Prediction of Indian summer monsoon rainfall using Niño indices: A neural network approach

Shukla, Rahul; Tripathi, K. C.; Pandey, Avinash C.; Das, I.

doi:10.1016/j.atmosres.2011.06.013

Cited by 84 publications

(52 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…During the last decade, complex machine learning algorithms such as artificial neural networks have become more prominent in the field of geoscientific research and have been utilized, for example, for hydrological simulations (Dawson and Wilby, 2001;Jain and Kumar, 2007), snow cover prediction (Sauter and Venema, 2011) and habitat modeling (Özesmi and Özesmi, 1999), as well as for statistical downscaling and climate modeling applications. For the analysis and prediction of the variability and change of monsoonal precipitation rates over India, various recent studies have applied ANNs, attaining reliable results (Chattopadhyay, 2007;Shukla et al, 2011;Singh and Borah, 2013). In the field of precipitation downscaling ANNs were utilized (amongst others) by Coulibaly et al (2005), Dibike and Coulibaly (2006), Mekanik et al (2013) and Tomassetti et al (2009).…”

Section: Implementation and Evaluation Of An Ann Modelmentioning

confidence: 99%

Large-scale atmospheric forcing and topographic modification of precipitation rates over High Asia – a neural-network-based approach

2015

View full text Add to dashboard Cite

Abstract. The heterogeneity of precipitation rates in high-mountain regions is not sufficiently captured by stateof-the-art climate reanalysis products due to their limited spatial resolution. Thus there exists a large gap between the available data sets and the demands of climate impact studies. The presented approach aims to generate spatially high resolution precipitation fields for a target area in central Asia, covering the Tibetan Plateau and the adjacent mountain ranges and lowlands. Based on the assumption that observed local-scale precipitation amounts are triggered by varying large-scale atmospheric situations and modified by local-scale topographic characteristics, the statistical downscaling approach estimates local-scale precipitation rates as a function of large-scale atmospheric conditions, derived from the ERA-Interim reanalysis and high-resolution terrain parameters. Since the relationships of the predictor variables with local-scale observations are rather unknown and highly nonlinear, an artificial neural network (ANN) was utilized for the development of adequate transfer functions. Different ANN architectures were evaluated with regard to their predictive performance.The final downscaling model was used for the cellwise estimation of monthly precipitation sums, the number of rainy days and the maximum daily precipitation amount with a spatial resolution of 1 km 2 . The model was found to sufficiently capture the temporal and spatial variations in precipitation rates in the highly structured target area and allows for a detailed analysis of the precipitation distribution. A concluding sensitivity analysis of the ANN model reveals the effect of the atmospheric and topographic predictor variables on the precipitation estimations in the climatically diverse subregions.

show abstract

Section: Implementation and Evaluation Of An Ann Modelmentioning

confidence: 99%

Large-scale atmospheric forcing and topographic modification of precipitation rates over High Asia – a neural-network-based approach

2015

View full text Add to dashboard Cite

show abstract

“…ANN is a powerful and versatile data-driven algorithm for capturing and representing complex input and output relationships Marohasy, 2012, 2014;Govindaraju, 2000;Şahin et al, 2013). This model has been tested for rainfall and temperature predictions in many parts of the world including Australia Marohasy, 2012, 2014;Masinde, 2013;Nastos et al, 2014;Ortiz-García et al, 2014, 2012Shukla et al, 2011). However a major challenge encountered by the ANN is the requirement of iterative tuning of model parameters, slow response of the gradient based learning algorithm used and the relatively low prediction accuracy compared to the more advanced ML algorithms (e.g.…”

Section: Introductionmentioning

confidence: 99%

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Deo

Şahin

2015

Atmospheric Research

259

102

View full text Add to dashboard Cite

The prediction of future drought is an effective mitigation tool for assessing adverse consequences of drought events on vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using machine learning algorithms are promising tenets for these purposes as they require less developmental time, minimal inputs and are relatively less complex than the dynamic or physical model. This paper authenticates a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957-2008 and the monthly EDI predicted over the period 2009-2011. The predictive variables for the ELM model were the rainfall and mean, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and the Indian Ocean Dipole moment. To demonstrate the effectiveness of the proposed datadriven model a performance comparison in terms of the prediction capabilities and learning speeds was conducted between the proposed ELM algorithm and the conventional artificial neural network (ANN) algorithm trained with Levenberg-Marquardt back propagation. The prediction metrics certified an excellent performance of the ELM over the ANN model for the overall test sites, thus yielding Mean Absolute Errors, Root-Mean Square Errors, Coefficients of Determination and Willmott's Indices of Agreement of 0.277, 0.008, 0.892 and 0.93 (for ELM) and 0.602, 0.172, 0.578 and 0.92 (for ANN) models. Moreover, the ELM model was executed with learning speed 32 times faster and training speed 6.1 times faster than the ANN model. An improvement in the prediction capability of the drought duration and severity by the ELM model was achieved. Based on these results we aver that out of the two machine learning algorithms tested, the ELM was the more expeditious tool for prediction of drought and its related properties.

show abstract

“…This is a statistical approach that enables non-linear relationships to be considered as well as facilitating the input of multiple variables. For example, Shulka et al [26] found with a NN model, inputting Nino indices produced superior forecasts compared to linear models for forecasting Indian monsoon rainfall. However, the NN approach has rarely been used to forecast rainfall in Australia.…”

Section: Introductionmentioning

confidence: 99%

The application of artificial intelligence for monthly rainfall forecasting in the Brisbane Catchment, Queensland, Australia

Abbot¹,

Marohasy²

2013

WIT Transactions on Ecology and the Environment

View full text Add to dashboard Cite

Concurrent relationships between climate indices and Australian spring rainfall have been used extensively to explain weather events. In order for climate indices to be useful for rainfall forecasting there must be relationships between their lagged values and rainfall. The methods currently used by the Australian Bureau of Meteorology for seasonal weather forecasting have limited capacity to exploit the often non-linear relationships that potentially exist between the lagged values for these indices and rainfall. This paper reports on the application of a method of forecasting based on the use of neural networks, a form of artificial intelligence. Neural networks facilitate the input of multiple variables simultaneously. The variables most useful for determining rainfall are elucidated by application of algorithms during the optimisation process.Brisbane, the capital of Queensland, Australia, has flooded periodically and catastrophically. The neural network described in this study was used to forecast rainfall for three locations in the Brisbane River catchment one to three months in advance, including the 2011 flood event. Results are compared on the basis of root mean square error with output from the Australian Bureau of Meteorology's general circulation model, POAMA. The Neural Network model shows considerable more skill. The Neural Network incorporates lagged values for key climatic indices and also rainfall and atmospheric temperatures.

show abstract

Prediction of Indian summer monsoon rainfall using Niño indices: A neural network approach

Cited by 84 publications

References 30 publications

Large-scale atmospheric forcing and topographic modification of precipitation rates over High Asia – a neural-network-based approach

Large-scale atmospheric forcing and topographic modification of precipitation rates over High Asia – a neural-network-based approach

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

The application of artificial intelligence for monthly rainfall forecasting in the Brisbane Catchment, Queensland, Australia

Contact Info

Product

Resources

About