Comparing machine-learning-based black box techniques and white box models to predict rainfall-runoff in a northern area of Iraq, the Little Khabur River

Sayed, Biju Theruvil; Al-Mohair, Hani K.; Alkhayyat, Ahmed; Ramírez‐Coronel, Andrés Alexis; Elsahabi, Mohamed

doi:10.2166/wst.2023.014

Cited by 21 publications

(2 citation statements)

References 29 publications

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“…In essence, XAI serves as a pivotal bridge, bridging the gap between the inherent complexity of advanced AI models and the imperative for human comprehensibility. Its role extends beyond technical nuances, fostering trust, acceptance, and ethical deployment of artificial intelligence by ensuring that AI systems are not perceived as black boxes [27] but as tools that can be understood and scrutinized by a wider audience.…”

Section: Explainable Artificial Intelligence (Xai)mentioning

confidence: 99%

Advancing Skin Cancer Diagnosis through the Comparison of SHAP and Layer-wise Relevance Propagation (LRP)

ÖZKURT

2024

Preprint

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Skin cancer detection stands at the intersection of cutting-edge technology and critical healthcare needs. In this study, we employ a Convolutional Neural Network (CNN) on the HAM10000 dataset, showcasing commendable accuracy. However, the inherent opacity of deep learning models sparks a quest for interpretability. We introduce SHAP (SHapley Additive exPlanations) and Layerwise analysis to unravel the decision-making intricacies. SHAP provides a global spotlight on feature importance, while Layerwise analysis offers a step-by-step exploration of the model's learning process. Beyond model performance, our goal is to inject transparency, trust, and innovation into skin cancer diagnosis, paving the way for a novel approach to humanizing artificial intelligence decisions in healthcare. Future work includes testing interpretation methods on diverse datasets and applying them to other healthcare domains for a transformative impact. This study not only contributes to understanding AI decisions in skin cancer diagnosis but also introduces methodologies that may revolutionize interpretability in healthcare AI applications.

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Section: Explainable Artificial Intelligence (Xai)mentioning

confidence: 99%

Advancing Skin Cancer Diagnosis through the Comparison of SHAP and Layer-wise Relevance Propagation (LRP)

ÖZKURT

2024

Preprint

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“…The process known as rainfall-runoff is one of the most complex hydrological phenomena, however, when historical meteorological information is available, the simulation results are very useful for flood control and water resource planning (Rad et al, 2022;Sayed et al, 2023). Therefore, the hydrological modeling applied to the La Leche basin, was of the rainfall-runoff type, using the HEC-HMS software (Figure 6).…”

Section: Hydrological Modelingmentioning

confidence: 99%

Generation of Flows Applying a Simple Method of Flood Routing to Monthly Level in La Leche Basin, Peru

Máximo,

Villegas,

Guillermo

et al. 2024

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Use of Artificial Intelligence Modelling for the Dynamic Simulation of Urban Catchment Runoff

Balacumaresan,

Imteaz,

Aziz

et al. 2024

Water Resour Manage

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The complex topography and inherent nonlinearity affiliated with influential hydrological processes of urban catchments, coupled with limited availability of measured data, limits the prediction accuracy of conventional models. Artificial Neural Network models (ANNs) have displayed commendable progress in recognising and simulating highly complex, non-linear associations allied with input-output variables, with limited comprehension of the underlying physical processes. Therefore, this paper investigates the effectiveness and accuracy of ANN models, in estimating the urban catchment runoff, employing minimal and commonly available hydrological data variables – rainfall and upstream catchment flow data, employing two powerful supervised-learning-algorithms, Bayesian-Regularization (BR) and Levenberg-Marquardt (LM). Gardiners Creek catchment, encompassed in Melbourne, Australia, with more than thirty years of quality-checked rainfall and streamflow data was chosen as the study location. Two significant storm events that transpired within the last fifteen years - the 4th of February 2011 and the 6th of November 2018, were nominated for calibration and validation of the ANN model. The study results advocate that the use of the LM-ANN model stipulates accurate estimates of the historical storm events, with a stronger correlation and lower generalisation error, in contrast to the BR-ANN model, while the integration of upstream catchment flow alongside rainfall, vindicate for their collective impact upon the dynamics of the flow being spawned at the downstream catchment locations, significantly enhancing the model performance and providing a more cost-effective and near-realistic modelling approach that can be considered for application in studies of urban catchment responses, with limited data availability.

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Comparing machine-learning-based black box techniques and white box models to predict rainfall-runoff in a northern area of Iraq, the Little Khabur River

Cited by 21 publications

References 29 publications

Advancing Skin Cancer Diagnosis through the Comparison of SHAP and Layer-wise Relevance Propagation (LRP)

Advancing Skin Cancer Diagnosis through the Comparison of SHAP and Layer-wise Relevance Propagation (LRP)

Generation of Flows Applying a Simple Method of Flood Routing to Monthly Level in La Leche Basin, Peru

Use of Artificial Intelligence Modelling for the Dynamic Simulation of Urban Catchment Runoff

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