A Survey on Rainfall Prediction using Artificial Neural Network

Nayak, Deepak Ranjan; Mahapatra, Amitav; Mishra, Pranati

doi:10.5120/12580-9217

Cited by 65 publications

(19 citation statements)

References 36 publications

(22 reference statements)

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“…Beberapa penelitian prediksi curah hujan JST telah dilakukan di berbagai wilayah di dunia (RanjanNayak et al, 2013;Venkata Ramana et al, 2013). Penelitian di Indonesia menunjukkan bahwa hasil prediksi curah hujan metode JST mampu mengikuti nilai aktual variabilitas curah hujan wilayah Indramayu (Apriyana, 2015).…”

Section: Pendahuluanunclassified

Rainfall Prediction Using Artificial Neural Network

Salmayenti¹,

Hidayat²,

Pramudia³

2017

J.Agromet

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Artificial neural network (ANN) is widely used for modelling in environmental science including climate, especially in rainfall prediction. Current knowledge has used several predictors consisting of historical rainfall data and El Niño Southern Oscillation (ENSO). However, rainfall variability of Indonesian is not only driven by ENSO, but Indian Ocean Dipole (IOD) could also influence variability of rainfall. Here, we proposed to use Dipole Mode Index (DMI) as index of IOD as complementary for ENSO. We found that rainfall variability in region with a monsoonal pattern has a strong correlation with ENSO and DMI. This strong correlation occurred during June-November, but a weak correlation was found for region with rainfall’s equatorial pattern. Based on statistical criteria, our model has R<sup>2</sup> 0.59 to 0.82, and RMSE 0.04-0.09 for monsoonal region. This finding revealed that our model is suitable to be applied in monsoonal region. In addition, ANN based model likely shows a low accuracy when it uses for long period prediction.

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Section: Pendahuluanunclassified

Rainfall Prediction Using Artificial Neural Network

Salmayenti¹,

Hidayat²,

Pramudia³

2017

J.Agromet

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“…The northern branch of the 200 hPa jetstream strengthens, and the major trajectories of the western disturbances shift northward. The Indus-Ganges Lowlands and especially the southern Himalayan slopes receive high solar radiation, which results in occasional convective precipitation events (Romatschke et al, 2010).…”

Section: Large-scale Circulation Modes Pluviometric Regimes and The mentioning

confidence: 99%

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

Gerlitz¹,

Conrad²,

Böhner³

2014

Preprint

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Abstract. The heterogeneity of precipitation rates in high mountain regions is not sufficiently captured by state of 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, 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 non-linear, 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 km2. The model was found to sufficiently capture the temporal and spatial variations of precipitation rates in the highly structured target area and allows 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.

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“…However, such models involve computationally infeasible mathematical calculations in order to predict the rainfall quantity. Therefore, even after being accurate in several cases, such models are not suitable for rainfall predictions as it does not meet the current trend of predicting rainfall for small geographic regions [34]. A second type of rainfall prediction model has been proposed which utilizes machine learning models which aim at approximating the complex relation between the rainfall quantity and measurable weather parameters [15].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Neural Network Based Rainfall Prediction Supported by Flower Pollination Algorithm

Chatterjee¹,

Datta²,

Dey³

2018

NNW

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The present work proposes a hybrid neural network based model for rainfall prediction in the Southern part of the state West Bengal of India. The hybrid model is a multistep method. Initially, the data is clustered into a reasonable number of clusters by applying fuzzy c-means algorithm, then for every cluster a separate Neural Network (NN) is trained with the data points of that cluster using well known metaheuristic Flower Pollination Algorithm (FPA). In addition, as a preprocessing phase a feature selection phase is included. Greedy forward selection algorithm is employed to find the most suitable set of features for predicting rainfall. To establish the ingenuity of the proposed hybrid prediction model (Hybrid Neural Network or HNN) has been compared with two well-known models namely multilayer perceptron feed-forward network (MLP-FFN) using different performance metrics. The data set for simulating the model is collected from Dumdum meteorological station (West Bengal, India), recorded with in the 1989 to 1995. The simulation results have revealed that the proposed model is significantly better than traditional methods in predicting rainfall.

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A Survey on Rainfall Prediction using Artificial Neural Network

Cited by 65 publications

References 36 publications

Rainfall Prediction Using Artificial Neural Network

Rainfall Prediction Using Artificial Neural Network

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

Hybrid Neural Network Based Rainfall Prediction Supported by Flower Pollination Algorithm

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