavior of COVID-19: Application of the autoregressive integrated-moving average (ARIMA) and polynomial models

Lesson Learned from Catastrophic Floods in Western Japan in 2018: Sustainable Perspective Analysis

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