Development of an Extreme Gradient Boosting Model Integrated With Evolutionary Algorithms for Hourly Water Level Prediction

Abstract

The establishment of reliable water level prediction models is vital for urban flood control and planning. In this paper, we develop hybrid models (GA-XGBoost and DE-XGBoost) that couple two evolutionary models, a genetic algorithm (GA) and a differential evolution (DE) algorithm, with the extreme gradient boosting (XGBoost) model for hourly water level prediction. The Jungrang urban basin located on the Han River, South Korea, was selected as a case study for the proposed models. Hourly rainfall and water level data were collected between 2003 and 2020 to construct and evaluate the performance of the selected models. To compare the prediction efficiency, two other tree-based models were chosen: classification and registration tree (CART) and random forest (RF) models. A comparison of the results showed that two hybrid models, GA-XGBoost and DE-XGBoost, outperformed RF and CART in the multistep-ahead prediction of water level, and the relative errors of the hybrid model ranged from [2.18%-9.21%], compared to [3.76%-10.41%] and [2.99%-11.88%] for the RF and CART, respectively. Reliable performance was also supported by other measures. In general, the GA-XGBoost and DE-XGBoost models displayed relatively similar performance despite their small differences. The CART model was not preferable for multistep-ahead water level predictions, even though it yielded the lowest Akaike information criterion (AIC) value. This study verifies that despite having some drawbacks when considering long step-ahead prediction and model complexity, hybrid XGBoost models might be superior to many existing models for hourly water level prediction.

Short-term streamflow modeling using data-intelligence evolutionary machine learning models

“…It’s important to highlight that DE and GA exhibited strongly non-significant differences. This observation aligns with the findings of Nguyen et al 98 , who compared the performances of the extreme gradient boosting model relative to two evolutionary algorithms: genetic algorithms and differential evolution, i.e., GA-XGB and DE-XGB. Their study revealed that these models also displayed similar results.…”

Daily Scale Streamflow Forecasting Based-Hybrid Gradient Boosting Machine Learning Model

“…The ndings revealed that the model was satisfactory in anticipating the arrival time of the oods. Nguyen et al [30] developed hybrid models by using two deeplearning models with XGBoost to forecast hourly water levels. The study veri ed that hybrid XGBoost models might be bene cial to many existing models for hourly water level prediction.…”

Artificial neural network for flood susceptibility mapping in Bangladesh

“…Research using machine learning techniques has gained popularity in flood prediction and monitoring. In some cases, machine learning approaches such as XGBoost [ 5 , 10 , 11 ], a tree-based supervised algorithm, have been used, as have KNN (K-Nearest Neighbours) [ 12 , 13 ]. The support vector machine (SVM) is commonly used in flood prediction models [ 14 ], and random forest and boosted tree models [ 15 , 16 ] have been used to assess flood susceptibility in recent years.…”