Developing drought index–based forecasts for tropical climates using wavelet neural network: an application in Fiji

Anshuka, Anshuka; Buzacott, Alexander; Vervoort, W.; Ogtrop, Floris F. van

doi:10.1007/s00704-020-03446-3

Cited by 14 publications

(8 citation statements)

References 65 publications

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“…Wavelet analysis can be used to study trends, periodicity, randomness, mutations and "multi time scale" structures in time series and can clearly reveal various changes hidden in nonstationary time series, such as those of precipitation, temperature and humidity [43,44]. In this paper, Morlet wavelet analysis is used to analyze the variation in vegetation drought in four geographical regions of China.…”

Section: Discussionmentioning

confidence: 99%

VCI-Based Analysis on Spatiotemporal Variations of Spring Drought in China

Liang

Qiu

Yan

et al. 2021

IJERPH

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The analysis of spatiotemporal variations in drought is important for environmental monitoring and agricultural production. In this study, the spring vegetative drought conditions in China were analyzed by using the vegetation condition index (VCI) as an indicator to reveal the drought characteristics in China from 1981–2015. The results suggest that spring vegetative drought (especially moderate drought) occurs frequently in China, and drought conditions have obvious geographical differences and are highly affected by monsoons. The frequency of spring vegetative drought is relatively high in the southern and northern regions, which are greatly affected by monsoons, and is relatively low in the northwestern and Qinghai-Tibet regions, which are less affected by monsoons. During 1981–2015, the spring VCI in China showed an overall upward trend. In addition, the trend was not a single change but a wave-like increasing trend that can be divided into four stages: (1) a stage of slow growth from 1981–1990, (2) a stage of intense fluctuations from 1991–2000, (3) a stage of steady growth from 2001–2010, and (4) a stage of slow descent after 2010. The Mann–Kendall test confirmed that the spring VCI in China was increasing, and the changes in the southern, northwestern, and Qinghai-Tibet regions reached significant levels. The time point of mutation in the southern region was 2000, and that in the northwestern and Qinghai-Tibet regions was 1992. Wavelet time series analysis showed that spring vegetation drought in China has a short-period oscillation of 5–7 years and a long-period oscillation of approximately 23–28 years. The northwestern and Qinghai-Tibet regions, which are less affected by the monsoons, are dominated by long-period oscillations, while the southern and northern regions, which are more affected by the monsoons, are dominated by short-period oscillations.

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Section: Discussionmentioning

confidence: 99%

VCI-Based Analysis on Spatiotemporal Variations of Spring Drought in China

Liang

Qiu

Yan

et al. 2021

IJERPH

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“…The advantages and disadvantages of drought indices are based upon the clarity and provisional flexibilities of their administration Standardized Precipitation Index (SPI) has been endorsed as a standard drought index by the world meteorological organization that is likely due to its simple calculations, as precipitation data is itself enough to direct and test without the demand of statistical barriers. Furthermore, the SPI is also capable of showing high performance in finding and computing drought potency [18,19]. Despite of all this ease, the SPI still has some issues regarding water balance.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Drought: A Case Study of Water-Stressed Region of Pakistan

et al. 2021

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Demand for water resources has increased dramatically due to the global increase in consumption of water, which has resulted in water depletion. Additionally, global climate change has further resulted as an impediment to human survival. Moreover, Pakistan is among the countries that have already crossed the water scarcity line, experiencing drought in the water-stressed Thar desert. Drought mitigation actions can be effectively achieved by forecasting techniques. This research describes the application of a linear stochastic model, i.e., Autoregressive Integrated Moving Average (ARIMA), to predict the drought pattern. The Standardized Precipitation Evapotranspiration Index (SPEI) is calculated to develop ARIMA models to forecast drought in a hyper-arid environment. In this study, drought forecast is demonstrated by results achieved from ARIMA models for various time periods. Result shows that the values of p, d, and q (non-seasonal model parameter) and P, D, and Q (seasonal model parameter) for the same SPEI period in the proposed models are analogous where “p” is the order of autoregressive lags, q is the order of moving average lags and d is the order of integration. Additionally, these parameters show the strong likeness for Moving Average (M.A) and Autoregressive (A.R) parameter values. From the various developed models for the Thar region, it has been concluded that the model (0,1,0)(1,0,2) is the best ARIMA model at 24 SPEI and could be considered as a generalized model. In the (0,1,0) model, the A.R term is 0, the difference/order of integration is 1 and the moving average is 0, and in the model (1,0,2) whose A.R has the 1st lag, the difference/order of integration is 0 and the moving average has 2 lags. Larger values for R2 greater than 0.9 and smaller values of Mean Error (ME), Mean Absolute Error (MAE), Mean Percentile Error (MPE), Mean Absolute Percentile Error (MAPE), and Mean Absolute Square Error (MASE) provide the acceptance of the generalized model. Consequently, this research suggests that drought forecasting can be effectively fulfilled by using ARIMA models, which can be assist policy planners of water resources to place safeguards keeping in view the future severity of the drought.

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“…Compared to the regression analysis model, the method improved the accuracy of corn N content estimation. Wavelet neural network (WNN) [ 31 ] is a type of artificial neural network, that is generated by applying wavelet analysis theory to neural network theory. This network exhibits strong non-linear mapping and learning abilities [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

“…Wavelet neural network (WNN) [ 31 ] is a type of artificial neural network, that is generated by applying wavelet analysis theory to neural network theory. This network exhibits strong non-linear mapping and learning abilities [ 31 ]. Current studies on WNN traverse several fields, including medicine [ 32 ], industry [ 33 ], and finance [ 34 ] and has achieved satisfactory results.…”

Section: Introductionmentioning

confidence: 99%

Estimation of nitrate nitrogen content in cotton petioles under drip irrigation based on wavelet neural network approach using spectral indices

Dong

Liu

et al. 2021

Plant Methods

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Background Estimation of nitrate nitrogen (NO3−–N) content in petioles is one of the key approaches for monitoring nitrogen (N) nutrition in crops. Rapid, non-destructive, and accurate evaluation of NO3−–N contents in cotton petioles under drip irrigation is of great significance. Methods In this study, we discussed the use of hyperspectral data to estimate NO3−–N contents in cotton petioles under drip irrigation at different N treatments and growth stages. The correlations among trilateral parameters and six vegetation indices and petiole NO3−–N contents were first investigated, after which a traditional regression model for petioles NO3−–N content was established. A wavelet neural network (WNN) model for estimating petiole NO3−–N content was also established. In addition, the performance of WNN was compared to those of random forest (RF), radial basis function neural network (RBF) and back propagation neural network (BP). Results Between the blue edge amplitude (Db) and blue edge area (SDb) of the blue edge parameters was the optimal index for the estimation model of petiole NO3−–N content. We found that the prediction results of the blue edge parameters and WNN were 7.3% higher than the coefficient of determination (R2) of the first derivative vegetation index and WNN. Root mean square error (RMSE) and mean absolute error (MAE) were 25.2% and 30.9% lower than first derivative vegetation, respectively, and the performance was better than that of RF, RBF and BP. Conclusions An inexpensive approach consisting of the WNN algorithm and blue edge parameters can be used to enhance the accuracy of NO3−–N content estimation in cotton petioles under drip irrigation.

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Developing drought index–based forecasts for tropical climates using wavelet neural network: an application in Fiji

Cited by 14 publications

References 65 publications

VCI-Based Analysis on Spatiotemporal Variations of Spring Drought in China

VCI-Based Analysis on Spatiotemporal Variations of Spring Drought in China

Forecasting of Drought: A Case Study of Water-Stressed Region of Pakistan

Estimation of nitrate nitrogen content in cotton petioles under drip irrigation based on wavelet neural network approach using spectral indices

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