Development of machine learning models for short-term water level forecasting

The impact of precise river flood forecasting and warnings in preventing potential victims along with promoting awareness and easing evacuation is realized in the reduction of flood damage and avoidance of loss of life. Machine learning models have been used widely in flood forecasting through discharge. However the usage of discharge can be inconvenient in terms of issuing a warning since discharge is not the direct measure for the early warning system. This paper focuses on water level prediction on the Storå River, Denmark utilizing several machine learning models. The study revealed that the transformation of features to follow a Gaussian-like distribution did not improve the prediction accuracy further. Additional data through different feature sets resulted in increased prediction performance of the machine learning models. Using a hybrid method for the feature selection improved the prediction performance as well. The Feed-Forward Neural Network gave the lowest mean absolute error and highest coefficient of determination value. The results indicated the difference in prediction performance in terms of mean absolute error term between the Feed-Forward Neural Network and the Multiple Linear Regression model was 0.003 cm. It was concluded that the Multiple Linear Regression model would be a good alternative when time, resources, or expert knowledge is limited

Wavelet discrete transform, ANFIS and linear regression for short-term time series prediction of air temperature

This paper investigates the ability of Discrete Wavelet Transform and Adaptive Network-Based Fuzzy Inference System in time-series data modeling of weather parameters. Plotting predicted data results on Linear Regression is used as the baseline of the statistical model. Data were tested in every 10 minutes interval on weather station of Bungus port in Padang, Indonesia. Mean absolute errors (MAE), the coefficient of determination (R2), Pearson correlation coefficient (r) and root mean squared error (RMSE) are used as performance indicators. The result of Plotting ANFIS data against linear regression using 1-input data is the optimal values combination of output predictions

Prediction of player position for talent identification in association netball: a regression-based approach

Among the challenges in industrial revolutions, 4.0 is managing organizations’ talents, especially to ensure the right person for the position can be selected. This study is set to introduce a predictive approach for talent identification in the sport of netball using individual player qualities in terms of physical fitness, mental capacity, and technical skills. A data mining approach is proposed using three data mining algorithms, which are Decision Tree (DT), Neural Network (NN), and Linear Regressions (LR). All the models are then compared based on the Relative Absolute Error (RAE), Mean Absolute Error (MAE), Relative Square Error (RSE), Root Mean Square Error (RMSE), Coefficient of Determination (R2), and Relative Square Error (RSE). The findings are presented and discussed in light of early talent spotting and selection. Generally, LR has the best performance in terms of MAE and RMSE as it has the lowest values among the three models

An Artificial Intelligence-Based Noninvasive Solution to Estimate Pulmonary Artery Pressure

Aims: Design to develop an artificial intelligence (AI) algorithm to accurately predict the pulmonary artery pressure (PAP) waveform using non-invasive signal inputs. Methods and results: We randomly sampled training, validation, and testing datasets from a waveform database containing 180 patients with pulmonary atrial catheters (PACs) placed for PAP waves collection. The waveform database consisted of six hemodynamic parameters from bedside monitoring machines, including PAP, artery blood pressure (ABP), central venous pressure (CVP), respiration waveform (RESP), photoplethysmogram (PPG), and electrocardiogram (ECG). We trained a Residual Convolutional Network using a training dataset containing 144 (80%) patients, tuned learning parameters using a validation set including 18 (10%) patients, and tested the performance of the method using 18 (10%) patients, respectively. After comparing all multi-stage algorithms on the testing cohort, the combination of the residual neural network model and wavelet scattering transform data preprocessing method attained the highest coefficient of determination R2 of 90.78% as well as the following other performance metrics and corresponding 95% confidence intervals (CIs): mean square error of 11.55 (10.22–13.5), mean absolute error of 2.42 (2.06–2.85), mean absolute percentage error of 0.91 (0.76–1.13), and explained variance score of 90.87 (85.32–93.31). Conclusion: The proposed analytical approach that combines data preprocessing, sampling method, and AI algorithm can precisely predict PAP waveform using three input signals obtained by noninvasive approaches

Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection.

Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan

Determination of impact parameter for CEE with Digi-input neural networks

Impact parameter is an important quantity which characterizes the centrality in nucleus-nucleus collision geometry. The determination of impact parameter in real experiments takes use of the hits on detector system or the reconstructed tracks of the secondary particles. As a task of feature recognition, methods such as sharp cut-off, Bayesian methods and Neural Networks (NN) has been studied and applied. However, in the situation of the Cooler-storage-ring External-target Experiment (CEE), the low beam energy brings a lapse of dependency between impact parameter and charged particle multiplicity, which decreases the validity of the explicit determination methods. This work proposes a regressor constructed with Graph Attention neural network, which takes the hit-level data as input. This model has shown a mean absolute error of 0.496 fm for the IQMD collision data of the UU system at 0.5 AMeV. The performance of such a model is compared with reference models, showing its capacity in handling the original but potentially interrelated digi information.Comment: 13 pages, 9 figure

A comparative study between mathematical models and the ANN data mining technique in draft force prediction of disk plow implement in clay loam soil

This paper communicates the prediction of required draft force of disk plow implement during tillage operations. The well-known mathematical model proposed by American Society of Agricultural and Biological Engineers (ASABE), multiple linear regression (MLR) and data mining model, based on artificial neural network (ANN), were employed for this purpose. The input variables of the models were considered as forward speed of 2-6 (km/h) and plowing depth of 10-30 (cm). The development details of the models are documented in the paper. On account of statistical performance criteria, the best ANN model with coefficient of determination of 0.971, root mean square error of 0.762 (kN), mean absolute percentage error of 1.886 (%) and mean value of absolute prediction residual errors of 0.968 (kN) was better performed than ASABE and MLR models for prediction of required draft force. The ANN modeling results also showed that the simultaneous or individual increment of forward speed and plowing depth caused nonlinear increment of draft force. The well-developed ANN model is considered operational to predict draft force as an essential step toward proper selection of combination of tractor and disk plow implement

Neural Network Model Development with Soft Computing Techniques for Membrane Filtration Process

Membrane bioreactor employs an efficient filtration technology for solid and liquid separation in wastewater treatment process. Development of membrane filtration model is significant as this model can be used to predict filtration dynamic which is later utilized in control development. Most of the available models only suitable for monitoring purpose, which are too complex, required many variables and not suitable for control system design. This work focusing on the simple time seris model for membrane filtration process using neural network technique. In this paper, submerged membrane filtration model developed using recurrent neural network (RNN) train using genetic algorithm (GA), inertia weight particle swarm optimization (IW-PSO) and gravitational search algorithm (GSA). These optimization algorithms are compared in term of its accuracy and convergent speed in updating the weights and biases of the RNN for optimal filtration model. The evaluation of the models is measured using three performance evaluations, which are mean square error (MSE), mean absolute deviation (MAD) and coefficient of determination (R2). From the results obtained, all methods yield satisfactory result for the model, with the best results given by IW-PSO