Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall

Pham, Quoc Bao; Abba, S.I.; Usman, A. G.; Linh, Nguyễn Thị Thùy; Gupta, Vivek; Malik, Anurag; Costache, Romulus; Vo, Ngoc Duong; Tri, Doan Quang

doi:10.1007/s11269-019-02408-3

Cited by 112 publications

(55 citation statements)

References 38 publications

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“…MLP is a reliable tool that is capable of handling the non-linearity of a data set [10,11]. Just like other ANNs, the architecture of MLP consists of an output layer, a hidden layer and an input layer [12,13]. Generally, the signals are processed; they are transferred to the output layer via the input layer through sequential mathematical processes with the help of biases and weights.…”

Section: Multi-layer Perceptron (Mlp) Neural Networkmentioning

confidence: 99%

“…Whereby, it gives a flexible use of various parameters and (1) variables, and ensures that the basic and primary knowledge regarding the process of physical characteristics [16]. HW provides more satisfactory performance in various applications as compared to linear systems like the MLR and the non-linear systems such as ANN, since it considered the dual properties of the data set of both the non-linearity and linear properties of the data set [13]. The general structure of HW model composed of the application of a black-box process, which is developed purposely to predict the non-linearity [16].…”

Section: Hammerstein-wiener Model (Hw)mentioning

confidence: 99%

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Advanced chromatographic technique for performance simulation of anti-Alzheimer agent: an ensemble machine learning approach

et al. 2020

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This work employs the application of three artificial intelligence (AI) techniques namely; support vector machine (SVM), Hammerstein-Wiener (HW) and multi-layer perceptron (MLP) for predicting the qualitative properties of an anti-Alzheimer agent using high-pressure liquid chromatography technique. The mobile phase (inform of acetonitrile and trifluoroacetic acid) and the column temperature was used as the predictors in modelling the maximum retention time (tR-max) and resolution (Resol.) as the output variables of the analyte. The measured and predicted values were checked using three performance indices including; Nash-Sutcliffe efficiency (NSE), correlation coefficient (CC) as the goodness of fits and a statistical error inform of root-mean-square error (RMSE). The results obtained demonstrated the promising ability of AI-based models in modelling the qualitative properties of the anti-Alzheimer agent. Observation of different outputs of the AI-based models at various time intervals showed the necessity of ensembling the outputs of the AI-based models. Therefore, simple average ensemble and support vector machine ensemble (SVM-E) were employed to enhance the performance skills of the simple models. The comparative performance of SVM-E inform of NSE indicated its ability in boosting and enhancing the performance skills of the single models SVM, MLP and HW models up to 5, 13 and 20% respectively in the testing stage for modelling tR-max.

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Section: Multi-layer Perceptron (Mlp) Neural Networkmentioning

confidence: 99%

Section: Hammerstein-wiener Model (Hw)mentioning

confidence: 99%

Advanced chromatographic technique for performance simulation of anti-Alzheimer agent: an ensemble machine learning approach

et al. 2020

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“…The need for understanding the science and nature of data is paramount in chemometrics [22‐26]. In this work, two different nonlinear AI‐based models and a classical linear model, namely ANFIS, FFNN, and MLR, were employed in order to predict the retention time (tR) of a bioactive component of C. sativum (isoquercetin).…”

Section: Methodsmentioning

confidence: 99%

Chemometrics‐based models hyphenated with ensemble machine learning for retention time simulation of isoquercitrin in Coriander sativum L. using high‐performance liquid chromatography

Usman

Işık

Abba

et al. 2021

J of Separation Science

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In this research, two nonlinear models, namely; adaptive neuro‐fuzzy inference system and feed‐forward neural network and a classical linear model were employed for the prediction of retention time of isoquercitrin in Coriander sativum L. using the high‐performance liquid chromatography technique. The prediction employed the use of composition of mobile phase and pH as the corresponding input parameters. The performance indices of the models were evaluated using root mean square error, determination co‐efficient, and correlation co‐efficient. The results obtained from the simple models showed that subclustering‐adaptive‐neuro fuzzy inference system gave the best results in both the training and testing phases and boosted the performance accuracy of the simple models. The overall comparison of the results showed that subclustering‐adaptive‐neuro fuzzy inference system ensemble demonstrated outstanding performance and increased the accuracy of the single models and ensemble models in the testing phase, up to 35% and 3%, respectively.

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“…Consequently, mathematical models, e.g., machine learning (ML)-based models that can account for high dimensions and nonlinearity, are required to improve prediction accuracy. Numerous ML models have been successfully applied in related research on rainfall retrievals and estimations, such as [ 48 , 49 , 50 , 51 , 52 , 53 , 54 ]. XGB is a popular ML algorithm developed by Chen and Guestrin [ 55 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan

Wei

Hsu

2021

Sensors

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This study developed a real-time rainfall forecasting system that can predict rainfall in a particular area a few hours before a typhoon’s arrival. The reflectivity of nine elevation angles obtained from the volume coverage pattern 21 Doppler radar scanning strategy and ground-weather data of a specific area were used for accurate rainfall prediction. During rainfall prediction and analysis, rainfall retrievals were first performed to select the optimal radar scanning elevation angle for rainfall prediction at the current time. Subsequently, forecasting models were established using a single reflectivity and all elevation angles (10 prediction submodels in total) to jointly predict real-time rainfall and determine the optimal predicted values. This study was conducted in southeastern Taiwan and included three onshore weather stations (Chenggong, Taitung, and Dawu) and one offshore weather station (Lanyu). Radar reflectivities were collected from Hualien weather surveillance radar. The data for a total of 14 typhoons that affected the study area in 2008–2017 were collected. The gated recurrent unit (GRU) neural network was used to establish the forecasting model, and extreme gradient boosting and multiple linear regression were used as the benchmarks. Typhoons Nepartak, Meranti, and Megi were selected for simulation. The results revealed that the input data set merged with weather-station data, and radar reflectivity at the optimal elevation angle yielded optimal results for short-term rainfall forecasting. Moreover, the GRU neural network can obtain accurate predictions 1, 3, and 6 h before typhoon occurrence.

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Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall

Cited by 112 publications

References 38 publications

Advanced chromatographic technique for performance simulation of anti-Alzheimer agent: an ensemble machine learning approach

Advanced chromatographic technique for performance simulation of anti-Alzheimer agent: an ensemble machine learning approach

Chemometrics‐based models hyphenated with ensemble machine learning for retention time simulation of isoquercitrin in Coriander sativum L. using high‐performance liquid chromatography

Real-Time Rainfall Forecasts Based on Radar Reflectivity during Typhoons: Case Study in Southeastern Taiwan

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