Rainfall forecasting using neural network: A survey

Darji, Mohini; Dabhi, Vipul K.; Prajapati, Harshadkumar B.

doi:10.1109/icacea.2015.7164782

Cited by 97 publications

(47 citation statements)

References 16 publications

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“…ANN imitates modelling of the human brain's neural network via uses of nonlinear patterns [33] for complex system modelling. Although ANN is not totally comparable to natural neuronal systems, ANN makes important contributions to some applications such as pattern separation, linear, or non-linear mapping [34].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data

Salimi

Samakosh

Sharifi

et al. 2019

Water

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Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks (ANN), and to impose a residual correction method for five separate daily heavy precipitation events localized over northeast Austria. For the ANN model, a precipitation variable was the chosen output and the inputs were temperature, MODIS cloud optical, and microphysical variables. The particle swarm optimization (PSO), imperialist competitive algorithm,(ICA), and genetic algorithm (GA) were utilized to improve the performance of ANN. Moreover, to examine the efficiency of the networks, the downscaled product was evaluated using 54 rain gauges at a daily timescale. In addition, sensitivity analysis was conducted to obtain the most and least influential input parameters. Among the optimized algorithms for network training used in this study, the performance of the ICA slightly outperformed other algorithms. The best-recorded performance for ICA was on 17 April 2015 with root mean square error (RMSE) = 5.26 mm, mean absolute error (MAE) = 6.06 mm, R 2 = 0.67, bias = 0.07 mm. The results showed that the prediction of precipitation was more sensitive to cloud optical thickness (COT). Moreover, the accuracy of the final downscaled satellite precipitation was improved significantly through residual correction algorithms.As precipitation varies greatly in space and time, gridded precipitation data in high spatiotemporal resolution is a considerable requirement as input for hydrometeorological and water resources management applications. This type of data is highly important for timely action (e.g., the initiation of landslide and mudslide movement) and decision making, such as evacuating an area with high potential for flooding, or to secure food and water supply [1]. However, high spatiotemporal resolution datasets are usually available only on a country level or cover a specific geographical region. These type of datasets might be obtained by interpolation of in situ observations and reanalysis products, or derived through remote sensing observations [2].The monitoring of precipitation from space opens a new era of precipitation observation. Hence, precipitation and hydrologic cycle studies are a hot topic in atmospheric science. High-resolution satellite precipitation estimates (SPEs) provide an effective global source of uninterrupted data for various water-related applications, especially over regions where ground-based observations are often lacking or sparse. These factors are underlying reasons why the increasing use of satellite data, which provide much better geographical coverage despite the potential limited accuracy, depends on precipitation products. The limited accuracy could be caused by various reasons e.g., algorithm, orographic effects, cloudy and/or snowy conditions, the relatively s...

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data

Salimi

Samakosh

Sharifi

et al. 2019

Water

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“…Statistical techniques were used for performance evaluation and according to results WNN performed better than ANN. In [12], researchers provided a detailed survey and performed a comparative analysis of various neural networks on rainfall forecasting. According to survey RNN, FFNN, and TDNN are suitable for rainfall prediction as compared to other statistical and numerical forecasting methods.…”

Section: Related Workmentioning

confidence: 99%

Rainfall Prediction using Data Mining Techniques: A Systematic Literature Review

Aftab¹,

Ahmad²,

Hameed³

et al. 2018

ijacsa

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Abstract-Rainfall prediction is one of the challenging tasks in weather forecasting. Accurate and timely rainfall prediction can be very helpful to take effective security measures in advance regarding: ongoing construction projects, transportation activities, agricultural tasks, flight operations and flood situation, etc. Data mining techniques can effectively predict the rainfall by extracting the hidden patterns among available features of past weather data. This research contributes by providing a critical analysis and review of latest data mining techniques, used for rainfall prediction. Published papers from year 2013 to 2017 from renowned online search libraries are considered for this research. This review will serve the researchers to analyze the latest work on rainfall prediction with the focus on data mining techniques and also will provide a baseline for future directions and comparisons.

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“…A wide range of ANN architectures have been applied in forecasting rainfall [11,13,[25][26][27]. The selection of an ANN architecture is commonly achieved through a trial and error process [11][12][13].…”

Section: Progressing To Automation and Single-month Optimizationmentioning

confidence: 99%

“…This is significant improvement on that currently achievable using general circulation models. ANNs have been applied to rainfall forecasting in other parts of the world [25,26,43], with Table 6 showing a survey of 36 articles published between 2014 and 2017 where ANNs have been used to forecast monthly or seasonal rainfall. The majority of these investigations were from Asia, particularly India and China.…”

Section: Other Ann Studiesmentioning

confidence: 99%

Forecasting of Medium-term Rainfall Using Artificial Neural Networks: Case Studies from Eastern Australia

Abbot¹,

Marohasy²

2018

Engineering and Mathematical Topics in Rainfall

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The advent of machine learning, of which artificial neural networks (ANN) are a component, has provided an opportunity for improved rainfall forecasts, which is of value for water infrastructure management, agriculture, mining and other industries. In this chapter, ANNs are shown to provide more skillful monthly rainfall forecasts for locations in southeastern Queensland, Australia, for lead-times of 3-12 months. The skill of the forecasts from the ANNs is highest when the models are individually optimized for each month, and when longer-duration series are used as input. The ANN technique has application where there is temperature and rainfall data extending back at least 50 years. Such datasets exist for much of Europe and North America, though a review of the available literature indicates most research into the application of ANN has focused on China, India and Australia.

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Rainfall forecasting using neural network: A survey

Cited by 97 publications

References 16 publications

Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data

Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data

Rainfall Prediction using Data Mining Techniques: A Systematic Literature Review

Forecasting of Medium-term Rainfall Using Artificial Neural Networks: Case Studies from Eastern Australia

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