Clay deposits of Bageshpur area, Hassan district, Karnataka.

These clays occur as alteration products of basic gneisses and amphibolites. DTA curves and EM demonstrate the presence of illite, kaolinite and metahalloysite. These clays, which have long been worked commercially, were produced by kaolinization of feldspars. Chemical analyses are given for two gneisses and six clays. -R.A.H

Erythrocyte pyrophosphatase in health and disease. I. Properties of the enzyme

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ANFIS-based soft computing models for forecasting effective drought index over an arid region of India

Drought is a natural hazard that is characterized by a low amount of precipitation in a region. In order to evaluate the drought-related issues that cause chaos for human well-being, drought indices have become increasingly important. In this study, the monthly precipitation data from 1964 to 2013 (about 50 years) of the Jodhpur district in the drought-prone Rajasthan state of India was used to derive the effective drought index (EDI). The machine learning models hybridized with evolutionary optimizers such as the genetic algorithm adaptive neuro-fuzzy inference system (GA-ANFIS) and particle swarm optimization ANFIS (PSO-ANFIS) were used in addition to the generalized regression neural network (GRNN) to predict the EDI index. Using the partial autocorrelation function (PACF), models for forecasting the monthly EDI were constructed with 2-, 3- and 5-input combinations to evaluate their outcomes based on various performance indices. The results of the different combination models were compared. With reference to 2-input and 3-input combination models, both GA-ANFIS and PSO-ANFIS show better performance results with R2 = 0.75, while among the models with 5-input combination, GA-ANFIS depicts better performance results compared to other models with R2 = 0.78. The results are presented suitably with the aid of scatter plots, Taylor's diagram and violin plots. Overall, the GA-ANFIS and PSO-ANFIS models outperformed the GRNN model. HIGHLIGHTS Effective drought index (EDI) was predicted using soft computing techniques.; Hybrid machine learning algorithms were used.; GA-ANFIS, PSO-ANFIS and GRNN paradigms were used.; The EDI of an arid region in India was used for prediction.; Precipitation data was used for computing the EDI of drought-prone areas.