Deep learning for time series forecasting: Advances and open problems
A time series is a sequence of time-ordered data, and it is generally used to describe how a
phenomenon evolves over time. Time series forecasting, estimating future values of time …
phenomenon evolves over time. Time series forecasting, estimating future values of time …
Continual deep learning for time series modeling
The multi-layer structures of Deep Learning facilitate the processing of higher-level
abstractions from data, thus leading to improved generalization and widespread …
abstractions from data, thus leading to improved generalization and widespread …
Short-term rainfall forecasting using cumulative precipitation fields from station data: a probabilistic machine learning approach
Rainfall nowcasting supports emergency decision-making in hydrological, agricultural, and
economical sectors. However, short-term prediction is challenging because meteorological …
economical sectors. However, short-term prediction is challenging because meteorological …
An integrated statistical-machine learning approach for runoff prediction
Nowadays, great attention has been attributed to the study of runoff and its fluctuation over
space and time. There is a crucial need for a good soil and water management system to …
space and time. There is a crucial need for a good soil and water management system to …
A predictive analysis of heart rates using machine learning techniques
Heart disease, caused by low heart rate, is one of the most significant causes of mortality in
the world today. Therefore, it is critical to monitor heart health by identifying the deviation in …
the world today. Therefore, it is critical to monitor heart health by identifying the deviation in …
Group method of data handling using Christiano–Fitzgerald random walk filter for insulator fault prediction
Disruptive failures threaten the reliability of electric supply in power branches, often
indicated by the rise of leakage current in distribution insulators. This paper presents a …
indicated by the rise of leakage current in distribution insulators. This paper presents a …
Improving monthly rainfall forecast in a watershed by combining neural networks and autoregressive models
The main aim of the rain forecast is to determine rain occurrence conditions in a specific
location. This is considered of vital importance to assess the availability of water resources …
location. This is considered of vital importance to assess the availability of water resources …
[PDF][PDF] Is deep learning on tabular data enough? An assessment
It is critical to select the model that best fits the situation while analyzing the data. Many
scholars on classification and regression issues have offered ensemble techniques on …
scholars on classification and regression issues have offered ensemble techniques on …
Convolutional neural network-support vector machine model-gaussian process regression: a new machine model for predicting monthly and daily rainfall
Rainfall prediction is an important issue in water resource management. Predicting rainfall
helps researchers to monitor droughts, surface water and floods. The current study …
helps researchers to monitor droughts, surface water and floods. The current study …
Drip fertigation triggered by soil matric potential reduces residual soil nitrate content and improves maize nitrogen uptake and yield stability in an arid area
Y Cheng, T Zhang, X Hu, Z Liu, Q Liang, S Yan… - European Journal of …, 2023 - Elsevier
Effective water and fertilizer management is crucial for enhancing nitrogen (N) use efficiency
and ensuring regional food security, especially in arid regions. Drip fertigation can …
and ensuring regional food security, especially in arid regions. Drip fertigation can …