Analysis of Precipitation Variability using Memory Based Artificial Neural Networks

This article analyzes the variability in precipitation of the Barak river basin using memory-based ANN models called Gamma Memory Neural Network(GMNN) and genetically optimized GMNN called GMNN-GA for precipitation downscaling precipitation. GMNN having adaptive memory depth is capable techniques in modeling time varying inputs with unknown input characteristics, while an integration of the model with GA can further improve its performances. NCEP reanalysis and HadCM3A2 (a) scenario data are used for downscaling and forecasting precipitation series for Barak river basin. Model performances are analyzed by using statistical criteria, RMSE and mean error and are compared with the standard SDSM model. Results obtained by using 24 years of daily data sets show that GMNN-GA is efficient in downscaling daily precipitation series with maximum daily annual mean error of 6.78%. The outcomes of the study demonstrate that execution of the GMNN-GA model is superior to the GMNN and similar with that of the standard SDSM.

A new species of the genus Garra Hamilton (Teleostei: Cyprinidae) from Northeast India

A new species of Garra with a rostral proboscis is described from the Barak River drainage in Manipur, India. Garra paratrilobata, new species, is distinguished from its congeners in having the combination of the following characters: a prominent trilobed proboscis, the median lobe with 5–7 uni- to tricuspid tubercles on its anterior margin and 4–6 minute tubercles on its anteroventral margin; lateral lobe of the proboscis with 3–4 minute tubercles; lateral surface of the snout lobular, with 6–9 tubercles; 33–34 lateral-line scales; and the rostral surface concave, creased and depressed.