Predicting Bulk Average Velocity with Rigid Vegetation in Open Channels Using Tree-Based Machine Learning: A Novel Approach Using Explainable Artificial Intelligence

Meddage, D. P. P.; Ekanayake, I.U.; Herath, Sumudu; Gobirahavan, R.; Muttil, Nitin; Rathnayake, Upaka

doi:10.3390/s22124398

Cited by 20 publications

(3 citation statements)

References 74 publications

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“…In the presented analysis, only the most important predictors of streamflow were taken into account. Their selection can be examined in detail thanks to the fact that all of the tested models belong to the Explainable Artificial Intelligence category [54]. This factor determines the advantage of models such as XGBoost, LGBoost or CatBoost over recursive models of the LSTM type, which give slightly better forecasts, but still are difficult to interpret due to their black-box nature.…”

Section: Discussionmentioning

confidence: 99%

Daily Streamflow Forecasting in Mountainous Catchment Using XGBoost, LightGBM and CatBoost

Szczepanek

2022

Hydrology

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Streamflow forecasting in mountainous catchments is and will continue to be one of the important hydrological tasks. In recent years machine learning models are increasingly used for such forecasts. A direct comparison of the use of the three gradient boosting models (XGBoost, LightGBM and CatBoost) to forecast daily streamflow in mountainous catchment is our main contribution. As predictors we use daily precipitation, runoff at upstream gauge station and two-day preceding observations. All three algorithms are simple to implement in Python, fast and robust. Compared to deep machine learning models (like LSTM), they allow for easy interpretation of the significance of predictors. All tested models achieved Nash-Sutcliffe model efficiency (NSE) in the range of 0.85–0.89 and RMSE in the range of 6.8–7.8 m3s−1.A minimum of 12 years of training data series is required for such a result. The XGBoost did not turn out to be the best model for the daily streamflow forecast, although it is the most popular model. Using default model parameters, the best results were obtained with CatBoost. By optimizing the hyperparameters, the best forecast results were obtained by LightGBM. The differences between the model results are much smaller than the differences within the models themselves when suboptimal hyperparameters are used.

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Section: Discussionmentioning

confidence: 99%

Daily Streamflow Forecasting in Mountainous Catchment Using XGBoost, LightGBM and CatBoost

Szczepanek

2022

Hydrology

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“…SHAP reframes the Shapley value problems on how members of a coalition contribute to a coalition value. SHAP considers the contribution of every individual feature which influences the target output 44 .…”

Section: Methods and Datamentioning

confidence: 99%

Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface

Kulasooriya,

Ranasinghe,

Perera

et al. 2023

Sci Rep

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This study investigated the importance of applying explainable artificial intelligence (XAI) on different machine learning (ML) models developed to predict the strength characteristics of basalt-fiber reinforced concrete (BFRC). Even though ML is widely adopted in strength prediction in concrete, the black-box nature of predictions hinders the interpretation of results. Among several attempts to overcome this limitation by using explainable AI, researchers have employed only a single explanation method. In this study, we used three tree-based ML models (Decision tree, Gradient Boosting tree, and Light Gradient Boosting Machine) to predict the mechanical strength characteristics (compressive strength, flexural strength, and tensile strength) of basal fiber reinforced concrete (BFRC). For the first time, we employed two explanation methods (Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME)) to provide explanations for all models. These explainable methods reveal the underlying decision-making criteria of complex machine learning models, improving the end user's trust. The comparison highlights that tree-based models obtained good accuracy in predicting strength characteristics yet, their explanations were different either by the magnitude of feature importance or the order of importance. This disagreement pushes towards complicated decision-making based on ML predictions which further stresses (1) extending XAI-based research in concrete strength predictions, and (2) involving domain experts to evaluate XAI results. The study concludes with the development of a “user-friendly computer application” which enables quick strength prediction of basalt fiber reinforced concrete (BFRC).

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“…Sensitivity analysis identified vegetation height as the most critical variable in predicting flow velocity. Meddage et al 38 proposed models using tree-based ML models (Decision Tree, Extra Trees, XGBoost) to predict bulk-average velocity and surface layer friction factor (fS), with SHAP for interpretation. Existing regression models, despite accuracy, lack feature importance and causality insights.…”

Section: Introductionmentioning

confidence: 99%

AI-driven predictions of geophysical river flows with vegetation

Kumar,

Agarwal,

Deshpande

et al. 2024

Sci Rep

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In river research, forecasting flow velocity accurately in vegetated channels is a significant challenge. The forecasting performance of various independent and hybrid machine learning (ML) models are thus quantified for the first time in this work. Utilizing flow velocity measurements in both natural and laboratory flume experiments, we assess the efficacy of four distinct standalone machine learning techniques—Kstar, M5P, reduced error pruning tree (REPT) and random forest (RF) models. In addition, we also test for eight types of hybrid ML algorithms trained with an Additive Regression (AR) and Bagging (BA) (AR-Kstar, AR-M5P, AR-REPT, AR-RF, BA-Kstar, BA-M5P, BA-REPT and BA-RF). Findings from a comparison of their predictive capabilities, along with a sensitivity analysis of the influencing factors, indicated: (1) Vegetation height emerged as the most sensitive parameter for determining the flow velocity; (2) all ML models displayed outperforming empirical equations; (3) nearly all ML algorithms worked optimal when the model was built using all of the input parameters. Overall, the findings showed that hybrid ML algorithms outperform regular ML algorithms and empirical equations at forecasting flow velocity. AR-M5P (R2 = 0.954, R = 0.977, NSE = 0.954, MAE = 0.042, MSE = 0.003, and PBias = 1.466) turned out to be the optimal model for forecasting of flow velocity in vegetated-rivers.

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Predicting Bulk Average Velocity with Rigid Vegetation in Open Channels Using Tree-Based Machine Learning: A Novel Approach Using Explainable Artificial Intelligence

Cited by 20 publications

References 74 publications

Daily Streamflow Forecasting in Mountainous Catchment Using XGBoost, LightGBM and CatBoost

Daily Streamflow Forecasting in Mountainous Catchment Using XGBoost, LightGBM and CatBoost

Modeling strength characteristics of basalt fiber reinforced concrete using multiple explainable machine learning with a graphical user interface

AI-driven predictions of geophysical river flows with vegetation

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