Multi layer perceptron model in pattern recognition of surface parameters during pre-monsoon thunderstorm

The concept of Multi Layer Perceptron and Fuzzy logic is introduced in this paper to recognize the pattern of surface parameters pertaining to forecast the occurrence of pre-monsoon thunderstorms over Kolkata (22 ° 32¢ , 88 ° 20¢ ). The results reveal that surface temperature fluctuates significantly from Fuzzy Multi Layer Perceptron (FMLP) model values on thunderstorm days whereas on non-thunderstorm days FMLP model fits well with the surface temperature. The results further indicate that no definite pattern could be made available with surface dew point temperature and surface pressure that can help in forecasting the occurrence of these storms.

Relationship between surface fields over Indian ocean and monsoon rainfall over homogeneous zones of India

The monthly mean atmospheric fields and surface parameters of NCEP/NCAR reanalysis for the period 1948-1998 have been studied to examine the characteristics of monsoon circulation features, sea surface temperature (SST), sea level pressure, surface wind stress and latent heat flux over the Indian Ocean and nearby seas during deficient, normal and excess rain years. The entire period of study has been classified into deficient, normal and excess rain years for all India as well as for each of the five homogeneous zones separately based on the observed seasonal mean rainfall. On the basis of the mean characteristics of the surface fields, the oceanic region covering the Indian Ocean and adjacent seas has been divided into four regional sectors. Using various statistical means the relation between the surface fields over the four regional sectors and the monsoon rainfall over five homogeneous zones of Indian landmass has been examined. Attempt have been made to identify some surface parameters which can be used as predictors for seasonal mean monsoon rainfall over the entire India and also over some homogeneous zones.

Estimation of Soil Salinization by Machine Learning Algorithms in Different Arid Regions of Northwest China

Hyperspectral data has attracted considerable attention in recent years due to its high accuracy in monitoring soil salinization. At present, most existing research focuses on the saline soil in a single area without comparative analysis between regions. The regional differences in the hyperspectral characteristics of saline soil are still unclear. Thus, we chose Golmud in the cold–dry Qaidam Basin (QB–G) and Gaotai–Minghua in the relatively warm–dry Hexi Corridor (HC–GM) as the study areas, and used the deep extreme learning machine (DELM) and sine cosine algorithm–Elman (SCA–Elman) to predict soil salinity, and then selected the most suitable algorithm in these two regions. A total of 79 (QB–G) and 86 (HC–GM) soil samples were collected and tested to obtain their electrical conductivity (EC) and corresponding hyperspectral reflectance (R). We utilized the land surface parameters that affect the soil based on Landsat 8 and digital elevation model (DEM) data, selected the variables using the light gradient boosting machine (LightGBM), and built SCA–Elman and DELM from the hyperspectral reflectance data combined with land surface parameters. The results revealed the following: (1) The soil hyperspectral reflectance in QB–G was higher than that in HC–GM. The soils of QB–G are mainly the chloride type and those of HC–GM mainly belong to the sulfate type, having lower reflectance. (2) The accuracies of some of the SCA–Elman and DELM models in QB–G (the highest MAEv, RMSEv, and were 0.09, 0.12 and 0.75, respectively) were higher than those in HC–GM (the highest MAEv, RMSEv, and were 0.10, 0.14 and 0.73, respectively), which has flatter terrain and less obvious surface changes. The surface parameters in QB–G had higher correlation coefficients with EC due to the regular altitude change and cold–dry climate. (3) Most of the SCA–Elman results (the mean in HC-GM and QB-G were 0.62 and 0.60, respectively) in all areas performed better than the DELM results (the mean in HC–GM and QB–G were 0.51 and 0.49, respectively). Therefore, SCA–Elman was more suitable for the soil salinity prediction in HC–GM and QB–G. This can provide a reference for soil salinization monitoring and model selection in the future.

Estimation of surface parameters from a 30 metre micro-meteorological tower over a deep moist convective region

The estimation of u*, 0*, q*. and Obukov-length In the surface layer from micro-meteorological tower data still poses an important challange. In the present study a procedure for the parametric estimation has been developed which is consistent both with the similarity relation and the profile relation. The study has been done using both fast response and slow response tower data. Since similarity relations involve a particular level z. so inspite of starting from a layer, the parameters should be attributed to a relations involve a particular level only, It has been suggested that the convenient level is geometric mean height of the layer. The ratio of eddy diffusivities (KhKm.) has been estimated both for stable and unstable situation and this ratio is presented by a single expression which incidentally yields a new value of a constant involved.

Application of Artificial Intelligence and Machine Learning to Detect Drilling Anomalies Leading to Stuck Pipe Incidents

This project used predictive analytics and machine learning-based modeling to detect drilling anomalies, namely stuck pipe events. Analysis focused on historical drilling data and real-time operational data to address the limitations of physics-based modeling. This project was designed to enable drilling crews to minimize downtime and non-productive time through real-time anomaly management. The solution used data science techniques to overcome data consistency/quality issues and flag drilling anomalies leading to a stuck pipe event. Predictive machine learning models were deployed across seven wells in different fields. The models analyzed both historical and real-time data across various data channels to identify anomalies (difficulties that impact non-productive time). The modeling approach mimicked the behavior of drillers using surface parameters. Small deviations from normal behavior were identified based on combinations of surface parameters, and automated machine learning was used to accelerate and optimize the modeling process. The output was a risk score that flags deviations in rig surface parameters. During the development phase, multiple data science approaches were attempted to monitor the overall health of the drilling process. They analyzed both historical and real-time data from torque, hole depth and deviation, standpipe pressure, and various other data channels. The models detected drilling anomalies with a harmonic model accuracy of 80% and produced valid alerts on 96% of stuck pipe and tight hole events. The average forewarning was two hours. This allowed personnel ample time to make corrections before stuck pipe events could occur. This also enabled the drilling operator to save the company upwards of millions of dollars in drilling costs and downtime. This project introduced novel data aggregation and deep learning-based normal behavior modeling methods. It demonstrates the benefits of adopting predictive analytics and machine learning in drilling operations. The approach enabled operators to mitigate data issues and demonstrate real-time, high-frequency and high-accuracy predictions. As a result, the operator was able to significantly reduce non-productive time.

Rehardening and the Protective Effect of Gamma-Polyglutamic Acid/Nano-Hydroxyapatite Paste on Surface-Etched Enamel

Many revolutionary approaches are on the way pertaining to the high occurrence of tooth decay, which is an enduring challenge in the field of preventive dentistry. However, an ideal dental care material has yet to be fully developed. With this aim, this research reports a dramatic enhancement in the rehardening potential of surface-etched enamels through a plausible synergistic effect of the novel combination of γ-polyglutamic acid (γ-PGA) and nano-hydroxyapatite (nano-HAp) paste, within the limitations of the study. The percentage of recovery of the surface microhardness (SMHR%) and the surface parameters for 9 wt% γ-PGA/nano-HAp paste on acid-etched enamel were investigated with a Vickers microhardness tester and an atomic force microscope, respectively. This in vitro study demonstrates that γ-PGA/nano-HAp treatment could increase the SMHR% of etched enamel to 39.59 ± 6.69% in 30 min. To test the hypothesis of the rehardening mechanism and the preventive effect of the γ-PGA/nano-HAp paste, the surface parameters of mean peak spacing (Rsm) and mean arithmetic surface roughness (Ra) were both measured and compared to the specimens subjected to demineralization and/or remineralization. After the treatment of γ-PGA/nano-HAp on the etched surface, the reduction in Rsm from 999 ± 120 nm to 700 ± 80 nm suggests the possible mechanism of void-filling within a short treatment time of 10 min. Furthermore, ΔRa-I, the roughness change due to etching before remineralization, was 23.15 ± 3.23 nm, while ΔRa-II, the roughness change after remineralization, was 11.99 ± 3.90 nm. This statistically significant reduction in roughness change (p < 0.05) implies a protective effect against the demineralization process. The as-developed novel γ-PGA/nano-HAp paste possesses a high efficacy towards tooth microhardness rehardening, and a protective effect against acid etching.

Distribution of mean monthly surface/upper air parameters during July-August months of 1982-83 and 1987- 88

Lkkj & Hkkjrh; xzh"edkyhu ekulwu dks leqnz vkSj /kjkry dh feyh&tqyh ok;qeaMyh; ifj?kVuk ekurs gq, bldk v/;;u HkweaMyh; izÑfr ds ifjn`’; esa fd;k x;k gSA bl v/;;u esa nks fo"ke ifjfLFkfr;ksa esa ekulwu ds O;ogkj dks le>us ds fy, Øe’k% de vkSj vf/kd nksuksa rjg dh o"kkZ okys nks o"kksaZ ¼1982] 1987½] nks o"kksaZ ¼1983] 1988½ esa xzh"edkyhu ekulwu ds nks izeq[k eghuksa tqykbZ vkSj vxLr ds nkSjku 'kwU; va’k m- ls 40 va’k m-@40 va’k iw- ls 100 va’k iw- ds {ks= esa ek/; ekfld /kjkryh; izkpyksa ds forj.k dks fy;k x;k gSA blds vfrfjDr xzh"edkyhu ekulwu ij {kksHkeaMyh; if’peh gokvksa ds izHkko vkSj if’peh fo{kksHkksa dh xfrfof/k dk ewY;kadu djus ds fy, fuEu {kksHkeaMy dh dfVca/kh; iouksa ds forj.k vFkkZr~] tqykbZ] vxLr ds eghuksa esa 850 gSDVkikLdy vkSj 700 gSDVkikLdy ds Lrjksa tuojh] ebZ] tqykbZ vkSj vxLr ds eghuksa ds fy, 500 gSDVkikLdy ij HkwfLFkfrt Å¡pkb;ksa dk v/;;u fd;k x;k gSA bl v/;;u ls izkIr gq, ifj.kkeksa ij fopkj&foe’kZ fd;k x;k gSA Indian summer monsoon is considered as an ocean-land-atmosphere coupled phenomenon and also of global nature. In present study, the distribution of mean monthly surface parameters within 0° N – 40° N / 40° E – 100° E region during the two representative months of summer monsoon, July and August, in both deficient years (1982, 1987) and excess years (1983, 1988) was taken up to understand the behaviour of monsoon during two contrasting situations. Apart from this, the distribution of lower tropospheric zonal winds viz., 850 hPa and 700 hPa levels during July, August months, 500 hPa geopotential heights for the months of January, May, July and August months studied to assess the influence of tropospheric westerlies and activity of Western Disturbances on the summer monsoon. The results discussed.