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Damping autoregressive grey model and its application to the prediction of losses caused by meteorological disasters

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GREY SYSTEMS-THEORY AND APPLICATION 2025年第1期15卷 160-184页

作者： Yan, Shuli Gong, Xiaoyu Zeng, Xiangyan Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing Peoples R China Guilin Univ Elect Technol Sch Math & Comp Sci Guilin Peoples R China

PurposeMeteorological disasters pose a significant risk to people's lives and safety, and accurate prediction of weather-related disaster losses is crucial for bolstering disaster prevention and mitigation capabilities and for addressing the challenges posed by climate change. Based on the uncertainty of meteorological disaster sequences, the damping accumulated autoregressive GM(1,1) model (DAARGM(1,1)) is ***/methodology/approachFirstly, the autoregressive terms of system characteristics are added to the damping-accumulated GM(1,1) model, and the partial autocorrelation function (PACF) is used to determine the order of the autoregressive terms. In addition, the optimal damping parameters are determined by the optimization *** properties of the model were analyzed in terms of the stability of the model solution and the error of the restored value. By fitting and predicting the losses affected by meteorological disasters and comparing them with the results of four other grey models, the validity of the new model in fitting and prediction was ***/valueThe dynamic damping trend factor is introduced into the grey generation operator so that the grey model can flexibly adjust the accumulative order of the sequence. On the basis of the damping accumulated grey model, the autoregressive term of the system characteristics is introduced to take into account the influence of the previous data, which is more descriptive of the development trend of the time series itself and increases the effectiveness of the model.

关键词： Damping autoregressive grey model Meteorological disaster Prediction partial autocorrelation function

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Short-Term Daily Univariate Streamflow Forecasting Using Deep Learning Models

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ADVANCES IN METEOROLOGY 2022年第1期2022卷

作者： Wegayehu, Eyob Betru Muluneh, Fiseha Behulu Addis Ababa Univ Addis Ababa Inst Technol Sch Civil & Environm Engn Addis Ababa Ethiopia

Hydrological forecasting is one of the key research areas in hydrology. Innovative forecasting tools will reform water resources management systems, flood early warning mechanisms, and agricultural and hydropower management schemes. Hence, in this study, we compared Stacked Long Short-Term Memory (S-LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and Gated Recurrent Unit (GRU) with the classical Multilayer Perceptron (MLP) network for one-step daily streamflow forecasting. The analysis used daily time series data collected from Borkena (in Awash river basin) and Gummera (in Abay river basin) streamflow stations. All data sets passed through rigorous quality control processes, and null values were filled using linear interpolation. A partial autocorrelation was also applied to select the appropriate time lag for input series generation. Then, the data is split into training and testing datasets using a ratio of 80 : 20, respectively. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R-2) were used to evaluate the performance of the proposed models. Finally, the findings are summarized in model variability, lag time variability, and time series characteristic themes. As a result, time series characteristics (climatic variability) had a more significant impact on streamflow forecasting performance than input lagged time steps and deep learning model architecture variations. Thus, Borkena's river catchment forecasting result is more accurate than Gummera's catchment forecasting result, with RMSE, MAE, MAPE, and R-2 values ranging between (0.81 to 1.53, 0.29 to 0.96, 0.16 to 1.72, 0.96 to 0.99) and (17.43 to 17.99, 7.76 to 10.54, 0.16 to 1.03, 0.89 to 0.90) for both catchments, respectively. Although the performance is dependent on lag time variations, MLP and GRU outperform S-LSTM and Bi-LSTM on a nearly equal basis.

关键词： Streamflow Short-term memory partial autocorrelation function Multilayer perceptron Deep learning Hydrology

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BIFM: Big-Data Driven Intelligent Forecasting Model for COVID-19

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IEEE ACCESS 2021年 9卷 97505-97517页

作者： Dash, Sujata Chakraborty, Chinmay Giri, Sourav Kumar Pani, Subhendu Kumar Frnda, Jaroslav Maharaja Sriram Chandra Bhanja Deo Univ Dept Comp Applicat Baripada 757003 Odisha India Birla Inst Technol Dept Elect & Commun Engn Ranchi 835215 Jharkhand India Krupajal Comp Acad Bhubaneswar 751002 Odisha India Univ Zilina Fac Operat & Econ Transport & Commun Dept Quantitat Methods & Econ Informat Zilina 01026 Slovakia

Ever since the pandemic of Coronavirus disease (COVID-19) emerged in Wuhan, China, it has been recognized as a global threat and several studies have been carried out nationally and globally to predict the outbreak with varying levels of dependability and accuracy. Also, the mobility restrictions have had a widespread impact on people's behavior such as fear of using public transportation (traveling with unknown passengers in the closed area). Securing an appropriate level of safety during the pandemic situation is a highly problematic issue that resulted from the transportation sector which has been hit hard by COVID-19. This paper focuses on developing an intelligent computing model for forecasting the outbreak of COVID-19. The autoregressive integrated moving average (ARIMA) machine learning model is used to develop the best model for twenty-one worst-affected states of India and six worst-hit countries of the world including India. The best ARIMA models are used for predicting the daily-confirmed cases for 90 days future values of six worst-hit countries of the world and six high incidence states of India. The goodness-of-fit measures for the model achieved 85% MAPE for all the countries and all states of India. The above computational analysis will be able to throw some light on the planning and management of healthcare systems and infrastructure.

关键词： COVID-19 Predictive models Diseases Coronaviruses Pandemics Biological system modeling Computational modeling ARIMA models autocorrelation function infectious disease Ljung-box test partial autocorrelation function pandemics time series models white residual transportation

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Modelling HIV/AIDS Cases in Zambia: A Comparative Study of the Impact of Mandatory HIV Testing

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Open Journal of Statistics 2021年第3期11卷 409-419页

作者： Edwin Moyo James C. Shakalima Gilbert Chambashi James Muchinga Levy K. Matindih Department of Science and Mathematics Mulungushi University Kabwe Zambia School of Business Studies Unicaf University Lusaka Zambia Department of Natural Sciences Levy Mwanawasa University Lusaka Zambia

In this study, a time series modeling approach is used to determine an ARIMA model and advance counterfactual forecasting at a point of policy intervention. We consider monthly data of HIV/AIDS cases from the Ministry of Health (Copperbelt province) of Zambia, for the period 2010 to 2019 and have a total of 120 observations. Results indicate that ARIMA (1, 0, 0) is an adequate model which best fits the HIV/AIDS time series data and is, therefore, suitable for forecasting cases. The model predicts a reduction from an average of 3500 to 3177 representing 14.29% in HIV/AIDS cases from 2017 (year of policy activation) to 2019, but the actual recorded cases dropped from 3500 to 1514 accounting for 57.4% in the same time frame.

关键词： Counterfactual Forecasting Box-Jenkins Methodology ARIMA Model Auto-correlation function partial autocorrelation function

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Demand forecasting and PV/EV-integrated energy system modeling for achieving a sustainable island

Sustainable Futures

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Sustainable Futures 2025年 10卷

作者： Mohammad Hasan Ghodusinejad Hossein Yousefi Mohammad Sotoodeh School of Energy Engineering and Sustainable Resources College of Interdisciplinary Science and Technology University of Tehran Tehran Iran

Due to the increasing demand in the electric energy and transportation sector, future planning for these sectors is essential. This paper deals with forecasting electricity and gasoline demand for the Qeshm island located in the south of Iran and modeling its energy system. In this regard, using a time series analysis model, ARIMA, the electricity and gasoline demand of the island in 2030 is forecasted. Then, by compiling different scenarios for the island's power and transportation network, supply and demand modeling for the island's energy system is conducted. Finally, using a multi-criteria decision-making method, the energy scenarios are ranked and the best one is selected. The results of the research showed that increasing the share of solar energy as well as electric vehicles in the island's energy and transportation network, from the worst to the best case, has a direct relationship with reducing energy consumption (up to 17.3 %), reducing emissions (up to 23.8 %) and, as a result, increasing the sustainability of the island. Also, investing only in the development of thermal power plants, regardless of renewable sources and electric transportation, will reduce the economic and environmental efficiency of the island's energy system in the long term.

关键词： Demand forecasting Energy planning ARIMA BWM WASPAS EnergyPLAN ARIMA Auto-Regressive Integrated Moving Average TAC Total Annual Cost BWM Best Worst Method AIC Akaike Information Criterion EV Electric Vehicles OEMOF Open Energy Modelling Framework NSGA-II Non-dominated Sorting Genetic Algorithm II ANN Artificial Neural Network MCDM Multi-Criteria Decision-Making ANP Analytic Network Process VIKOR Vlse Kriterijumsk Optimizacija Kompromisno Resenje ELECTRE ÉLimination Et Choix Traduisant la REalité PROMETHEE-II Preference Ranking Organization METHod for Enrichment of Evaluations PACF partial autocorrelation function TPES Total Primary Energy Supply RES Renewable Energy Share WASPAS Weighted Aggregated Sum Product Assessment BIC , Bayesian Information Criterion XGBoost extreme gradient boosting MILP Mixed-integer Linear Programming SAM System Advisor Model SVM Support Vector Machine AHP Analytic Hierarchy Process WSM Weighted Sum Method TOPSIS Technique for Order Preference by Similarity to Ideal Solution SMART Simple Multi-Attribute Rating Technique ACF autocorrelation function WPM Weighted Product Method

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Bio-inspired MXene membranes for enhanced separation and anti-fouling in oil-in-water emulsions: SHAP explainability ML

Cleaner Water

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Cleaner Water 2024年 2卷

作者： Nadeem Baig Sani I. Abba Jamil Usman Ibrahim Muhammad Ismail Abdulazeez A.G. Usman Isam H. Aljundi Interdisciplinary Research Center for Membrane and Water Security King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Department of Chemical Engineering Prince Mohammad Bin Fahd University Al Khobar 31952 Saudi Arabia Water Research Centre Prince Mohammad Bin Fahd University Al Khobar 31952 Saudi Arabia Department of Pure and Industrial Chemistry Faculty of Science Sokoto State University P.M.B. 2134 Along Birnin Kebbi Road Sokoto Nigeria Operational Research Centre in Healthcare Near East University Nicosia Cyprus Department of Chemical Engineering King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia

Optimizing membrane performance for efficient water treatment is crucial for sustainable development and environmental protection, aligning with UN SDGs. This study involves experimental design, statistical reliability of small data, and explainable machine learning (ML) using SHAP (Shapley Additive Explanations). The research uses ML models and statistical tests to ensure data reliability and stationarity and investigate various membranes’ fouling and separation efficiency (MX-CM, PDMX-CM, and SPDMX-CM). Stationarity tests, including the Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests, revealed that MX-CM is stationary at level (I(0)), while PDMX-CM and SPDMX-CM required first differencing (I(1)) to achieve stationarity. SHAP analysis showed that in the fouling study, higher values of PDMX-CM and MX-CM positively influenced model predictions, with SHAP values of +0.09 for Cycle, −0.06 for PDMX-CM, and −0.06 for MX-CM. In the separation efficiency study, Cycle had a neutral impact (0.00), PDMX-CM had a slight positive effect, and MX-CM had a slight negative impact. These findings highlight the importance of ensuring data stationarity and utilizing SHAP for model explainability in predicting membrane performance. Accurate preprocessing and model interpretation enhance decision-making and optimization in membrane fouling and separation efficiency studies, ensuring robust and reliable ML models.

关键词： ML Machine Learning SHAP Shapley Additive Explanations ADF Augmented Dickey-Fuller PP Phillips-Perron SPDMX-CM Sulfonated Polydopamine functionalized MXene Ceramic Membrane PDMX-CM Polydopamine functionalized MXene Ceramic Membrane LMH Liters per Meter Squared per Hour EPA Environmental Protection Agency SDGs Sustainable Development Goals PDA Polydopamine HF Hydrofluoric Acid SD Standard Deviation ACF autocorrelation function PACF partial autocorrelation function KPSS Kwiatkowski-Phillips-Schmidt-Shin ARIMA Autoregressive Integrated Moving Average MOF Metal-Organic Framework AIC Akaike Information Criterion BK Bartlett Kernel SC Schwarz Criterion TV Test Value CV Critical Value TT Test Type ICC Intraclass Correlation Coefficient KR-20 Kuder-Richardson Formula 20 EDA Exploratory Data Analysis FRR Flux Recovery Ratio TiO2 Titanium Dioxide 2D Two-Dimensional GO Graphene Oxide XAI Explainable Artificial Intelligence LIME Local Interpretable Model-Agnostic Explanations TVET Technical and Vocational Education and Training ANOVA Analysis of Variance PLS partial Least Squares Cf concentration in the feed Cp concentration of the permeate V permeate volume T time F flux environmental sustainability clean water access machine learning MXene reliability analysis SHAP

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Transfer function Modeling for Cotton Yield Prediction in Haryana

Transfer Function Modeling for Cotton Yield Prediction in Ha...

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作者： Naveen Punjab Agricultural University

学位级别：硕士

Crop yield models are abstract presentation of interaction of the crop with its environment and can range from simple correlation of yield with a finite number of variables to the complex statistical models with predictive end. The pre-harvest forecasts are useful to farmers to decide in advance their future prospects and course of action. The study has been categorized in two parts i.e. development of regression based weather-yield models and transfer function models for cotton yield prediction in Hisar, Sirsa, Bhiwani and Fatehabad districts of Haryana. Firstly, the multiple linear regression was used to develop zonal yield models for obtaining cotton yield estimates in the districts under consideration. Linear time-trend was obtained using cotton yield data of the period 1980-81 to 2009-10. The zonal models were fitted by taking DOA yield as dependent variable and fortnightly weather variables along with trend yield/crop condition term as regressors. The validity of fitted models have been checked for the post-sample years 2010-11 to 2014-15. Secondly, the ARIMA models with alternative combinations of weather variables were tried for fitting transfer function models. The fortnightly weather variables selected on the basis of stepwise regression method (viz., RH4, RF3, RF7, SSH4 and SSH7 over the crop growth period) were utilized as input series for fitting TF models. TF (2,1,0) model with RH4 and RF7 for Hisar and TF (0,1,1) model with SSH4 and SSH7 for Sirsa and Fatehabad districts respectively, were finalized to obtain the district-level cotton yield estimates for the post sample period. The performance(s) of the contending models were observed in terms of average absolute percent deviations and RMSEs. Transfer function models consistently showed the superiority over regression based weather-yield models in capturing lower percent deviations in all time regimes. The results showed that the district-level cotton yield(s) prediction gives good agreement with DOA

关键词： Fortnightly maximum temperature minimum temperature Rainfall Sunshine hours and relative humidity trend yield multiple linear regression autocorrelation function partial autocorrelation function transfer function/ARIMA-Intervention models

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Comparative study of ARIMA and ARIMAX models for sugarcane yield forecasting in Northern Agro-climate zone of Haryana

Comparative study of ARIMA and ARIMAX models for sugarcane y...

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作者： Pushpa Punjab Agricultural University

学位级别：硕士

Crop yield. forecasting is. one of the most important aspects of agricultural statistics system. An efficient crop forecasting is pre-requisite for information system about food supply, especially export–import policies, procurement and price-fixation. The study has been performed in two parts i.e. the development of ARIMA and ARIMAX models for sugarcane yield prediction in Karnal, Ambala and Kurukshetra districts of Haryana. The ARIMA models have been fitted using sugarcane yield data for the period 1966-67 to 2011-12 of Karnal and Ambala districts and 1972-73 to 2011-12 of Kurukshetra district. However, the fortnightly weather data from 1978-79 to 2011-12 have been utilized as input for ARIMAX model building. The validity of fitted models have been checked for subsequent years i.e. 2012-13 to 2016-17, not included in the development of the models. ARIMA(0,1,1) for Karnal, Ambala and Kurukshetra district have been fitted for sugarcane yield forecasting. Secondly, the ARIMA models with alternative combinations of weather variables were tried for fitting ARIMAX models. Consequently, ARIMA(0,1,1) for Karnal, Ambala and Kurukshetra districts along with fortnightly weather variables (viz., tmx7, tmn1, tmn10, arf8, arf11 over the crop growth period) as input series were utilized in fitting ARIMAX models. ARIMA and ARIMAX models both provided the relationships to reliably estimate the sugarcane yield. The predictive performance(s) of the contending models were observed in terms of the percent deviations of sugarcane yield forecasts in relation to the observed yield(s) and root mean square error(s). The level of accuracy achieved by ARIMA model(s) with weather as input was considered adequate for predicting sugarcane yield(s) i.e. the ARIMAX models consistently showed superiority over ARIMA models in capturing lower percent deviations pertaining to sugarcane yield estimates. The ARIMAX models performed well with lower error metrics as compared to ARIMA mod

关键词： autocorrelation function partial autocorrelation function Stationarity Invertibility Fortnightly Maximum Temperature Minimum Temperature and Rainfall ARIMA and ARIMAX models

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A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

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ENERGIES 2019年第18期12卷 3586-3586页

作者： Sun, Sizhou Fu, Jingqi Li, Ang Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Anhui Polytech Univ Sch Elect Engn Wuhu 241000 Peoples R China Anhui Polytech Univ Anhui Key Lab Elect Drive & Control Wuhu 241000 Peoples R China

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind

关键词： two-stage decomposition extreme learning machine least square support vector machine wavelet neural network partial autocorrelation function wind power forecasting

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Analysis and Forecasting of the Carbon Price in China's Regional Carbon Markets Based on Fast Ensemble Empirical Mode Decomposition, Phase Space Reconstruction, and an Improved Extreme Learning Machine

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ENERGIES 2019年第2期12卷 277-277页

作者： Sun, Wei Duan, Ming North China Elect Power Univ Dept Econ Management Baoding 071000 Peoples R China

With the development of the carbon market in China, research on the carbon price has received more and more attention in related fields. However, due to its nonlinearity and instability, the carbon price is undoubtedly difficult to predict using a single model. This paper proposes a new hybrid model for carbon price forecasting that combines fast ensemble empirical mode decomposition, sample entropy, phase space reconstruction, a partial autocorrelation function, and an extreme learning machine that has been improved by particle swarm optimization. The original carbon price series is decomposed using the fast ensemble empirical mode decomposition and sample entropy methods, which eliminate noise interference. Then, the phase space reconstruction and partial autocorrelation function methods are combined to determine the input and output variables in the forecasting models. An extreme learning machine optimized by particle swarm optimization was employed to forecast carbon prices. An empirical study based on carbon prices in three typical regional carbon markets in China found that this new hybrid model performed better than other comparable models.

关键词： carbon price forecasting decomposition phase space reconstruction maximal Lyapunov exponent partial autocorrelation function extreme learning machine optimized by particle swarm optimization

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