scholarly journals A Machine-Learning Approach Combining Wavelet Packet Denoising with Catboost for Weather Forecasting

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1618
Author(s):  
Dan Niu ◽  
Li Diao ◽  
Zengliang Zang ◽  
Hongshu Che ◽  
Tianbao Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Weather Forecasting ◽  
Human Life ◽  
Wavelet Packet ◽  
Weather Prediction ◽  
Convergence Time ◽  
Observation Data ◽  
Initial State ◽  
Historical Observation ◽  
Meteorological Elements

Accurate forecasting of future meteorological elements is critical and has profoundly affected human life in many aspects from rainstorm warning to flight safety. The conventional numerical weather prediction (NWP) sometimes leads to unsatisfactory performance due to inappropriate initial state settings. In this paper, a short-term weather forecasting model based on wavelet packet denoising and Catboost is proposed, which takes advantage of the fusion information combining the historical observation data with the prior knowledge from NWP. The feature selection and spatiotemporal feather addition are also explored to further improve performance. The proposed method is evaluated on the datasets provided by Beijing weather stations. Experimental results demonstrate that compared with many deep-learning or machine-learning methods such as LSTM, Seq2Seq, and random forest, the proposed Catboost model incorporated with wavelet packet denoising can achieve shorter convergence time and higher prediction accuracy.

scholarly journals Improving the Accuracy of Subseasonal Forecasting of China Precipitation With a Machine Learning Approach

2021 ◽  
Vol 9 ◽  
Author(s):  
Cen Wang ◽  
Zhaoying Jia ◽  
Zhaohui Yin ◽  
Fei Liu ◽  
Gaopeng Lu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
North Pacific ◽  
Western North Pacific ◽  
Weather Forecasting ◽  
Selection Process ◽  
Local Linear Regression ◽  
Physical Factors ◽  
Observation Data ◽  
Meteorological Elements

Precipitation change, which is closely related to drought and flood disasters in China, affects billions of people every year, and the demand for subseasonal forecasting of precipitation is even more urgent. Subseasonal forecasting, which is more difficult than weather forecasting, however, has remained as a blank area in meteorological service for a long period of time. To improve the accuracy of subseasonal forecasting of China precipitation, this work introduces the machine learning method proposed by Hwang et al. in 2019 to predict the precipitation in China 2–6 weeks in advance. The authors used a non-linear regression model called local linear regression together with multitask feature election (MultiLLR) model and chosen 21 meteorological elements as candidate predictors to integrate diverse meteorological observation data. This method automatically eliminates irrelevant predictors so as to establish the forecast equations using multitask feature selection process. The experiments demonstrate that the pressure and Madden–Julian Oscillation (MJO) are the most important physical factors. The average prediction skill is 0.11 during 2011–2016, and there are seasonal differences in forecasting skills, evidenced by higher forecast skills of winter and spring seasons than summer and autumn seasons. The proposed method can provide effective and indicative guidance for the subseasonal prediction of precipitation in China. By adding another three factors, Arctic Oscillation (AO) index, Western North Pacific Monsoon (WNPM) index and Western North Pacific Subtropical High (WNPSH) index into the MultiLLR model, the authors find that AO can improve the forecast skill of China precipitation to the maximum extent from 0.11 to 0.13, followed by WNPSH. Moreover, the ensemble skill of our model and CFSv2 is 0.16. This work shows that our subseasonal prediction of China precipitation should be benefited from the MultiLLR model.

scholarly journals Weather Prediction for Tourism Application using Time Series Algorithms

2020 ◽  
Vol 9 (11) ◽  
pp. 406-412
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Weather Forecasting ◽  
Human Life ◽  
Weather Prediction ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Series Data ◽  
Observation Data ◽  
Fine Grained

Precise projections of future events are crucial in many areas, one of which is the tourism sector. Usually counter-trials and towns spend a enormous quantity of cash in planning and preparation to accommodate (and benefit) visitors. Precisely predicting the amount of visits in the days or months, that follow would benefit the economy and tourists both. Previous studies in this field investigate predictions for a nation as a whole rather than for fine-grained fields within a nation. Weather forecasting has drawn the attention of many scientists from distinct research communities due to its impact on human life globally. The developing deep learning methods coupled with the wide accessibility of huge weather observation data and the advancement of machine learning algorithms has motivated many scientists to investigate hidden hierarchical patterns for weather forecasting in large amounts of weather data over the previous century. To predict climate information accurately, heavy statistical algorithms are used on the big quantity of historical information. Time series Analysis enables us know the fundamental forces leading to a specific trend in time series data points and enables us to predict and monitor information points by fitting suitable models into them. In this study, Holt-Winter model is used for predicting time series. The forecasting algorithm for Holt-Winters enables users to construct a time series and then use that data to forecast interest areas. Exponential smoothing allocates weights and their respective values against past data to decrease exponentially, to decrease the weight value for older data.

scholarly journals Error Correction of Meteorological Data Obtained with Mini-AWSs Based on Machine Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Ji-Hun Ha ◽  
Yong-Hyuk Kim ◽  
Hyo-Hyuc Im ◽  
Na-Young Kim ◽  
Sangjin Sim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Error Correction ◽  
Atmospheric Pressure ◽  
Observation Interval ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Observation Data ◽  
Pressure Data ◽  
Weather Forecasts ◽  
Weather Events

Severe weather events occur more frequently due to climate change; therefore, accurate weather forecasts are necessary, in addition to the development of numerical weather prediction (NWP) of the past several decades. A method to improve the accuracy of weather forecasts based on NWP is the collection of more meteorological data by reducing the observation interval. However, in many areas, it is economically and locally difficult to collect observation data by installing automatic weather stations (AWSs). We developed a Mini-AWS, much smaller than AWSs, to complement the shortcomings of AWSs. The installation and maintenance costs of Mini-AWSs are lower than those of AWSs; Mini-AWSs have fewer spatial constraints with respect to the installation than AWSs. However, it is necessary to correct the data collected with Mini-AWSs because they might be affected by the external environment depending on the installation area. In this paper, we propose a novel error correction of atmospheric pressure data observed with a Mini-AWS based on machine learning. Using the proposed method, we obtained corrected atmospheric pressure data, reaching the standard of the World Meteorological Organization (WMO; ±0.1 hPa), and confirmed the potential of corrected atmospheric pressure data as an auxiliary resource for AWSs.

scholarly journals Machine Learning Weather Soft-Sensor for Advanced Control of Wastewater Treatment Plants

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3139 ◽  
Author(s):  
Félix Hernández-del-Olmo ◽  
Elena Gaudioso ◽  
Natividad Duro ◽  
Raquel Dormido
Keyword(s):  
Machine Learning ◽  
Wastewater Treatment ◽  
Control Systems ◽  
Weather Forecasting ◽  
Wastewater Treatment Plants ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Soft Sensor ◽  
Physical Sensors ◽  
Useful Signal

Control of wastewater treatment plants (WWTPs) is challenging not only because of their high nonlinearity but also because of important external perturbations. One the most relevant of these perturbations is weather. In fact, different weather conditions imply different inflow rates and substance (e.g., N-ammonia, which is among the most important) concentrations. Therefore, weather has traditionally been an important signal that operators take into account to tune WWTP control systems. This signal cannot be directly measured with traditional physical sensors. Nevertheless, machine learning-based soft-sensors can be used to predict non-observable measures by means of available data. In this paper, we present novel research about a new soft-sensor that predicts the current weather signal. This weather prediction differs from traditional weather forecasting since this soft-sensor predicts the weather conditions as an operator does when controling the WWTP. This prediction uses a model based on past WWTP influent states measured by only a few physical and widely applied sensors. The results are encouraging, as we obtained a good accuracy level for a relevant and very useful signal when applied to advanced WWTP control systems.

Machine Learning Method for Forecasting Weather Needed For Crop Water Demand Estimations in Low-Resource Settings Using A Case Study in Morocco

2021 ◽  
Author(s):  
Carolyn Sheline ◽  
Amos Winter
Keyword(s):  
Machine Learning ◽  
Water Demand ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Crop Productivity ◽  
Irrigation Scheduling ◽  
Crop Water ◽  
Low Resource Settings ◽  
Low Resource ◽  
Crop Water Demand

Abstract Low and middle income countries often do not have the infrastructure needed to support weather forecasting models, which are computationally expensive and often require detailed inputs from local weather stations. Local, low-cost weather prediction services are needed to enable optimal irrigation scheduling and increase crop productivity for rural farmers in low-resource settings. This work proposes a machine learning approach to predict the weather inputs needed to calculate crop water demand, namely evapotranspiration and precipitation. The focus of this work is on the accuracy with which Moroccan weather can be predicted with a vector autoregression (VAR) model compared to using typical meteorological year (TMY) weather, and how this accuracy changes as the number of weather parameters is reduced.

scholarly journals Automatic Regional Interpretation and Forecasting System Supported by Machine Learning

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 793
Author(s):  
Chao Yan ◽  
Jing Feng ◽  
Kaiwen Xia ◽  
Chaofan Duan
Keyword(s):  
Machine Learning ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Forecast Model ◽  
Support Vector ◽  
Observation Data ◽  
Forecasting System ◽  
Weather Changes ◽  
Model Output Statistics

The Model Output Statistics (MOS) model is a dynamic statistical weather forecast model based on multiple linear regression technology. It is greatly affected by the selection of parameters and predictors, especially when the weather changes drastically, or extreme weather occurs. We improved the traditional MOS model with the machine learning method to enhance the capabilities of self-learning and generalization. Simultaneously, multi-source meteorological data were used as the input to the model to improve the data quality. In the experiment, we selected the four areas of Nanjing, Beijing, Chengdu, and Guangzhou for verification, with the numerical weather prediction (NWP) products and observation data from automatic weather stations (AWSs) used to predict the temperature and wind speed in the next 24 h. From the experiment, it can be seen that the accuracy of the prediction values and speed of the method were improved by the ML-MOS. Finally, we compared the ML-MOS model with neural networks and support vector machine (SVM), the results show that the prediction result of the ML-MOS model is better than that of the above two models.

scholarly journals Weather Prediction using Machine Learning and IOT

2020 ◽  
Vol 9 (4) ◽  
pp. 2094-2098
Keyword(s):  
Machine Learning ◽  
Weather Forecasting ◽  
Learning Algorithm ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Data Sets ◽  
Machine Learning Algorithm ◽  
Time Data ◽  
Weather Parameters ◽  
Set Up

This project proposes a method for forecasting weather conditions and predicting rainfall by means of machine learning. Here, there are two set ups: one, to measure the weather parameters like temperature, humidity using sensors along with Arduino and another set up, to display the current values(status) and predicted rainfall based on the trained machine learning data sets. The weather forecasting and prediction is done based on the older datasets collected and compared with the current values. The user need not have a backup of huge data to predict the rainfall. Instead a machine learning algorithm can suffice the same. The temperature, humidity sensor modules are used to measure weather parameters and interfaced to an Arduino controller. The proposed setup will compare the forecast value with real-time data, and the predict rainfall based on the dataset fed to the machine learning algorithm.

scholarly journals Optimized K Nearest Neighbor Classification Algorithm for Weather Prediction

2019 ◽  
Vol 8 (4) ◽  
pp. 7261-7263
Keyword(s):  
Nearest Neighbor ◽  
Weather Forecasting ◽  
Daily Life ◽  
Human Life ◽  
Weather Prediction ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
The Past ◽  
Internal Part ◽  
Neighbor Classification

Weather has a lot of blow in our daily life and also gained researchers concentration due to its enormous effect in the human life. To defend ourselves from weather, we need to predict the weather such as rainfall, humidity and temperature etc. Using classification algorithms, we can predict the weather by using the past datasets. In this research paper, WEKA tool is used to implement classification algorithms for weather forecasting. Machine Learning is an internal part of artificial intelligence, which is used to design algorithms based on the relationships between data and data trends.

scholarly journals An Optimal K-Nearest Neighbor for Weather Prediction Using Whale Optimization Algorithm

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Optimization Algorithm ◽  
Nearest Neighbor ◽  
Human Life ◽  
Weather Prediction ◽  
Machine Learning Algorithms ◽  
Whale Optimization Algorithm ◽  
Optimal Prediction ◽  
K Nearest Neighbor ◽  
Whale Optimization

The weather has a serious impact on the environment as it affects to change day to day life. In recent days, many algorithms were proposed to predict the weather. Although various machine learning algorithms predict the weather, the optimal prediction of weather is not addressed. Optimal Prediction of weather is required as it has a serious impact on human life. Thus this domain invites an optimal system that can forecast weather thereby saving human life. To optimally predict the changes in weather, a metaheuristic algorithm called Whale Optimization Algorithm (WOA) is integrated with machine learning algorithm K- Nearest Neighbor (K-NN). Whale optimization is an algorithm inspired by the social behavior of whales. The proposed WOAK-NN is compared with K-NN. The integration of WOA with K-NN aims to maximize accuracy, F-measure and minimize mean absolute error. Also, the time complexity of WOAK-NN is compared with K-NN and observed that when the dataset is large, WOAK-NN requires minimum time for an optimal prediction.

scholarly journals SHORT-TERM PRECIPITATION OCCURRENCE PREDICTION FOR STRONG CONVECTIVE WEATHER USING FY2-G SATELLITE DATA: A CASE STUDY OF SHENZHEN,SOUTH CHINA

2016 ◽  
Vol XLI-B6 ◽  
pp. 215-219
Author(s):  
Kai Chen ◽  
Jun Liu ◽  
Shanxin Guo ◽  
Jinsong Chen ◽  
Ping Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Weather Prediction ◽  
Satellite Image ◽  
Precipitation Amount ◽  
In Situ Observation ◽  
Observation Data ◽  
Machine Learning Method ◽  
Learning Method ◽  
Short Term ◽  
Precipitation Occurrence

Short-term precipitation commonly occurs in south part of China, which brings intensive precipitation in local region for very short time. Massive water would cause the intensive flood inside of city when precipitation amount beyond the capacity of city drainage system. Thousands people’s life could be influenced by those short-term disasters and the higher city managements are required to facing these challenges. How to predict the occurrence of heavy precipitation accurately is one of the worthwhile scientific questions in meteorology. According to recent studies, the accuracy of short-term precipitation prediction based on numerical simulation model still remains low reliability, in some area where lack of local observations, the accuracy may be as low as 10%. The methodology for short term precipitation occurrence prediction still remains a challenge. In this paper, a machine learning method based on SVM was presented to predict short-term precipitation occurrence by using FY2-G satellite imagery and ground in situ observation data. The results were validated by traditional TS score which commonly used in evaluation of weather prediction. The results indicate that the proposed algorithm can present overall accuracy up to 90% for one-hour to six-hour forecast. The result implies the prediction accuracy could be improved by using machine learning method combining with satellite image. This prediction model can be further used to evaluated to predicted other characteristics of weather in Shenzhen in future.

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