Seasonal Prediction of Summer Precipitation in the Middle and Lower Reaches of the Yangtze River Valley: Comparison of Machine Learning and Climate Model Predictions

Summer Precipitation Forecast Using an Optimized Artificial Neural Network with a Genetic Algorithm for Yangtze-Huaihe River Basin, China

Owing to the complexity of the climate system and limitations of numerical dynamical models, machine learning based on big data has been used for climate forecasting in recent years. In this study, we attempted to use an artificial neural network (ANN) for summer precipitation forecasts in the Yangtze-Huaihe River Basin (YHRB), eastern China. The major ANN employed here is the standard backpropagation neural network (BPNN), which was modified for application to the YHRB. Using the analysis data of precipitation and the predictors/factors of atmospheric circulation and sea surface temperature, we calculated the correlation coefficients between precipitation and the factors. In addition, we sorted the top six factors for precipitation forecasts. In order to obtain accurate forecasts, month (factor)-to-month (precipitation) forecast models were applied over the training and validation periods (i.e., summer months over 1979–2011 and 2012–2019, respectively). We compared the standard BPNN with the BPNN using a genetic algorithm-based backpropagation (GABP), support vector machine (SVM) and multiple linear regression (MLR) for the summer precipitation forecast after the model training period, and found that the GABP method is the best among the above methods for precipitation forecasting, with a mean absolute percentage error (MAPE) of approximately 20% for the YHRB, which is substantially lower than the BPNN, SVM and MLR values. We then selected the best summer precipitation forecast of the GABP month-to-month models by summing up monthly precipitation, in order to obtain the summer scale forecast, which presents a very successful performance in terms of evaluation measures. For example, the basin-averaged MAPE and anomaly rate reach 4.7% and 88.3%, respectively, for the YHRB, which can be a good recommendation for future operational services. It appears that sea surface temperatures (SST) in some key areas dominate the factors for the forecasts. These results indicate the potential of applying GABP to summer precipitation forecasts in the YHRB.

Water Information Extraction Based on Multi-Model RF Algorithm and Sentinel-2 Image Data

For the Sentinel-2 multispectral satellite image remote sensing data, due to the rich spatial information, the traditional water body extraction methods cannot meet the needs of practical applications. In this study, a random forest-based RF_16 optimal combination model algorithm is proposed to extract water bodies. The research process uses Sentinel-2 multispectral satellite images and DEM data as the basic data, collected 24 characteristic variable indicators (B2, B3, B4, B8, B11, B12, NDVI, MSAVI, B5, B6, B7, B8A, NDI45, MCARI, REIP, S2REP, IRECI, PSSRa, NDWI, MNDWI, LSWI, DEM, SLOPE, SLOPE ASPECT), and constructed four combined models with different input variables. After analysis, it was determined that RF_16 was the optimal combination for extracting water body information in the study area. Model. The results show that: (1) The characteristic variables that have an important impact on the accuracy of the model are the improved normalized difference water index (MNDWI), band B2 (Blue), normalized water index (NDWI), B4 (Red), B3 (Green), and band B5 (Vegetation Red-Edge 1); (2) The water extraction accuracy of the optimal combined model RF_16 can reach 93.16%, and the Kappa coefficient is 0.8214. The overall accuracy is 0.12% better than the traditional Relief F algorithm. The RF_16 method based on the optimal combination model of random forest is an effective means to obtain high-precision water body information in the study area. It can effectively reduce the “salt and pepper effect” and the influence of mixed pixels such as water and shadows on the water extraction accuracy.