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Quality and reliable drought prediction is essential for mitigation strategies and planning in disaster-stricken regions globally. Prediction models such as empirical or data-driven models play a fundamental role in forecasting drought. However, selecting a suitable prediction model remains a challenge because of the lack of succinct information available on model performance. Therefore, this review evaluated the best model for drought forecasting and determined which differences if any were present in model performance using standardised precipitation index (SPI). In addition, the most effective combination of the SPI with its respective timescale and lead time was investigated. The effectiveness of data-driven models was analysed using meta-regression analysis by applying a linear mixed model to the coefficient of determination and the root mean square error of the validated model results. Wavelet-transformed neural networks had superior performance with the highest correlation and minimum error. Preprocessing data to eliminate non-stationarity performed substantially better than did the regular artificial neural network (ANN) model. Additionally, the best timescale to calculate the SPI was 24 and 12 months and a lead time of 1–3 months provided the most accurate forecasts. Studies from China and Sicily had the most variation based on geographical location as a random effect; while studies from India rendered consistent results overall. Variation in the result can be attributed to geographical differences, seasonal influence, incorporation of climate indices and author bias. Conclusively, this review recommends use of the wavelet-based ANN (WANN) model to forecast drought indices.
相似文献Biogrouting is a new ground improvement method that has been studied in recent years. This method involves mainly the use of a microbially induced calcite precipitation process to bind soil particles to increase the strength or to fill in the pores of soil or joints of rock for seepage control. There are two major challenges in the use of biogrout for seepage control through rock joints. The first is how to inject the biogrout solutions, and the second is to understand the mechanisms for the formation of calcite under seepage flow. In this paper, a study on the injection of biogrout solution and the formation of precipitates along a circular 1D flow channel is presented. To minimize the influence of flow, a new one-phase injection method to inject bacterial solution and cementation agents simultaneously was adopted in this study. Factors affecting the formation and distribution of precipitates along the flow channel such as flow velocity, flow rate, and aperture of flow channel were investigated. The experimental results indicated that less calcite was precipitated at locations further away from the injection point due to depletion of the reactants’ concentrations along the flow path. Using the one-phase injection method, the bacterial activity had a major effect on the accumulation of the calcite on the inner surface of the flow channel. The total calcite precipitated on the surface of the flow channel increased slightly with increasing bacterial activity or flow rate. An equation to predict the distance travelled by the biosolution has been derived based on the testing results.
相似文献Near-infrared (NIR) and short-wavelength infrared (SWIR) hyperspectral imagery can be used to detect certain alteration minerals. At epithermal deposits, the formation of alteration minerals is, in theory, related to the mineralisation of gold and silver. In order to provide foundations for developing sensor-based sorting applications at a mine that exploits such a deposit, it was investigated if NIR-SWIR hyperspectral imagery can be used to distinguish between ore and waste particles by characterising the alteration mineralogy. Maps were produced from the NIR-SWIR hyperspectral images of 827 drill core samples that show mineral occurrences, mineral absorption feature intensities and characteristics of the iron oxide mineralogy. Partial least squares discriminant analysis (PLS-DA) was applied to the information contained in these maps to investigate if this information can be used to discriminate between ore and waste. The results showed that NIR-SWIR hyperspectral imagery could be used to segment a population of waste samples by detecting occurrences of pyrophyllite, dickite and/or illite. This result can be explained by the fact that these minerals are commonly deposited further away from the ore-bearing epithermal veins, while the absence of SWIR-active minerals or detected occurrences of alunite are more closely associated with these structures. The ability to identify waste with NIR-SWIR spectral sensors means there is potential that sensor-based sorting can be used to remove this waste from mineral processing operations. Additional research is still required to assess the economic feasibility of such a sensor-based sorting application.
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