Modeling and forecasting of rainfall and temperature time series in East Wollega Zone, Western Ethiopia

Bekuma, Teka; Mamo, Girma; Regassa, Alemayehu

doi:10.1007/s12517-022-10638-w

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…Te crop production in Ethiopia including the Muger subwatershed depends on these two rainy seasons. Terefore, a small fuctuation in the rainfall amount, intensity, duration, onset days, and cessation days signifcantly afects the agricultural activities of farmers [12,24,28].…”

Section: Temporal Variability Analysis Resultsmentioning

confidence: 99%

“…Nowadays, examining the variability and trends of weather over space and time is becoming an area of serious concern. Previous studies conducted on climate variability, for example, Larbi et al [6], Taxak et al [7], Asfaw et al [8], Miheretu [9], Adugna et al [10], Kale and Nagesh Kumar [11], Bekuma et al [12], Hussain et al [13], Birara et al [14], Ma et al [15], Serrano-Notivoli et al [16], and Takano-Rojas et al [17] stated the signifcances of serious examination and monitoring of potential impacts and planning of possible adaptation strategies in diferent sectors. Moreover, Duan et al [18] studied the annual, seasonal, and monthly precipitation trends for the northern Japanese Island of Hokkaido using the Mann-Kendall test and geostatistical interpolation techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, the high and low rainfall phenomena can give rise, respectively, to food and drought conditions with adverse economic and humanitarian crises [27]. In Ethiopia, agricultural practice is dependent on rainfall [10,12,24,28].…”

Section: Introductionmentioning

confidence: 99%

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Temporal Dynamics and Trend Analysis of Areal Rainfall in Muger Subwatershed, Upper Blue Nile, Ethiopia

Moshe,

Beza

2024

Advances in Meteorology

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This study was employed to investigate the temporal variability and trend analysis of areal rainfall in the Muger subwatershed, Upper Blue Nile, Ethiopia. The study was run over the following procedures to handle the main objective: (1) determining the areal rainfall from gauged point rainfall using the Thiessen polygon method, (2) grouping the months in the season according to the study area, (3) evaluating the temporal dynamics of annual and seasonal areal rainfall using the coefficient of variation (CV), standard anomaly index (SAI), and precipitation concentration index (PCI), and (4) analyzing the trend of annual and seasonal areal rainfall using modified Mann–Kendall’s (modifiedmk) test in RStudio. Based on the temporal variability analysis, CV results depict that annual and summer areal rainfall had low variability with values of 13.43% and 13.7%, respectively. Winter and spring areal rainfall shows high variation with a CV value of 50.5% and 36%, respectively. According to the SAI output, around 70% of the considered year was in the normal condition of wetness. On the other hand, the seasonal (winter, spring, and summer) rainfall distribution of the study area shows strong irregularity distribution throughout the considered years as a result of PCI with a value of 57.5%. The trend of the areal rainfall was shown to be both increasing and decreasing. However, the trend was insignificant with a 10% confidence level.

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Section: Temporal Variability Analysis Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Temporal Dynamics and Trend Analysis of Areal Rainfall in Muger Subwatershed, Upper Blue Nile, Ethiopia

Moshe,

Beza

2024

Advances in Meteorology

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Predicting rainfall in Nineveh Governorate in northern Iraq using machine learning time-series forecasting algorithm

Abdaki,

Al-Ozeer,

Alobaydy

et al. 2023

Arab J Geosci

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Forecasting arabica coffee yields by auto-regressive integrated moving average and machine learning approaches

Kittichotsatsawat,

Boonprasope,

Rauch

et al. 2023

AIMSAGRI

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<abstract> Coffee is a major industrial crop that creates high economic value in Thailand and other countries worldwide. A lack of certainty in forecasting coffee production could lead to serious operation problems for business. Applying machine learning (ML) to coffee production is crucial since it can help in productivity prediction and increase prediction accuracy rate in response to customer demands. An ML technique of artificial neural network (ANN) model, and a statistical technique of autoregressive integrated moving average (ARIMA) model were adopted in this study to forecast arabica coffee yields. Six variable datasets were collected from 2004 to 2018, including cultivated areas, productivity zone, rainfalls, relative humidity and minimum and maximum temperatures, totaling 180 time-series data points. Their prediction performances were evaluated in terms of correlation coefficient (R2), and root means square error (RMSE). From this work, the ARIMA model was optimized using the fitting model of (p, d, q) amounted to 64 conditions through the Akaike information criteria arriving at (2, 1, 2). The ARIMA results showed that its R2 and RMSE were 0.7041 and 0.1348, respectively. Moreover, the R2 and RMSE of the ANN model were 0.9299 and 0.0642 by the Levenberg-Marquardt algorithm with TrainLM and LearnGDM training functions, two hidden layers and six processing elements. Both models were acceptable in forecasting the annual arabica coffee production, but the ANN model appeared to perform better. </abstract>

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Modeling and forecasting of rainfall and temperature time series in East Wollega Zone, Western Ethiopia

Cited by 3 publications

References 28 publications

Temporal Dynamics and Trend Analysis of Areal Rainfall in Muger Subwatershed, Upper Blue Nile, Ethiopia

Temporal Dynamics and Trend Analysis of Areal Rainfall in Muger Subwatershed, Upper Blue Nile, Ethiopia

Predicting rainfall in Nineveh Governorate in northern Iraq using machine learning time-series forecasting algorithm

Forecasting arabica coffee yields by auto-regressive integrated moving average and machine learning approaches

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