scholarly journals A Hybrid Intelligence Model for the Prediction of the Peak Flow of Debris Floods

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2246
Author(s):  
Mohammad Ebrahim Banihabib ◽  
Lubos Jurik ◽  
Mahsa Sheikh Kazemi ◽  
Jaber Soltani ◽  
Mitra Tanhapour
Keyword(s):  
Artificial Intelligence ◽  
Hybrid Model ◽  
Sediment Concentration ◽  
Peak Flows ◽  
Flood Peak ◽  
Flood Warning ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Debris Flood ◽  
Debris Floods

Debris floods, as one of the most significant natural hazards, often threaten the lives and property of many people worldwide. Predicting models are essential for flood warning systems to minimize casualties of debris floods. Since HEC-HMS (Hydrologic Engineering Center’s Hydrological Modelling System) cannot simulate debris flow, this study proposes a new hybrid model that uses artificial intelligence models to overcome HEC-HMS’s insufficiency in reflecting the sediment concentration effect on the debris floods. A sediment concentration is an effective factor for evaluating debris flood peak flows. This led to the proposal of new hybrid models for predicting the debris flood peak flows on the basis of hybridization of the artificial intelligence models (Bayesian Network (BN) and Support Vector Regression–Particle Swarm Optimization (SVR-PSO)) and HEC-HMS. To estimate the sediment concentration of floods by using the proposed artificial intelligence models, we nominated an average basin elevation, an average basin slope, a basin area, the current day rainfall, the antecedent rainfall of the past 3 days, and the streamflow of the previous day the previous day as the effective variables. In the validation stage, the average of the Mean Absolute Relative Error (MARE) of the estimated values were 0.024, 0.038, and 0.024 for the typical floods that occurred in the Navrood, Kasilian, and the Amameh basins in the north of Iran, respectively. Similarly, we obtained values of 0.038, 0.073, and 0.040 for the debris flood events for the three respective locations. After predicting the debris flood peak flows by the proposed hybrid HMS-BN and HMS-SVR-PSO models, the average of the MAREs for all debris flood events was reduced to 0.013 and 0.014, respectively. The comparison of MAREs of the examined hybrid models shows that the HMS-BN model results in higher accuracy than the HMS-SVR-PSO model in the prediction of the debris flood peak flows. Generally, the absolute error of prediction by the proposed hybrid model is reduced to one-third of the HEC-HMS. The prediction of the debris flood peak flows using the proposed hybrid model can be examined in the debris flood warning systems to reduce the potential damages and casualties in similar basins.

scholarly journals Hybrid artificial intelligence model for prediction of heating energy use

2021 ◽  
pp. 152-152
Author(s):  
Aleksandra Sretenovic ◽  
Radisa Jovanovic ◽  
Vojislav Novakovic ◽  
Natasa Nord ◽  
Branislav Zivkovic
Keyword(s):  
Artificial Intelligence ◽  
Hybrid Model ◽  
Energy Use ◽  
Linear Part ◽  
Hybrid Approach ◽  
Non Linear ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
The Individual ◽  
Hybrid Artificial Intelligence

Currently, in the building sector there is an increase in energy use due to the increased demand for indoor thermal comfort. Proper energy planning based on a real measurement data is a necessity. In this study, we developed and evaluated hybrid artificial intelligence models for the prediction of the daily heating energy use. Building energy use is defined by significant number of influencing factors, while many of them are hard to define and quantify. For heating energy use modelling, complex relationship between the input and output variables is not strictly linear nor non-linear. The main idea of this paper was to divide the heat demand prediction problem into the linear and the non-linear part (residuals) by using different statistical methods for the prediction. The expectations were that the joint hybrid model, could outperform the individual predictors. Multiple Linear Regression (MLR) was selected for the linear modelling, while the non-linear part was predicted using Feedforward (FFNN) and Radial Basis (RBFN) neural network. The hybrid model prediction consisted of the sum of the outputs of the linear and the non-linear model. The results showed that the hybrid FFNN model and the hybrid RBFN model achieved better results than each of the individual FFNN and RBFN neural networks and MLR on the same dataset. It was shown that this hybrid approach improved the accuracy of artificial intelligence models.

scholarly journals Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Albert T. Young ◽  
Kristen Fernandez ◽  
Jacob Pfau ◽  
Rasika Reddy ◽  
Nhat Anh Cao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Stress Testing ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Skin Lesions ◽  
Stress Tests ◽  
Performance Metric ◽  
Intelligence Models ◽  
Artificial Intelligence Models

AbstractArtificial intelligence models match or exceed dermatologists in melanoma image classification. Less is known about their robustness against real-world variations, and clinicians may incorrectly assume that a model with an acceptable area under the receiver operating characteristic curve or related performance metric is ready for clinical use. Here, we systematically assessed the performance of dermatologist-level convolutional neural networks (CNNs) on real-world non-curated images by applying computational “stress tests”. Our goal was to create a proxy environment in which to comprehensively test the generalizability of off-the-shelf CNNs developed without training or evaluation protocols specific to individual clinics. We found inconsistent predictions on images captured repeatedly in the same setting or subjected to simple transformations (e.g., rotation). Such transformations resulted in false positive or negative predictions for 6.5–22% of skin lesions across test datasets. Our findings indicate that models meeting conventionally reported metrics need further validation with computational stress tests to assess clinic readiness.

scholarly journals How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Xinran Wang ◽  
Liang Wang ◽  
Hong Bu ◽  
Ningning Zhang ◽  
Meng Yue ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Clinical Practice ◽  
Subjective Assessment ◽  
Current Guideline ◽  
Scoring Method ◽  
Programmed Death ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Scoring Accuracy

AbstractProgrammed death ligand-1 (PD-L1) expression is a key biomarker to screen patients for PD-1/PD-L1-targeted immunotherapy. However, a subjective assessment guide on PD-L1 expression of tumor-infiltrating immune cells (IC) scoring is currently adopted in clinical practice with low concordance. Therefore, a repeatable and quantifiable PD-L1 IC scoring method of breast cancer is desirable. In this study, we propose a deep learning-based artificial intelligence-assisted (AI-assisted) model for PD-L1 IC scoring. Three rounds of ring studies (RSs) involving 31 pathologists from 10 hospitals were carried out, using the current guideline in the first two rounds (RS1, RS2) and our AI scoring model in the last round (RS3). A total of 109 PD-L1 (Ventana SP142) immunohistochemistry (IHC) stained images were assessed and the role of the AI-assisted model was evaluated. With the assistance of AI, the scoring concordance across pathologists was boosted to excellent in RS3 (0.950, 95% confidence interval (CI): 0.936–0.962) from moderate in RS1 (0.674, 95% CI: 0.614–0.735) and RS2 (0.736, 95% CI: 0.683–0.789). The 2- and 4-category scoring accuracy were improved by 4.2% (0.959, 95% CI: 0.953–0.964) and 13% (0.815, 95% CI: 0.803–0.827) (p < 0.001). The AI results were generally accepted by pathologists with 61% “fully accepted” and 91% “almost accepted”. The proposed AI-assisted method can help pathologists at all levels to improve the PD-L1 assay (SP-142) IC assessment in breast cancer in terms of both accuracy and concordance. The AI tool provides a scheme to standardize the PD-L1 IC scoring in clinical practice.

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