Evaluation of Infilling Methods for Time Series of Daily Temperature Data: Case Study of Limpopo Province, South Africa

Shabalala, Zakhele Phumlani; Moeletsi, Mokhele Edmond; Tongwane, Mphethe Isaac; Mazibuko, Sabelo M.

doi:10.3390/cli7070086

Cited by 20 publications

(10 citation statements)

References 34 publications

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“…The present study was performed at Zuurfontein farm in Polokwane. The temperature in winter ranges from 7°C to 21°C and in summer ranges from 16°C to 28.1°C and experiences an annual rainfall of more than 600 mm [15].…”

Section: Study Period and Areamentioning

confidence: 99%

Predicting body weight of Kalahari Red goats from linear body measurements using data mining algorithms

et al. 2022

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Background and Aim: The Kalahari Red goat breed is the finest meat-producing species in South Africa, and its coat color ranges from light to dark red-brown. A practical approach to estimating their body weight (BW) using linear body measurements is still scarce. Therefore, this study aimed to determine the best data mining technique among classification and regression trees (CART), Chi-square automatic interaction detection (CHAID), and exhaustive CHAID (Ex-CHAID) for predicting the BW of Kalahari Red goats. Materials and Methods: This study included 50 Kalahari Red goats (does = 42 and bucks = 8) aged 3–5 years. Body length (BL), heart girth (HG), rump height (RH), height at withers (WH), sex, and age were the essential indicators to estimate BW. The best model was chosen based on the goodness of fit, such as adjusted coefficient of determination (Adj. R2), coefficient of determination (R2), root mean square error (RMSE), standard deviation ratio (SD ratio), mean absolute percentage error, Akaike information criteria, relative approximation error, and coefficient of variation. Results: The SD values of the ratio ranged from 0.32 (CART) to 0.40 (Ex-CHAID). The greatest R2 (%) was established for CART (89.23), followed by CHAID (81.99), and the lowest was established for Ex-CHAID (81.70). CART was established as the preferred algorithm with BL, HG, and WH as critical predictors. The heaviest BW (73.50 kg) was established in four goats with BL higher than 92.5 cm. Conclusion: This study reveals that CART is the optimum model with BL, HG, and WH as the essential linear body measurements for estimating BW for Kalahari Red goats. The updated records will assist the rural farmers in making precise judgments for various objectives, such as marketing, breeding, feeding, and veterinary services in remote areas where weighing scales are unavailable.

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Section: Study Period and Areamentioning

confidence: 99%

Predicting body weight of Kalahari Red goats from linear body measurements using data mining algorithms

et al. 2022

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“…The NR method was selected so as to estimate the missing daily precipitation and daily temperature values since similar studies have found that it outperformed other daily rainfall infilling methods during the use of two to five index gauges (Mair and Fares, 2010 ). It is also a recommended infilling method for timeseries of daily temperature data (Shabalala et al 2019 ) and is expressed as follows: where P x is the missing value of precipitation or temperature at meteorological station x , P i is the observed precipitation or temperature value for the same period at the ith neighbouring station, N x is the normal annual precipitation or temperature of the station x , N i is the normal annual precipitation or temperature of the ith station and n is the number of neighbouring stations. Moreover, the Milos M.S.…”

Section: Study Area and Description Of Datasetsmentioning

confidence: 99%

Section: Study Area and Description Of Datasetsmentioning

confidence: 99%

Changes in climatic patterns and tourism and their concomitant effect on drinking water transfers into the region of South Aegean, Greece

Myronidis

Theofanous

2021

Stoch Environ Res Risk Assess

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Global warming is already having a negative impact on vital sectors on which human development depends, such as water resource availability. In this study, the changes and abrupt change timing of climatic extreme indices, aridity and drought over the Region of South Aegean are captured using the Mann–Kendall and Pettitt tests, while the latter variables are correlated with the water volume transported by ships to the region as well as the relevant costs. The region's climate is shifting to warmer conditions with less precipitation, since significantly positive trends were noted with regard to the number of tropical nights, warm nights, warm days, the warm spell duration index and the diurnal temperature range; significant negative trends were observed in relation to the number of cool nights, cool days and the cold spell duration index, with the change-point year for the latter variables being 2006. Inaddition, 7/11 precipitation related indices exhibited a downward trend, while significantly negative trends were observed with regard to the number of consecutive dry days, with the timing of the abrupt change being 2001. The Aridity Index (AI) reveals that the region’s climate characterization is changing from dry and sub-humid to semi-arid conditions, whilst the Reconnaissance Drought Index standardized (RDI st ) and the Standardized Precipitation Index (SPI) indices suggests an amplification of drought phenomena over the Region. The tourism variables illustrated a significant positive trend, with the timing of the abrupt change being registered during 2006–2009, whilst the correlation analysis between tourism variables and water transfers implies that the surge on water transfer by ships to the Region occurred between 1998 and 2008. This can be mainly attributed to the changes in climate patterns. The correlation analysis documents a strong positive correlation between the water transfer dataset and the diurnal temperature range, and a moderately negative association with the precipitation related indices, annual precipitation, drought phenomena and aridity with 7/11.

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“…Furthermore, erroneous, suspicious and impossible values were patched using good quality data from nearby weather stations (within a radius of 100 km) to obtain complete long-term data sets of good quality. An inverse distance weighting method was used to estimate missing or erroneous daily rainfall and RH from neighbouring station data based on the recommendations of Moeletsi et al 30 The multiple regression method was used to estimate missing or erroneous T air min , T air max and U values from neighbouring station data based on the recommendations of Shabalala et al 34 The Hargreaves-Samani equation was used to estimate missing or erroneous daily R s from measurements of T air min and T air max based on the recommendations of Abraha and Savage 35 .…”

Section: Climate Datamentioning

confidence: 99%

Development and analysis of a long-term soil moisture data set in three different agroclimatic zones of South Africa

2021

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Understanding the potential impacts of climate variability/change on soil moisture is essential for the development of informed adaptation strategies. However, long-term in-situ soil moisture measurements are sparse in most countries. The objectives of this study were to develop and analyse the temporal variability of a long-term soil moisture data set in South Africa. In this study, a water balance model was used to reconstruct long-term soil moisture data sets from 1980 through 2018, in three sites that represent the diverse agroclimatic conditions of South Africa. Additionally, long-term changes and variability of soil moisture were examined to investigate the potential impacts of climate variability on soil moisture. The results of the Mann–Kendall test showed a non-significant decreasing trend of soil moisture for inland stations at a rate between -0.001 and -0.02 mm per annum. In contrast, a statistically significant (at 5% level of significance) increasing trend of soil moisture for a coastal station at a rate of 0.1131 mm per annum was observed. The findings suggest that the Bainsvlei and Bronkhorstspruit stations located in the inland region are gradually becoming drier as a result of decreasing rainfall and increasing air temperature. In contrast, the Mandeni station located in the coastal region is becoming wetter as a result of increasing rainfall, despite the increase in air temperature. The findings indicate that climate variability is likely to change the soil moisture content, although the influence will vary with region and climatic conditions. Therefore, understanding the factors that affect soil moisture variability at the local scale is critical for the development of informed and effective adaptation strategies.

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Evaluation of Infilling Methods for Time Series of Daily Temperature Data: Case Study of Limpopo Province, South Africa

Cited by 20 publications

References 34 publications

Predicting body weight of Kalahari Red goats from linear body measurements using data mining algorithms

Predicting body weight of Kalahari Red goats from linear body measurements using data mining algorithms

Changes in climatic patterns and tourism and their concomitant effect on drinking water transfers into the region of South Aegean, Greece

Development and analysis of a long-term soil moisture data set in three different agroclimatic zones of South Africa

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