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Predicting the Occurrence of Forest Fire in the Central-South Region of China

Understanding the spatial and temporal patterns of forest fires, along with the key factors influencing their occurrence, and accurately forecasting these events are crucial for effective forest management. In the Central-South region of China, forest fires pose a significant threat to the ecological system, public safety, and economic stability. This study employs Geographic Information Systems (GISs) and the LightGBM (Light Gradient Boosting Machine) model to identify the determinants of forest fire incidents and develop a predictive model for the likelihood of forest fire occurrences, in addition to proposing a zoning strategy. The purpose of the study is to enhance our understanding of forest fire dynamics in the Central-South region of China and to provide actionable insights for mitigating the risks associated with such disasters. The findings reveal the following: (i) Spatially, fire incidents exhibit significant clustering and autocorrelation, highlighting areas with heightened likelihood. (ii) The Central-South Forest Fire Likelihood Prediction Model demonstrates high accuracy, reliability, and predictive capability, with performance metrics such as accuracy, precision, recall, and F1 scores exceeding 85% and AUC values above 89%, proving its effectiveness in forecasting the likelihood of forest fires and differentiating between fire scenarios. (iii) The likelihood of forest fires in the Central-South region of China varies across regions and seasons, with increased likelihood observed from March to May in specific provinces due to various factors, including weather conditions and leaf litter accumulation. Risks of localized fires are noted from June to August and from September to November in different areas, while certain regions continue to face heightened likelihood from December to February.

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Robust remote sensing retrieval of key eutrophication indicators in coastal waters based on explainable machine learning

Excessive discharges of nitrogen and phosphorus nutrients lead to eutrophication in coastal waters. Optical remote sensing retrieval of the key eutrophication indicators, namely dissolved inorganic nitrogen concentration (DIN), soluble reactive phosphate concentration (SRP), and chemical oxygen demand (COD), remains challenging due to lack of distinct spectral features. Although machine learning (ML) has shown the potential, the retrieval accuracy is limited, and the interpretability is insufficient in terms of the black-box characteristics. To address these limitations, based on robust and explainable ML algorithms, we constructed models for retrieving DIN, SRP, and COD over coastal waters of Northern South China Sea (NSCS), which is experiencing prominent eutrophication. Retrieval models based on classification and regression trees (CART) ML algorithms were developed using 4038 groups of in situ observations and quasi-synchronous satellite images. A comparison of CART algorithms, including Random Forest, Gradient Boosting Decision Tree, and eXtreme Gradient Boosting (XGBoost), indicated the highest retrieval accuracy of XGBoost for DIN (R2 = 0.88, MRE = 24.39 %), SRP (R2 = 0.92, MRE = 33.27 %), and COD (R2 = 0.75, MRE = 18.58 %) for validation dataset. On the basis of spectral remote sensing reflectance, further inputs of ocean physio-chemical properties, spatio-temporal information, and inherent optical properties may reduce retrieval errors by 30.16 %, 19.85 %, and 3.95 %, respectively, and their combined use reduced errors by 54.71 %. Besides, explainable ML analysis characterized the contribution of input features and enhanced the transparency of ML black-box models. Based on the proposed models, 27,278 satellite images and spatio-temporal reconstruction method, 1-km resolution gap-free daily DIN, SRP, and COD products were constructed from 2002 to 2022 for the coastal waters of NSCS. Under the influence of urbanization and river discharge, nitrogen and phosphorus concentrations in this area were found to have increased by 6.09 % and 11.04 %, respectively, over the past 21 years, with the fastest rise in the Pearl River Estuary, where the eutrophic water area had shown an increase rate of approximately 112.66 km2/yr. The proposed robust and explainable ML retrieval models may support ocean environment management and water quality monitoring by providing key eutrophication indicators products over coastal waters.

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