An Artificial Intelligence Based Rainfall Prediction Using LSTM and Neural Network

Salehin, Imrus; Talha, Iftakhar Mohammad; Hasan, Md. Mehedi; Dip, Sadia Tamim; Saifuzzaman, Mohd.; Moon, Nazmun Nessa

doi:10.1109/wiecon-ece52138.2020.9398022

Cited by 41 publications

(14 citation statements)

References 11 publications

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“…Finally, the third model uses the concept of neural networks [16][17] [18] . These extracting patterns in input feature space, especially where the input data spans over long sequences.…”

Section: Method-3mentioning

confidence: 99%

Time Series Analysis and Forecasting of Precipitation (Rainfall) Data

Talhar¹,

Ramgirkar²,

Jena³

2022

ECS Trans.

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According to Mattew Rodell, chief of the Hydrological Sciences Laboratory at NASA's Goddard Space Flight Center, "Groundwater in northern India is being depleted at a rate of 19.2 gigatons per year." Furthermore, a report by NITI (National Institution for Transforming India) Aayog claimed that 40 percent of India's population will have no access to drinking water by 2030. It is an undisputed fact that the water crisis is a grave issue that we face today and facts like these, forces us to experiment with new plans and measures to carefully sustain our resources. Predicting rainfall is a significant step to mitigate plans related to water crisis and helps us to effectively use water resources, manage crop productivity and plan water structures. This work also aims to compare among efficient models (such as ML, and DL) to determine the best one for predicting the rainfall with higher accuracy. The raison d’etre of this paper is to study and develop efficient models and compare them to determine an approach that can predict rainfall with higher accuracy.

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“…Finally, the third model uses the concept of neural networks [16][17] [18] . These extracting patterns in input feature space, especially where the input data spans over long sequences.…”

Section: Method-3mentioning

confidence: 99%

Time Series Analysis and Forecasting of Precipitation (Rainfall) Data

Talhar¹,

Ramgirkar²,

Jena³

2022

ECS Trans.

View full text Add to dashboard Cite

show abstract

“…Through the integration of forgetting gate and input gate in LSTM network and the change of cell state, it optimizes the overall structure of the network, so as to improve the network solution speed while retaining the advantages of LSTM network. (Salehin et al., 2020) proposed a prediction model with LSTM for memory sequence data measurement, improving the speed of prediction task. However, this kind of method only considers the historical information of the current location and ignores the spatial information, resulting in poor performance in practice.…”

Section: Related Workmentioning

confidence: 99%

A Short‐Term Regional Precipitation Prediction Model Based on Wind‐Improved Spatiotemporal Convolutional Network

Qiu

Tang

2022

Earth and Space Science

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Accurate precipitation prediction can help decision makers judge the trend of climate change and formulate more effective measures, and prevent flood and drought disasters. In this paper, we propose a short‐term regional precipitation prediction model based on wind‐improved spatiotemporal convolutional network. Among them, the improved graph convolution network integrates the effects of wind direction and geographic location at past moments to capture the spatial dependence, whilst the gated recurrent unit captures the temporal dependence by learning the dynamic changes of data. The spatio‐temporal memory flow module and attention module are added to capture spatial deformation and temporal variation more accurately, thereby better matching the physical properties of precipitation. The proposed model achieves better prediction results on real data sets. Experiments show that our method is better at extracting the spatio‐temporal information of precipitation data and capturing its time dependence and spatial correlation.

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“…The key point of MLP is to develop a regression model to create and mapping help hand. To the data analysis, Rainfall, Humidity, Temperature are used as independent [35][36] variables where is production is dependent.…”

Section: Regression Analysismentioning

confidence: 99%