D. A. Sachindra

TL;DR: In this paper, statistical models were developed for downscaling reanalysis data to monthly precipitation at 48 observation stations scattered across the Australian State of Victoria belonging to wet, intermediate and dry climate regimes.

TL;DR: In this article, the performance of 36 coupled model intercomparison project 5 (CMIP5) GCMs was evaluated in relation to their skills in simulating mean annual, monsoon, winter, pre-monsoon, and postmonsoon precipitation and maximum and minimum temperature over Pakistan using state-of-the-art spatial metrics, SPAtial EFficiency, fractions skill score, Goodman-Kruskal's lambda, Cramer's V, Mapcurves, and Kling-Gupta efficiency, for the period 1961-2005.

TL;DR: In this article, the authors employed least square support vector machine regression (LS-SVM-R) and multi-linear regression (MLR) for statistically downscaling monthly general circulation model (GCM) outputs directly to monthly catchment streamflows.

TL;DR: In this article, the authors investigated the potential of developing drought prediction models over Pakistan using three state-of-the-art Machine Learning (ML) techniques; Support Vector Machine (SVM), Artificial Neural Network (ANN) and k-Nearest Neighbour (KNN).

TL;DR: In this article, two models were developed with National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis and HadCM3 outputs, for statistically downscaling these outputs to monthly precipitation at a site in north-western Victoria, Australia.