Downscaling of Open Coarse Precipitation Data Using a Machine Learning Algorithm

In this chapter, the authors propose a novel statistical model with a residual correction of downscaling coarse precipitation TRMM 3B43 product. The presented study was carried out over Morocco, and the objective is to improve statistical downscaling for TRMM 3B43 products using a machine learning algorithm. Indeed, the statistical model is based on the Transformed Soil Adjusted Vegetation Index (TSAVI), elevation, and distance from the sea. TSAVI was retrieved using the quantile regression method. Stepwise regression was implemented with the minimization of the Akaike information criterion and Mallows' Cp indicator. The model validation is performed using ten in-situ measurements from rain gauge stations (the most available data). The result shows that the model presents the best fit of the TRMM 3B43 product and good accuracy on estimating precipitation at 1km according to 𝑅2, RMSE, bias, and MAE. In addition, TSAVI improved the model accuracy in the humid bioclimatic stage and in the Saharan region to some extent due to its capacity to reduce soil brightness.

Yield of good steel at metallurgical plants of the world depending on large-dispersed precipitation

The moisture in the environment influences the formation of defects in steel and the yield of suitable metal products. However, no numerical dependence of the yield at a metallurgical enterprise on climatic and weather conditions, in particular, on coarse precipitation, has yet been proposed. A significant difference in the yield of steel grades suitable for various groups is shown on days without atmospheric precipitation, with precipitation and during the day after rain. For low-alloy steels, the yield of a suitable continuously cast billet during periods with precipitation is 11.3% lower than on days without precipitation; for high-alloy grades, this difference reaches 16.5%. Based on the annual reports of metallurgical companies, a database has been formed on the average annual output of suitable metal products at various plants around the world. The quantitative dependence of the yield at the enterprise on the number of days in a year with coarse precipitation (rain, snow) and the uneven distribution of these precipitation over the year has been obtained. Coarse precipitation significantly affects the level of yield of suitable metal products at a metallurgical enterprise. With an increase in the number of days with precipitation per year by one day, the level of waste and sorting at the enterprise increases by 0.087%. The increase in the unevenness of atmospheric precipitation per unit leads to an increase in waste and sorting by 0.822%. If the climatic factor is taken into account when forming a strategy for the development of metallurgy in the country, then it is possible to achieve a greater effect for the development of the economy in comparison with other states that do not take this factor into account.

Downscaling of Open Coarse Precipitation Data Using a Machine Learning Algorithm

In this chapter, the authors propose a novel statistical model with a residual correction of downscaling coarse precipitation TRMM 3B43 product. The presented study was carried out over Morocco, and the objective is to improve statistical downscaling for TRMM 3B43 products using a machine learning algorithm. Indeed, the statistical model is based on the Transformed Soil Adjusted Vegetation Index (TSAVI), elevation, and distance from the sea. TSAVI was retrieved using the quantile regression method. Stepwise regression was implemented with the minimization of the Akaike information criterion and Mallows' Cp indicator. The model validation is performed using ten in-situ measurements from rain gauge stations (the most available data). The result shows that the model presents the best fit of the TRMM 3B43 product and good accuracy on estimating precipitation at 1km according to 𝑅2, RMSE, bias, and MAE. In addition, TSAVI improved the model accuracy in the humid bioclimatic stage and in the Saharan region to some extent due to its capacity to reduce soil brightness.

Effects of Annealing on Microstructure and Mechanical Properties of Metastable Powder Metallurgy CoCrFeNiMo0.2 High Entropy Alloy

A CoCrFeNiMo0.2 high entropy alloy (HEA) was prepared through powder metallurgy (P/M) process. The effects of annealing on microstructural evolution and mechanical properties of P/M HEAs were investigated. The results show that the P/M HEA exhibit a metastable FCC single-phase structure. Subsequently, annealing causes precipitation in the grains and at the grain boundaries simultaneously. As the temperature increases, the size of the precipitates grows, while the content of the precipitates tends to increase gradually first, and then decrease as the annealing temperature goes up to 1000 °C. As the annealing time is prolonged, the size and content of the precipitates gradually increases, eventually reaching a saturated stable value. The mechanical properties of the annealed alloys have a significant correspondence with the precipitation behavior. The larger the volume fraction and the size of the precipitates, the higher the strength and the lower the plasticity of the HEA. The CoCrFeNiMo0.2 high entropy alloy, which annealed at 800 °C for 72 h, exhibited the most excellent mechanical properties with the ultimate tensile strength of about 850 MPa and an elongation of about 30%. Nearly all of the annealed HEAs exhibit good strength–ductility combinations due to the significant precipitation enhancement and nanotwinning. The separation of the coarse precipitation phase and the matrix during the deformation process is the main reason for the formation of micropores. Formation of large volume fraction of micropores results in a decrease in the plasticity of the alloy.

Effect of Precipitation State on Strength and Toughness of Precipitation Hardened Hot Rolled Steel Sheet

In this work, to clarify the effect of carbide precipitation state on strength and toughness, Ti,V alloyed precipitation hardened ferrite single phase steel sheets with different carbide size were investigated. In order to change the precipitated particle size, cooling conditions after hot rolling were changed. Under condition A, steel sheets were cooled to 873K by water spray (for fine precipitation). Under condition B, steel sheets were air-cooled from 1053K for 20sec, then cooled by water spray to 873K (for coarse precipitation). The experimental results were following. The balance of tensile strength and Charpy absorbed energy was better in condition B. (Ti,V)C were observed in both conditions, but the size of (Ti,V)C were larger in condition B. From the above, it was suggested that as the carbide size become larger, the decrease in toughness per strengthening amount becomes smaller.

Downscaling of Open Coarse Precipitation Data through Spatial and Statistical Analysis, Integrating NDVI, NDWI, Elevation, and Distance from Sea

This study aims to improve the statistical spatial downscaling of coarse precipitation (TRMM 3B43 product) and also to explore its limitations in the Mediterranean area. It was carried out in Morocco and was based on an open dataset including four predictors (NDVI, NDWI, DEM, and distance from sea) that explain TRMM 3B43 product. For this purpose, four groups of models were established based on different combinations of the four predictors, in order to compare from one side NDVI and NDWI based models and the other side stepwise with multiple regression. The models that have given rise to the best approximations and best fits were used to downscale TRMM 3B43 product. The resulting downscaled and calibrated precipitations were validated by independent RGS. Aside from that, the limitations of the proposed approach were assessed in five bioclimatic stages. Furthermore, the influence of the sea was analyzed in five classes of distance. The findings showed that the models built using NDVI and NDWI have a high correlation and therefore can be used to downscale precipitation. The integration of elevation and distance improved the correlation models. According to R2, RMSE, bias, and MAE, the study revealed that there is a great agreement between downscaled precipitations and RGS measurements. In addition, the analysis showed that the contribution of the variable (distance from sea) is evident around the coastal area and decreases progressively. Likewise, the study demonstrated that the approach performs well in humid and arid bioclimatic stages compared to others.