Forecasting environmental factors and zooplankton of Bakreswar reservoir in India using time series model

Banerjee, Arnab; Chakrabarty, Moitreyee; Bandyopadhyay, Gautam; Roy, Priti Kumar; Ray, Santanu

doi:10.1016/j.ecoinf.2020.101157

Cited by 14 publications

(4 citation statements)

References 61 publications

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“…56 Furthermore, since atmospheric circulation plays an important role in pest immigration, utilizing indices such as the intensity of the western Pacific subtropical high-pressure system 13 could further help understand migration patterns and the timing of initial occurrence of S. litura in May or June at the study sites. Furthermore, ANN-ARIMA models that combine artificial intelligencebased techniques with a conventional ARIMA model have also been developed in recent years, 57 and models incorporating these new nonlinear regression techniques are expected to further improve the accuracy of pest occurrence prediction.…”

Section: Potential Exogenous Factors Affecting the Occurrence Of S Li...mentioning

confidence: 99%

Time‐series analysis of population dynamics of the common cutworm,Spodoptera litura(Lepidoptera: Noctuidae), using anARIMAXmodel

Kawakita

Takahashi

2022

Pest Management Science

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BACKGROUND Developing a model that adequately explains pest population dynamics based on weather‐related parameters is fundamentally important for proper pest management. Autocorrelation with past occurrences should be considered when modeling the relationship between the time series of pest occurrence data and meteorological factors; however, few attempts have been made to model these factors simultaneously. In this study, we constructed an autoregressive integrated moving average with exogenous variables (ARIMAX) model to represent the occurrence of the common cutworm, Spodoptera litura (F.) (Lepidoptera: Noctuidae), a major moth pest species in Asia, using the trap catch data of S. litura recorded approximately every 5 days. The multiple meteorological measurements taken over several past periods before S. litura occurrence were included as explanatory variables to evaluate their lag effects on future occurrences. RESULTS It was suggested that temperature had the most important effect on S. litura occurrences among other meteorological factors (i.e., humidity, wind speed, and precipitation). Especially, higher temperatures during the larval/egg stage seemed to presage a higher moth abundance. When the model was fitted using independent data that were not used for calibrating the model, the model was able to capture trends in increases in the scale of occurrence, particularly after July, when the occurrence rapidly increased. CONCLUSION Past temperature condition, particularly during the larval and egg states, is suggested to be highly important for predicting future S. litura occurrences. The ARIMAX model proposed here will allow preventive measures to be taken, effectively safeguarding food resources against pest outbreaks. © 2022 Society of Chemical Industry.

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Section: Potential Exogenous Factors Affecting the Occurrence Of S Li...mentioning

confidence: 99%

Time‐series analysis of population dynamics of the common cutworm,Spodoptera litura(Lepidoptera: Noctuidae), using anARIMAXmodel

Kawakita

Takahashi

2022

Pest Management Science

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“…However, in recent years, the pollution of water resources has become increasingly severe as the global economy has developed rapidly. The management and effective protection of water quality and the ecological environments of various regions and river basins are of great strategic significance to global sustainable development (Banerjee et al, 2020). Water quality forecasting is a primary concern of water environmental protection and an important basis for solving the water resources crisis.…”

Section: Introductionmentioning

confidence: 99%

Combined water quality forecasting system based on multiobjective optimization and improved data decomposition integration strategy

Dong

Wang

Niu

et al. 2022

Journal of Forecasting

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“…The growing scarcity of fresh, clean water is one of the most pressing concerns confronting civilization in the twenty-first century [1]. Recent research has proven climate change will have a significant impact on freshwater supplies due to the probable reduction in rainfall [2].…”

Section: Introductionmentioning

confidence: 99%

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

Khudhair

Zubaidi²,

Ortega‐Martorell³

et al. 2022

Environments

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Water quality has a significant influence on human health. As a result, water quality parameter modelling is one of the most challenging problems in the water sector. Therefore, the major factor in choosing an appropriate prediction model is accuracy. This research aims to analyse hybrid techniques and pre-processing data methods in freshwater quality modelling and forecasting. Hybrid approaches have generally been seen as a potential way of improving the accuracy of water quality modelling and forecasting compared with individual models. Consequently, recent studies have focused on using hybrid models to enhance forecasting accuracy. The modelling of dissolved oxygen is receiving more attention. From a review of relevant articles, it is clear that hybrid techniques are viable and precise methods for water quality prediction. Additionally, this paper presents future research directions to help researchers predict freshwater quality variables.

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Forecasting environmental factors and zooplankton of Bakreswar reservoir in India using time series model

Cited by 14 publications

References 61 publications

Time‐series analysis of population dynamics of the common cutworm,Spodoptera litura(Lepidoptera: Noctuidae), using anARIMAXmodel

Time‐series analysis of population dynamics of the common cutworm,Spodoptera litura(Lepidoptera: Noctuidae), using anARIMAXmodel

Combined water quality forecasting system based on multiobjective optimization and improved data decomposition integration strategy

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

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