Application of bootstrap-based neural networks for monthly rainfall forecasting in Western Jilin Province, China

2014 ◽  
Vol 9 (2) ◽  
pp. 186-196
Author(s):  
Haibo Chu ◽  
Wenxi Lu ◽  
Xiaoqing Sun
Keyword(s):  
Time Series ◽  
Absolute Error ◽  
Monthly Rainfall ◽  
Jilin Province ◽  
Generalized Regression Neural Network ◽  
Rainfall Time Series ◽  
Rainfall Forecasting ◽  
Uncertainty Estimates ◽  
Western Jilin Province ◽  
Western Jilin

Rainfall forecasting is an important pre-requisite for effectively managing and planning water resources. This study developed a generalized regression neural network (GRNN) combined with a bootstrap approach for rainfall forecasting, and the forecasting results were compared with the autoregressive model and single GRNN model. The test was performed in western Jilin Province, China with a 53-year (1957–2010) monthly rainfall time series. To obtain the good performance of GRNN model, the number of input neurons was decided by the analysis of Bayesian information criterion, and the appropriate spread was selected considering the performance of the training and testing phases. mean absolute error, root mean square error, coefficient of efficiency and R2 are employed to evaluate the performances of the forecasting models. The results showed that the bootstrap-based GRNN model performed better than single GRNN and AR models in forecasting monthly rainfall and the proposed method can improve the prediction accuracy of monthly rainfall time series, while generating uncertainty estimates of the rainfall forecasting.

scholarly journals Monthly rainfall forecasting by a hybrid neural network of discrete wavelet transform and deep learning

2021 ◽  
Author(s):  
Xueyi You ◽  
Ming Wei
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Monthly Rainfall ◽  
Discrete Wavelet ◽  
Rainfall Time Series ◽  
Efficiency Coefficient ◽  
Rainfall Forecasting

Actual rainfall forecast is critical to the management and allocation of water resources. In recent years, deep learning has been proved to be superior to traditional forecasting methods when predicting rainfall time series with high temporal and spatial variability. In this study, the discrete wavelet transform (DWT) and two typical deep learning approaches, namely long-short term memory (LSTM) and dilated causal convolutional neural network (DCCNN), are integrated innovatively and the hybrid model (DWT-CLSTM-DCCNN) is used for monthly rainfall forecasting for the first time. Monthly rainfall time series of four major cities in China (Beijing, Tianjin, Chongqing and Guangzhou) are used as the dataset of DWT-CLSTM-DCCNN. Firstly, two methods of sample construction are used to train DWT-CLSTM-DCCNN and their effects on the model performance are analyzed. Then, LSTM and the dilated causal convolutional network (DCCNN) are established as the benchmark models, and their forecast accuracy is compared with that of DWT-CLSTM-DCCNN. From the results of the evaluation criteria such as mean absolute error (MAE), root mean squared error (RMSE) and Nash-Sutcliffe model efficiency coefficient (NSE) as well as the fitting curve for forecasted rainfall, it can be concluded that the DWT-CLSTM-DCCNN developed in this study outperforms the benchmark models in model accuracy, peak and mutational rainfall capturing ability. Compared with the previous studies, DWT-CLSTM-DCCNN is proven to be better peak capture and more suitable for long-term rainfall forecasting.

scholarly journals SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 602
Author(s):  
Luisa Martínez-Acosta ◽  
Juan Pablo Medrano-Barboza ◽  
Álvaro López-Ramos ◽  
John Freddy Remolina López ◽  
Álvaro Alberto López-Lambraño
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Autocorrelation Function ◽  
Information Criterion ◽  
Monthly Rainfall ◽  
Statistical Characteristics ◽  
Rainfall Time Series ◽  
River Watershed ◽  
Statistical Measures ◽  
Sarima Models

Seasonal Auto Regressive Integrative Moving Average models (SARIMA) were developed for monthly rainfall time series. Normality of the rainfall time series was achieved by using the Box Cox transformation. The best SARIMA models were selected based on their autocorrelation function (ACF), partial autocorrelation function (PACF), and the minimum values of the Akaike Information Criterion (AIC). The result of the Ljung–Box statistical test shows the randomness and homogeneity of each model residuals. The performance and validation of the SARIMA models were evaluated based on various statistical measures, among these, the Student’s t-test. It is possible to obtain synthetic records that preserve the statistical characteristics of the historical record through the SARIMA models. Finally, the results obtained can be applied to various hydrological and water resources management studies. This will certainly assist policy and decision-makers to establish strategies, priorities, and the proper use of water resources in the Sinú river watershed.

A case study on the relationship between soil geochemical components and salinization in the western Jilin Province of China

2004 ◽  
Author(s):  
Yingbo Zhu ◽  
Quansheng Ge ◽  
Jiyuan Liu ◽  
Yunxuan Zhou ◽  
Zhiqiang Gao ◽  
...  
Keyword(s):  
Jilin Province ◽  
Western Jilin Province ◽  
The Relationship ◽  
Western Jilin

scholarly journals Electrical Conductivity Inversion Method of Saline Soil based on Sentinel-2 MSI data

2020 ◽  
Author(s):  
Yishan Sun ◽  
Xiaojie Li ◽  
Tao Jiang ◽  
Xingming Zheng ◽  
Zhengwei Liang
Keyword(s):  
Electrical Conductivity ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Saline Soil ◽  
Soil Salinization ◽  
Jilin Province ◽  
Western Jilin Province ◽  
Western Jilin ◽  
Sentinel 2

Electrical conductivity (EC) is not only an important index to evaluate the degree of soil salinization, but also an essential basis for judging whether saline soil can be improved and assess the effect of improvement efforts. Satellite remote sensing provides much information for large scale EC inversion of saline soil, which enables the possibility for evaluating the degree and distribution of soil salinization. Taking the salinized region of western Jilin Province as the study area, 328 salinized soil samples were collected, and the EC was measured in June 2019. The construction of the optimal spectral parameters was based on the correlation between the conductivity and the spectral reflectivity of Sentinel-2 MSI data; after satisfying the normal distribution for the Box-Cox transformation of EC, the inversion model of EC was established by using linear regression model, support vector machine (SVM), regression tree (RT), Gaussian process regression (GPR), and ensemble tree (ET). The verification results of the model on the validation set showed that the performance of GPR was optimal (R2 = 0.66, RMSE = 0.48 mS/cm, MAE=0.52 mS/cm), which increased R2 by 29.04% compared with the traditional linear regression model. Finally, according to the GPR model, the EC results of pixel-level resolution (10 m × 10 m) of saline soil in western Jilin Province were inversed, which provided a scientific basis for the study of the distribution characteristics and improvement scheme of saline soil.

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