Slope Micrometeorological Analysis and Prediction Based on an ARIMA Model and Data-Fitting System

Abstract

The rapid development of highway engineering has made slope stability an important issue in infrastructure construction. To meet the needs of green vegetation growth, ecological recovery, landscape beautification and the economy, long-term monitoring research on high-slope micrometeorology has important practical significance. Because of that, we designed and created a new slope micrometeorological monitoring and predicting system (SMMPS). We innovatively upgraded the cloud platform system, by adding an ARIMA prediction system and data-fitting system. From regularly sensor-monitored slope micrometeorological factors (soil temperature and humidity, slope temperature and humidity, and slope rainfall), a data-fitting system was used to fit atmospheric data with slope micrometeorological data, the trend of which ARIMA predicted. The slope was protected in time to prevent severe weather damage to the slope vegetation on a large scale. The SMMPS, which upgrades its cloud platform, significantly reduces the cost of long-term monitoring, protects slope stability, and improves the safety of rail and road projects.

A Model for Predicting and Grading the Quality of Grain Storage Processes Affected by Microorganisms under Different Environments

“…The time series forecasting methods in existing studies can be classified into traditional linear regression methods [23,24], machine learning methods [25,26], and deep learning methods [27][28][29][30][31][32][33][34][35][36][37][38][39][40]. Traditional linear regression methods are moving average models (MA) based on historical white noise modeling, autoregressive models (AR) based on historical time series modeling, and autoregressive moving average models (ARIMA) that combine the first two models.…”

“…Traditional linear regression methods are moving average models (MA) based on historical white noise modeling, autoregressive models (AR) based on historical time series modeling, and autoregressive moving average models (ARIMA) that combine the first two models. These are also widely used in time series forecasting tasks [23,24]. However, none of the above models can capture nonlinear relationships.…”

FEDformer-Based Paddy Quality Assessment Model Affected by Toxin Change in Different Storage Environments

“…In recent years, time series analysis was often used to reveal the development and change pattern of a phenomenon or to portray the intrinsic quantitative relationship between a phenomenon and other phenomena. The existing studies in time series forecasting can be categorized into three groups: traditional linear regression methods [ 19 , 20 ], machine learning methods [ 21 , 22 ], and deep learning methods [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. Traditional models include moving average (MA), autoregressive (AR), and autoregressive moving average (ARIMA) models, all of which are widely used for time series forecasting [ 19 , 20 ].…”

“…The existing studies in time series forecasting can be categorized into three groups: traditional linear regression methods [ 19 , 20 ], machine learning methods [ 21 , 22 ], and deep learning methods [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. Traditional models include moving average (MA), autoregressive (AR), and autoregressive moving average (ARIMA) models, all of which are widely used for time series forecasting [ 19 , 20 ]. Coradi et al [ 1 ] developed six linear regression models to predict grain storage quality and evaluated the models to achieve high prediction accuracy; André et al [ 37 ] used machine learning methods such as artificial neural networks, decision tree algorithm REPTree, and random forest to predict the quality of soybean seeds for decision making in the seed storage process.…”

Power allocation method of battery energy storage system considering state balance in smoothing photovoltaic power fluctuations

“…Another difficulty directly affecting the inhibition of the power fluctuations is the selection of the time constant in the low-pass filter. A fixed-order empirical mode decomposition is devised in Liu et al, 2022, but it does not fully consider the volatility of the photovoltaic power, and the large fluctuations exist sometimes. An optical storage model is proposed to suppress the power fluctuations and corresponding control methods is devised to solve the intermittent output of new energy for grid connection.…”