Autoregressive models in environmental forecasting time series: a theoretical and application review

J Kaur, KS Parmar, S Singh - Environmental Science and Pollution …, 2023 - Springer
Though globalization, industrialization, and urbanization have escalated the economic
growth of nations, these activities have played foul on the environment. Better understanding …

An overview on source, mechanism and investigation approaches in groundwater salinization studies

M Mirzavand, H Ghasemieh, SJ Sadatinejad… - International Journal of …, 2020 - Springer
Groundwater quality, as major source of freshwater, is an important factor in sustainable
development and water resources management. Due to increase in water demand in …

Bayesian model averaging by combining deep learning models to improve lake water level prediction

G Li, Z Liu, J Zhang, H Han, Z Shu - Science of the Total Environment, 2024 - Elsevier
Water level (WL) is an essential indicator of lakes and sensitive to climate change.
Fluctuations of lake WL may significantly affect water supply security and ecosystem stability …

Comparison of strategies for multistep-ahead lake water level forecasting using deep learning models

G Li, Z Shu, M Lin, J Zhang, X Yan, Z Liu - Journal of Cleaner Production, 2024 - Elsevier
Accurate forecasting of multistep-ahead lake water level is valuable for extreme disaster
prevention and eco-environmental protection. However, existing studies mainly focus on …

Analytical approach for time‐dependent groundwater inflow into shield tunnel face in confined aquifer

XX Liu, SL Shen, YS Xu, ZY Yin - International Journal for …, 2018 - Wiley Online Library
Prediction of time‐dependent groundwater inflow into a shield tunnel is a significant task
facing engineers. Published literature shows that there is no available method with which to …

Comparative study among different time series models applied to monthly rainfall forecasting in semi-arid climate condition

M Dastorani, M Mirzavand, MT Dastorani… - Natural Hazards, 2016 - Springer
The aim of this study is to investigate the ability of different time series models in forecasting
monthly rainfall. In order to do this, monthly rainfall data were collected from 9 rainfall …

Studies on predicting soil moisture levels at Andhra Loyola College, India, using SARIMA and LSTM models

MT Kumar, MC Rao - Environmental Monitoring and Assessment, 2023 - Springer
Time series modeling is a way to predict future values by examining temporal data. The
present study analyzes the monthly mean soil moisture data at various depths: surface …

[PDF][PDF] Data mining approaches to predict the factors that affect the agriculture growth using stochastic model

P Rajesh, M Karthikeyan - International Journal of Computer …, 2019 - researchgate.net
In the recent times, there has been an increasing demand for efficient strategies in the data
mining in agriculture prediction. Data mining is equipment to predict effectively by stochastic …

Data mining approaches to predict the factors that affect the groundwater level using stochastic model

P Rajesh, M Karthikeyan, R Arulpavai - AIP Conference Proceedings, 2019 - pubs.aip.org
In the recent times, there has been an increasing demand for efficient strategies in the field
of data assimilation about groundwater. Data mining process is a discovery of hiding …

An empirical study on rainfall patterns of monsoon season in the north-west India using time series models

K Polisetty, AY Ebenezer - Journal of Statistics and Management …, 2021 - Taylor & Francis
Rainfall is an important natural source of water, which is the most essential component of
living organisms. The study of rainfall modeling and forecasting are critically important for an …