Liyao Peng
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Liyao Peng has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Chenzhihao Qian
Laboratory Cultivation Base of Environment Process and Digital Simulation, Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing 100048, China
1 shared publication - Jian Tong
School of Civil Engineering, Wuhan Huaxia Institute of Technology, Wuhan 430223, China
1 shared publication - Jiemin Fu
Hydrology and Water Resources Information Center of Fenghua District, Ningbo 315502, China
1 shared publication - Jincan Wang
School of Earth Sciences, China University of Geosciences (Wuhan), Wuhan 430079, China
1 shared publication - Xiang Wang
Ningbo Ecological and Environmental Monitoring Center of Zhejiang Province, Ningbo 315048, China
1 shared publication - Yanbin Yuan
School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China
1 shared publication
Latest Publications
- Open Access
A Bayesian Ensemble Learning-Based Scheme for Real-Time Error Correction of Flood Forecasting
To address the critical demand for high-precision forecasts in flood management, real-time error correction techniques are increasingly implemented to improve the accuracy and operational reliability of the hydrological prediction framework. However, developing a robust error correction scheme remains a significant challenge due to the compounded errors inherent in hydrological modeling frameworks. In this study, a Bayesian ensemble learning-based correction (BELC) scheme is proposed which integrates hydrological modeling with multiple machine learning methods to enhance real-time error correction for flood forecasting. The Xin’anjiang (XAJ) model is selected as the hydrological model for this study, given its proven effectiveness in flood forecasting across humid and semi-humid regions, combining structural simplicity with demonstrated predictive accuracy. The BELC scheme straightforwardly post-processes the output of the XAJ model under the Bayesian ensemble learning framework. Four machine learning methods are implemented as base learners: long short-term memory (LSTM) networks, a light gradient-boosting machine (LGBM), temporal convolutional networks (TCN), and random forest (RF). Optimal weights for all base learners are determined by the K-means clustering technique and Bayesian optimization in the BELC scheme. Four baseline schemes constructed by base learners and three ensemble learning-based schemes are also built for comparison purposes. The performance of the BELC scheme is systematically evaluated in the Hengshan Reservoir watershed (Fenghua City, China). Results indicate the following: (1) The BELC scheme achieves better performance in both accuracy and robustness compared to the four baseline schemes and three ensemble learning-based schemes. The average performance metrics for 1–3 h lead times are 0.95 (NSE), 0.92 (KGE), 24.25 (RMSE), and 8.71% (RPE), with a PTE consistently below 1 h in advance. (2) The K-means clustering technique proves particularly effective with the ensemble learning framework for high flow ranges, where the correction performance exhibits an increment of 62%, 100%, and 100% for 1 h, 2 h, and 3 h lead hours, respectively. Overall, the BELC scheme demonstrates the potential of a Bayesian ensemble learning framework in improving real-time error correction of flood forecasting systems.Open AccessA Bayesian Ensemble Learning-Based Scheme for Real-Time Error Correction of Flood Forecasting
To address the critical demand for high-precision forecasts in flood management, real-time error correction techniques are increasingly implemented to improve the accuracy and operational reliability of the hydrological prediction framework. However, developing a robust error correction scheme remains a significant challenge due to the compounded errors inherent in hydrological modeling frameworks. In this study, a Bayesian ensemble learning-based correction (BELC) scheme is proposed which integrates hydrological modeling with multiple machine learning methods to enhance real-time error correction for flood forecasting. The Xin’anjiang (XAJ) model is selected as the hydrological model for this study, given its proven effectiveness in flood forecasting across humid and semi-humid regions, combining structural simplicity with demonstrated predictive accuracy. The BELC scheme straightforwardly post-processes the output of the XAJ model under the Bayesian ensemble learning framework. Four machine learning methods are implemented as base learners: long short-term memory (LSTM) networks, a light gradient-boosting machine (LGBM), temporal convolutional networks (TCN), and random forest (RF). Optimal weights for all base learners are determined by the K-means clustering technique and Bayesian optimization in the BELC scheme. Four baseline schemes constructed by base learners and three ensemble learning-based schemes are also built for comparison purposes. The performance of the BELC scheme is systematically evaluated in the Hengshan Reservoir watershed (Fenghua City, China). Results indicate the following: (1) The BELC scheme achieves better performance in both accuracy and robustness compared to the four baseline schemes and three ensemble learning-based schemes. The average performance metrics for 1–3 h lead times are 0.95 (NSE), 0.92 (KGE), 24.25 (RMSE), and 8.71% (RPE), with a PTE consistently below 1 h in advance. (2) The K-means clustering technique proves particularly effective with the ensemble learning framework for high flow ranges, where the correction performance exhibits an increment of 62%, 100%, and 100% for 1 h, 2 h, and 3 h lead hours, respectively. Overall, the BELC scheme demonstrates the potential of a Bayesian ensemble learning framework in improving real-time error correction of flood forecasting systems. - Open Access
- 28 August 2024
- Published by MDPI AG in ISPRS International Journal of Geo-Information
- Vol. 13 (9) , 306
Landslide Recognition Based on Machine Learning Considering Terrain Feature Fusion
Landslides are one of the major disasters that exist worldwide, posing a serious threat to human life and property safety. Rapid and accurate detection and mapping of landslides are crucial for risk assessment and humanitarian assistance in affected areas. To achieve this goal, this study proposes a landslide recognition method based on machine learning (ML) and terrain feature fusion. Taking the Dawan River Basin in Detuo Township and Tianwan Yi Ethnic Township as the research area, firstly, landslide-related data were compiled, including a landslide inventory based on field surveys, satellite images, historical data, high-resolution remote sensing images, and terrain data. Then, different training datasets for landslide recognition are constructed, including full feature datasets that fusion terrain features and remote sensing features and datasets that only contain remote sensing features. At the same time, different ratios of landslide to non-landslide (or positive/negative, P/N) samples are set in the training data. Subsequently, five ML algorithms, including Extreme Gradient Boost (XGBoost), Adaptive Boost (AdaBoost), Light Gradient Boost (LightGBM), Random Forest (RF), and Convolutional Neural Network (CNN), were used to train each training dataset, and landslide recognition was performed on the validation area. Finally, accuracy (A), precision (P), recall (R), F1 score (F1), and intersection over union (IOU) were selected to evaluate the landslide recognition ability of different models. The research results indicate that selecting ML models suitable for the study area and the ratio of the P/N samples can improve the A, R, F1, and IOU of landslide identification results, resulting in more accurate and reasonable landslide identification results; Fusion terrain features can make the model recognize landslides more comprehensively and align better with the actual conditions. The best-performing model in the study is LightGBM. When the input data includes all features and the P/N sample ratio is optimal, the A, P, R, F1, and IOU of landslide recognition results for this model are 97.47%, 85.40%, 76.95%, 80.95%, and 71.28%, respectively. Compared to the landslide recognition results using only remote sensing features, this model shows improvements of 4.51%, 35.66%, 5.41%, 22.27%, and 29.16% in A, P, R, F1, and IOU, respectively. This study serves as a valuable reference for the precise and comprehensive identification of landslide areas.
- 28 August 2024
- Published by MDPI AG in ISPRS International Journal of Geo-Information
- Vol. 13 (9) , 306
Open AccessLandslide Recognition Based on Machine Learning Considering Terrain Feature Fusion
Landslides are one of the major disasters that exist worldwide, posing a serious threat to human life and property safety. Rapid and accurate detection and mapping of landslides are crucial for risk assessment and humanitarian assistance in affected areas. To achieve this goal, this study proposes a landslide recognition method based on machine learning (ML) and terrain feature fusion. Taking the Dawan River Basin in Detuo Township and Tianwan Yi Ethnic Township as the research area, firstly, landslide-related data were compiled, including a landslide inventory based on field surveys, satellite images, historical data, high-resolution remote sensing images, and terrain data. Then, different training datasets for landslide recognition are constructed, including full feature datasets that fusion terrain features and remote sensing features and datasets that only contain remote sensing features. At the same time, different ratios of landslide to non-landslide (or positive/negative, P/N) samples are set in the training data. Subsequently, five ML algorithms, including Extreme Gradient Boost (XGBoost), Adaptive Boost (AdaBoost), Light Gradient Boost (LightGBM), Random Forest (RF), and Convolutional Neural Network (CNN), were used to train each training dataset, and landslide recognition was performed on the validation area. Finally, accuracy (A), precision (P), recall (R), F1 score (F1), and intersection over union (IOU) were selected to evaluate the landslide recognition ability of different models. The research results indicate that selecting ML models suitable for the study area and the ratio of the P/N samples can improve the A, R, F1, and IOU of landslide identification results, resulting in more accurate and reasonable landslide identification results; Fusion terrain features can make the model recognize landslides more comprehensively and align better with the actual conditions. The best-performing model in the study is LightGBM. When the input data includes all features and the P/N sample ratio is optimal, the A, P, R, F1, and IOU of landslide recognition results for this model are 97.47%, 85.40%, 76.95%, 80.95%, and 71.28%, respectively. Compared to the landslide recognition results using only remote sensing features, this model shows improvements of 4.51%, 35.66%, 5.41%, 22.27%, and 29.16% in A, P, R, F1, and IOU, respectively. This study serves as a valuable reference for the precise and comprehensive identification of landslide areas.