Influencing Factors and Risk Assessment of Precipitation-Induced Flooding in Zhengzhou, China, Based on Random Forest and XGBoost Algorithms

Liu, Xun; Zhou, Peng; Lin, Ye; Sun, Shi‐Gang; Zhang, Hailu; Xu, Wei; Yang, Sangdi

doi:10.3390/ijerph192416544

Cited by 14 publications

(7 citation statements)

References 64 publications

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“…The RF algorithm is to model the decision tree for each sample, then combine the prediction of multiple decision trees, and obtain the final result by voting or averaging. 21 Fig. 4 Elevation reclassification map of Kaiyuan City.…”

Section: Random Forests Classification Methodsmentioning

confidence: 99%

Remote sensing monitoring of planting area of Panax notoginseng: A case study of Kaiyuan City in Honghe Prefecture

Zhang

Zhao

et al. 2023

J. Appl. Rem. Sens.

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Panax notoginseng is second only to ginseng Panax notoginseng in the Chinese traditional medicine industry. It has extremely high economic value and medicinal value. However, due to the unsustainable planting characteristics of P. notoginseng. The original planting area of P. notoginseng has been exhausted, so the planting area of P. notoginseng has gradually expanded to other areas, and the planting area of P. notoginseng is also changing. Based on Sentinel-2 and Landsat 8 OLI remote sensing image data from 2018 to 2021, the study used Google Earth engine and Random Forest classification to analyze the spatial distribution information of P. notoginseng planting areas in Kaiyuan City, Honghe Prefecture. The results show that, based on Sentinel-2 data, Landsat 8 OLI data, and other multisource data, comprehensively considering the image spectral characteristics and habitat characteristics of P. notoginseng, and applying the Random Forest classification method. The spatial distribution information of P. notoginseng planting areas can be quickly extracted. After calculation, the transfer matrix indicates that the accuracy of the results is high and can meet the requirements of research and application. According to the analysis of the results, the cultivated area of P. notoginseng showed no significant change in 2018 and 2019. It shows an increasing trend during this period. In 2020, the cultivated area of P. notoginseng decreased slightly. While in 2021, the cultivated area of P. notoginseng increased sharply, and the cultivated area of P. notoginseng was more than twice that of the previous year. However, the price of P. notoginseng has shown a trend of decline year by year. By monitoring the planting area of P. notoginseng and analyzing the relationship between the planting area and the price of P. notoginseng, we can predict the future market price of P. notoginseng and plan the rational planting of P. notoginseng.

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Section: Random Forests Classification Methodsmentioning

confidence: 99%

Remote sensing monitoring of planting area of Panax notoginseng: A case study of Kaiyuan City in Honghe Prefecture

Zhang

Zhao

et al. 2023

J. Appl. Rem. Sens.

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“…The research site is located in Xingyang city, Henan Province, China (Figure 1). Xingyang city is situated between 34°36′ and 34°59′N and between 113°7′ and 113°30′E [42]. Xingyang city experiences a warm, temperate, continental monsoon climate, with an average annual temperature of 14.8 °C and an average annual precipitation of 608.8 millimeters.…”

Section: Study Areamentioning

confidence: 99%

Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation

Hu,

Feng,

Wang

et al. 2024

Remote Sensing

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Crop leaf chlorophyll content (LCC) and fractional vegetation cover (FVC) are crucial indicators for assessing crop health, growth development, and maturity. In contrast to the traditional manual collection of crop trait parameters, unmanned aerial vehicle (UAV) technology rapidly generates LCC and FVC maps for breeding materials, facilitating prompt assessments of maturity information. This study addresses the following research questions: (1) Can image features based on pretrained deep learning networks and ensemble learning enhance the estimation of remote sensing LCC and FVC? (2) Can the proposed adaptive normal maturity detection (ANMD) algorithm effectively monitor maize maturity based on LCC and FVC maps? We conducted the following tasks: (1) Seven phases (tassel initiation to maturity) of maize canopy orthoimages and corresponding ground-truth data for LCC and six phases of FVC using UAVs were collected. (2) Three features, namely vegetation indices (VI), texture features (TF) based on Gray Level Co-occurrence Matrix, and deep features (DF), were evaluated for LCC and FVC estimation. Moreover, the potential of four single-machine learning models and three ensemble models for LCC and FVC estimation was evaluated. (3) The estimated LCC and FVC were combined with the proposed ANMD to monitor maize maturity. The research findings indicate that (1) image features extracted from pretrained deep learning networks more accurately describe crop canopy structure information, effectively eliminating saturation effects and enhancing LCC and FVC estimation accuracy. (2) Ensemble models outperform single-machine learning models in estimating LCC and FVC, providing greater precision. Remarkably, the stacking + DF strategy achieved optimal performance in estimating LCC (coefficient of determination (R2): 0.930; root mean square error (RMSE): 3.974; average absolute error (MAE): 3.096); and FVC (R2: 0.716; RMSE: 0.057; and MAE: 0.044). (3) The proposed ANMD algorithm combined with LCC and FVC maps can be used to effectively monitor maize maturity. Establishing the maturity threshold for LCC based on the wax ripening period (P5) and successfully applying it to the wax ripening-mature period (P5–P7) achieved high monitoring accuracy (overall accuracy (OA): 0.9625–0.9875; user’s accuracy: 0.9583–0.9933; and producer’s accuracy: 0.9634–1). Similarly, utilizing the ANMD algorithm with FVC also attained elevated monitoring accuracy during P5–P7 (OA: 0.9125–0.9750; UA: 0.878–0.9778; and PA: 0.9362–0.9934). This study offers robust insights for future agricultural production and breeding, offering valuable insights for the further exploration of crop monitoring technologies and methodologies.

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“…Fractional vegetation cover (VFC): Vegetation can block surface runoff [28] and increase the infiltration recharge of precipitation to the slope. Soil erosion is reduced by reinforcing the soil and increasing the shear strength.…”

Section: Conditioning Factorsmentioning

confidence: 99%

A Multi-Hazard Risk Assessment Model for a Road Network Based on Neural Networks and Fuzzy Comprehensive Evaluation

Zhou,

Chen,

Chen

et al. 2024

Sustainability

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The frequency of extreme weather events has increased worldwide, leading to more intense natural disasters, which pose significant threats to human life and property safety. The main form of disaster occurrence is multi-hazard coupling and multi-hazard chaining. This paper constructs a road natural disaster risk assessment model using a fuzzy comprehensive evaluation method and neural network to quantitatively analyze road disasters with multiple hazards, and provides valuable insights for the predication of road natural disaster risk. Here, ten factors, including temperature, relative humidity, precipitation, elevation, slope, slope orientation, vegetation cover, geologic lithology, historical impact factors, and road density, were selected as input variables, and risk grade was selected as the output value (the evaluation results). The remaining hidden layers use the fully connected neural network. This model was first trained using historical data (from 2011 to 2021) obtained from road networks and natural disasters in Guangxi, China. Then, taking Lingchuan County as an example, the model was used to predict the risk of natural disasters on its roads, and, finally, the prediction accuracy of the model was determined by comparing the results with actual disaster situations. This study can provide theoretical support and technical operations for the development of subsequent early warning systems.

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Influencing Factors and Risk Assessment of Precipitation-Induced Flooding in Zhengzhou, China, Based on Random Forest and XGBoost Algorithms

Cited by 14 publications

References 64 publications

Remote sensing monitoring of planting area of Panax notoginseng: A case study of Kaiyuan City in Honghe Prefecture

Remote sensing monitoring of planting area of Panax notoginseng: A case study of Kaiyuan City in Honghe Prefecture

Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation

A Multi-Hazard Risk Assessment Model for a Road Network Based on Neural Networks and Fuzzy Comprehensive Evaluation

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