Artificial Neural Network and Multiple Linear Regression for Flood Prediction in Mohawk River, New York

Tsakiri, Katerina; Marsellos, Antonios; Kapetanakis, Stelios

doi:10.3390/w10091158

Cited by 90 publications

(55 citation statements)

References 38 publications

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“…For example, using the multivariate discriminant analysis (MDA), classification and regression trees (CART), SVM [22], genetic algorithm rule-set production (GARP), quick unbiased efficient statistical tree (QUEST) [28], ANN [23], adaptive neuro fuzzy inference system (ANFIS) [55] and boosted regression trees (BRT) [9] methods, the researchers successfully predicted and mapped the distribution of flood susceptibilities within different regions of Iran. Similar results have also been reported from USA [56], Australia [57], China [58], Vietnam [6] and Romania [59]. Additionally, the literature consists of several successful experiments of using machine learning methods for the prediction of landslide [39], wildfire [50] and gully erosion [60].…”

Section: Discussionsupporting

confidence: 77%

Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

et al. 2019

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Floods are some of the most destructive and catastrophic disasters worldwide. Development of management plans needs a deep understanding of the likelihood and magnitude of future flood events. The purpose of this research was to estimate flash flood susceptibility in the Tafresh watershed, Iran, using five machine learning methods, i.e., alternating decision tree (ADT), functional tree (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), and quadratic discriminant analysis (QDA). A geospatial database including 320 historical flood events was constructed and eight geo-environmental variables—elevation, slope, slope aspect, distance from rivers, average annual rainfall, land use, soil type, and lithology—were used as flood influencing factors. Based on a variety of performance metrics, it is revealed that the ADT method was dominant over the other methods. The FT method was ranked as the second-best method, followed by the KLR, MLP, and QDA. Given a few differences between the goodness-of-fit and prediction success of the methods, we concluded that all these five machine-learning-based models are applicable for flood susceptibility mapping in other areas to protect societies from devastating floods.

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Section: Discussionsupporting

confidence: 77%

Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

et al. 2019

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“…The presented results, according to the resulting RMSE's, seem to produce smaller error values compared with works solving problems similar to the presented one (e.g., in [28] and [29] the used ANN's result RMSE values of about 0.44 and 0.15-0.36 m respectively), but those comparisons cannot be absolute, since the used datasets are totally different. In general, in problems as the presented one, each work focuses to a specific scope of the use of ANN's, e.g., in [29] it is mentioned that "the predictions accurately reproduced the rising and falling tendencies of water levels over time" and in [12] the authors focus in the forecasting of the water level, using previous waterlevel measurements; while in this work the authors focus on deploying a fast and accurate regression model.…”

Section: Discussionmentioning

confidence: 57%

A polynomial neural network for river’s water-level prediction

et al. 2020

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Flood risk prediction is based on collecting long time-series data using in situ sensors. As such, the consistency and quality of the hydrological flood models is subsequent to information gaps included in the aforementioned time series, which may cause issues that negatively affect the efficiency of the prediction. Herein, to cope with that problem, a novel polynomial neural network is developed, which utilizes Legendre polynomial activation functions and it is able to fill those gaps by predicting the river's water level by intertwining observations coming from different areas of the river, and weather data. The network is trained by a modified evolutionary computation algorithm, which is based on the wellknown artificial bee colony method. The experimental case study concerns the Kifisos river basin (Attica, Greece), where a number of sensors have been deployed to collect data. The network was compared to a feed-forward neural network in terms of the root mean square error performance index. Results indicated a superior performance of the proposed network obtaining predictions from 1 to 2 cm.Keywords Artificial neural network · Polynomial neural network · Water-level prediction · Flood prediction · Legendre Polynomials · Artificial bee colony Electronic supplementary material The online version of this article (https ://doi.org/10.1007/s4245 2-020-2328-9) contains supplementary material, which is available to authorized users.

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“…The model development required the training of the neural system, which depended on the training for data collection. In order to estimate the performance of the ANN model, the coefficient of selection, R 2 , and the Mean Square of Error (MSE) were used for the analysis [33]. The accuracy of the predicted treatment techniques was determined using the following Equations 1 and 2.…”

Section: Ann Modelmentioning

confidence: 99%

Comparative Study of Utilising Neural Network and Response Surface Methodology for Flexible Pavement Maintenance Treatments

Milad

Majeed²,

Yusoff

2020

Civ Eng J

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The use of Artificial Intelligence (AI) for the prediction of flexible pavement maintenance that is caused by distressing on the surface layer is crucial in the effort to increase the service life span of pavements as well as reduce government expenses. This study aimed to predict flexible pavement maintenance in tropical regions by using an Artificial Neural Network (ANN) and the Response Surface Methodology (RSM) for predicting models for pavement maintenance in the tropical region. However, to predict the performance of the treatment techniques for flexible pavements, we used critical criteria to choose our date from different sources to represent the situation of the current pavement. The effect of the distress condition on the flexible pavement surface performance was one of the criteria considered in our study. The data were chosen in this study for 288 sets of treatment techniques for flexible pavements. The input parameters used for the prediction were severity, density, road function, and Average Daily Traffic (ADT). The finding of regression models in (R2) values for the ANN prediction model is 0.93, while the (R2) values are (RSM) prediction model dependent on the full quadratic is 0.85. The results of two methods were compared for their predictive capabilities in terms of the coefficient of determination (𝑅2), the Mean Squared Error (MSE), and the Root Mean Square Error (RMSE), based on the dataset. The results showed that the prediction made utilizing ANN was very relevant to the goal in contrast to that made using the statistical program RSM based on different types of mathematical methods such as full quadratic, pure quadratic, interactions, and linear regression.

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Artificial Neural Network and Multiple Linear Regression for Flood Prediction in Mohawk River, New York

Cited by 90 publications

References 38 publications

Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

A polynomial neural network for river’s water-level prediction

Comparative Study of Utilising Neural Network and Response Surface Methodology for Flexible Pavement Maintenance Treatments

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