Simulation of meteorological drought using exponential smoothing models: a study on Bankura District, West Bengal, India

Although the variability and prediction of rainfall is an essential issue of the Santhal Pargana Division of the Jharkhand State but the issue is still far from its’ conclusive statement till date. Therefore, this study aimed to simulate the monthly rainfall from 1901 to 2020 using an eight-step procedure. After downloading the monthly rainfall for the Santhal Pargana Division from 1901 to 2020, the TBATS and Naive models were used to simulate the rainfall. The accuracy assessment of each model was done by using the MASE, MAE, RMSE, ME, and R. For the Naïve model, the Godda station was noticed with a comparatively high combined error. The lowest combined error was found for the Pakur station in case of Naïve models. Similar result was also obtained for the TBATS model. The TBATS was found with comparatively higher accuracy, as the combined error was less for the TBATS. The spatial assessment for the standardized rainfall varied from 84.419 mm. to 149.225 mm. For the Naïve predicted model, the rainfall was marked in between 8.133 mm. to 67.059 mm. For the TBATS fitted model, the rainfall fluctuated from the 37.127 mm. to 62.993 mm. Dumka station was noticed with comparatively low rainfall (i.e.,37.127 mm.). Deoghar and Jamtara stations were marked with a moderate rainfall. Remaining stations were marked with higher amount of rainfall for the TBATS fitted model. The Wilcoxon test proved that each model was significant at 95% confidence interval. The result produced in this research is fruitful enough to be utilized for agricultural planning in the Santhal Pargana Division of the Jharkhand state, India. Keywords : TBATS model; Naive model; simulation; accuracy Copyright (c) 2022 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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“…RMSE represents how spread out the residuals are. The RMSE is represented as follows (Chai & Draxler, 2014;Raha & Gayen, 2020):…”

Section: Root Mean Squared Error (Rmse)mentioning

confidence: 99%

“…There are various simulation models are already developed in the field of civil engineering. The ARIMA model, is indeed the most commonly used model (Raha & Gayen, 2020). Due to its' univariate design and linearity assumption, the model is unable to consider the geographical interrelationships exists in the time series model.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Rainfall Using Two Statistical Data Driven Models: A Study on Santhal Pargana Division of Jharkhand State, India

Raha

Gayen

2022

Geos. Ind.

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“…They indicated that the CANFIS model predicted the SPI better than the other models, and prediction results were different for different meteorological stations and proposed that the CANFIS model can build a reliable expert intelligent system for predicting meteorological drought at multi-time scales and decision-making for remedial measures to cope with meteorological drought and can help to maintain sustainable water resources management (Malik et al 2020). Also, Raha & Gayen (2020) in their study simulated the SPI using double exponential (DE) and Holt-Winter (HW) exponential smoothing models for several time steps (e.g. 3, 6, 12, 24 and 48 months) from 1979 to 2014 in meteorological drought for Bankura District.…”

Section: Water Smartmentioning

confidence: 99%

Climate-smart agriculture (CSA) and extension advisory service (EAS) stakeholders' prioritisation: a case study of Anantapur district, Andhra Pradesh, India

Vincent¹,

Balasubramani²

2021

Journal of Water and Climate Change

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Several climate-smart agriculture (CSA) interventions are promoted by public, private and civil societies in India. However, there is a considerable variation among them. Therefore, to understand the different CSA interventions supported and prioritised by the public and non-governmental organisations (NGOs) as well as their impacts at the farmer level, a case study was undertaken in Anantapur district, as it is highly vulnerable to climate change risks due to the increase in temperature, delayed monsoon, erratic rainfall and frequent occurrence of droughts. A case study research method was followed to assess the CSA interventions promoted by Krishi Vigyan Kendra (KVK), Department of Agriculture, Accion and Adarsha. The findings showed that KVK has focused its extension advisory services on the promotion of field crop (e.g. groundnut)-based CSA. The extension services of NGO-Accion were aimed at promoting horticulture, and Adarsha was prioritised promoting millet-based CSA interventions. Whereas the CSA priority of the department of agriculture was driven by the prevailing zero-budget natural farming project. However, interventions of KVK and NGOs were implemented on a limited scale. Therefore, the recommendations that emerged from the study will help the stakeholders to ensure convergence and foster synergy in implementing CSA interventions at scale. Some challenges faced during the research study were difficulties in the identification of the right stakeholders who were promoting CSA, also their technologies and services related to CSA. However, after a thorough discussion with the extension officers of Anantapur district, the stakeholders were identified and their CSA interventions were ascertained through focus group discussions and secondary data reviewed from magazines and other publications. Furthermore, the present study focused only on the CSA interventions promoted by two public sectors and two NGOs, and there is a wider scope for identifying more stakeholders, e.g. private sector, FPOs and entrepreneurs, and assessing their extent of involvement in the promotion of CSA and prioritisation.

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“…Suppose the current time is t, and the observed value of the corresponding clock error series is x 1 , x 2 , • • • , x t , then the ES1 value of the clock bias time series at this time is [35,36]…”

Section: Exponential Smoothing Prediction Modelmentioning

confidence: 99%

A New BDS-2 Satellite Clock Bias Prediction Algorithm with an Improved Exponential Smoothing Method

Huang

Wang

et al. 2020

Applied Sciences

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High-accuracy and dependable prediction of the bias of space-borne atomic clocks is extremely crucial for the normal operation of the satellites in the case of interrupted communication. Currently, the clock bias prediction for the Chinese BeiDou Navigation Satellite System (BDS) remains still a huge challenge. To develop a high-precision approach for forecasting satellite clock bias (SCB) in allusion to analyze the shortcomings of the exponential smoothing (ES) model, a modified ES model is proposed hereof, especially for BDS-2 satellites. Firstly, the basic ES models and their prediction mechanism are introduced. As the smoothing coefficient is difficult to determine, this leads to increasing fitting errors and poor forecast results. This issue is addressed by introducing a dynamic “thick near thin far (TNTF)” principle based on the sliding windows (SW) to optimize the best smoothing coefficient. Furthermore, to enhance the short-term forecasted accuracy of the ES model, the gray model (GM) is adopted to learn the fitting residuals of the ES model and combine the forecasted results of the ES model with the predicted results of the GM model from error learning (ES + GM). Compared with the single ES models, the experimental results show that the short-term forecast based on the ES + GM models is improved remarkably, especially for the combination of the three ES model and GM model (ES3 + GM). To further improve the medium-term prediction accuracy of the ES model, the new algorithms in ES with GM error learning based on the SW (ES + GM + SW) are presented. Through examples analysis, compared with the single ES2 (ES3) model, results indicate that (1) the average forecast precision of the new algorithms ES2 + GM + SW (ES3 + GM + SW) can be dramatically enhanced by 49.10% (56.40%) from 5.56 ns (6.77 ns) to 2.83 ns (2.95 ns); (2) the average forecast stability of the new algorithms ES2 + GM + SW (ES3 + GM + SW) is also observably boosted by 53.40% (49.60%) from 8.99 ns (16.13 ns) to 4.19 ns (8.13 ns). These new coupling forecast models proposed in this contribution are more effective in clock bias prediction both forecast accuracy and forecast stability. BeiDou satellite navigation system (BDS); satellite clock bias (SCB) prediction; exponential smoothing (ES) method; smoothing coefficient; thick near thin far (TNTF) principle; sliding windows (SW)

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Simulation of meteorological drought using exponential smoothing models: a study on Bankura District, West Bengal, India

Cited by 20 publications

References 60 publications

Simulation of Rainfall Using Two Statistical Data Driven Models: A Study on Santhal Pargana Division of Jharkhand State, India

Simulation of Rainfall Using Two Statistical Data Driven Models: A Study on Santhal Pargana Division of Jharkhand State, India

Climate-smart agriculture (CSA) and extension advisory service (EAS) stakeholders' prioritisation: a case study of Anantapur district, Andhra Pradesh, India

A New BDS-2 Satellite Clock Bias Prediction Algorithm with an Improved Exponential Smoothing Method

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