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Rivers

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Imputation of missing monthly rainfall data using machine learning and spatial interpolation approaches in Thale Sap Songkhla River Basin, Thailand.

Environmental science and pollution research international
Missing rainfall data has been a prevalent issue and primarily interested in hydrology and meteorology. This research aimed to examine the capability of machine learning (ML) and spatial interpolation (SI) methods to estimate missing monthly rainfall...

Forest Environmental Carrying Capacity Based on Deep Learning.

Computational intelligence and neuroscience
In this paper, we proposed an assessment system of forest environmental carrying capacity from many aspects and comprehensively evaluated and predicted the forest environmental carrying capacity of 40 cities in the Yangtze River Delta of China by usi...

Assessing spatial connectivity effects on daily streamflow forecasting using Bayesian-based graph neural network.

The Science of the total environment
Data-driven models have been widely developed and achieved impressive results in streamflow prediction. However, the existing data-driven models mostly focus on the selection of input features and the adjustment of model structure, and less on the im...

Green Financial Health Risk Early Monitoring of Commercial Banks Based on Neural Network Model in a Small Sample Environment.

Journal of environmental and public health
Financial innovations emerge in an endless stream, and it is difficult for the regulatory measures and efforts of banks in various countries and the credit risk management level of commercial banks themselves to adapt to the increasingly complex risk...

A Novel English Translation Model in Complex Environments Using Two-Stream Convolutional Neural Networks.

Journal of environmental and public health
Although translation is an essential component of learning English, it does not receive the attention it merits in the modern English classroom. Teachers and students primarily emphasize listening, reading, and writing while neglecting the developmen...

MEEMD Decomposition-Prediction-Reconstruction Model of Precipitation Time Series.

Sensors (Basel, Switzerland)
To address the problem of low prediction accuracy of precipitation time series data, an improved overall mean empirical modal decomposition-prediction-reconstruction model (MDPRM) is constructed in this paper. First, the non-stationary precipitation ...

High-Quality Development of Science and Technology Finance and Logistics Industry in the Yangtze River Economic Belt: Coupling Analysis Based on Deep Learning Model.

Computational intelligence and neuroscience
Using the entropy method, the coupling coordination model, and the Tobit model, the coupling coordination degree of the high-quality development of science and technology finance and the logistics industry in the Yangtze River Economic Belt in China ...

Prediction and Estimation of River Velocity Based on GAN and Multifeature Fusion.

Computational intelligence and neuroscience
The necessity of predicting and estimating river velocity motivates the development of a prediction method based on GAN image enhancement and multifeature fusion. In this method, in order to improve the image quality of river velocity, GAN network is...

Drought Assessment Based on Data Fusion and Deep Learning.

Computational intelligence and neuroscience
Drought is a major factor affecting the sustainable development of society and the economy. Research on drought assessment is of great significance for formulating drought emergency policies and drought risk early warning and enhancing the ability to...

Application of artificial intelligence models for prediction of groundwater level fluctuations: case study (Tehran-Karaj alluvial aquifer).

Environmental monitoring and assessment
The nonlinear groundwater level fluctuations depend on the interaction of many factors such as evapotranspiration, precipitation, groundwater abstraction, and hydrogeological characteristics, making groundwater level prediction a complex task. Ground...