Machine Learning Based Weather Prediction Model for Short Term Weather Prediction in Sri Lanka

Long Short Term Memory (LSTM) is known as optimized Recurrent Neural Network (RNN) architectures that overcome RNN’s lact about maintaining long period of memories. As part of machine learning networks, LSTM also notable as the right choice for time-series prediction. Currently, machine learning is a burning issue in economic world, abundant studies such predicting macroeconomic and microeconomics indicators are emerge. Inflation rate has been used for decision making for central banks also private sector. In Indonesia, CPI (Consumer Price Index) is one of best practice inflation indicators besides Wholesale Price Index and The Gross Domestic Product (GDP). Since CPI data could be used as a direction for next inflation move, we conducted CPI prediction model using LSTM method. The network model input consists of 28 variables of staple price in Surabaya and the output is CPI value, also the entire development of prediction model are done in Amazon Web Service (AWS) Cloud. In the interest of accuracy improvement, we used several optimization algorithm i.e. Stochastic Gradient Descent (sgd), Root Mean Square Propagation (RMSProp), Adaptive Gradient(AdaGrad), Adaptive moment (Adam), Adadelta, Nesterov Adam (Nadam) and Adamax. The results indicate that Nadam has 4,008 RMSE’s value, less than other algorithm which indicate the most accurate optimization algorithm to predict CPI value.

Download Full-text

Short period forecasting of catchment-scale precipitation. Part II: a water-balance storm model for short-term rainfall and flood forecasting

Hydrology and Earth System Sciences ◽

10.5194/hess-4-635-2000 ◽

2000 ◽

Vol 4 (4) ◽

pp. 635-651 ◽

Cited By ~ 8

Author(s):

V. A. Bell ◽

R. J. Moore

Keyword(s):

Prediction Model ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Flood Forecasting ◽

Numerical Weather Prediction Model ◽

Short Term ◽

Catchment Scale ◽

Rainfall Model ◽

Radar Satellite ◽

Storm Model

Abstract. A simple two-dimensional rainfall model, based on advection and conservation of mass in a vertical cloud column, is investigated for use in short-term rainfall and flood forecasting at the catchment scale under UK conditions. The model is capable of assimilating weather radar, satellite infra-red and surface weather observations, together with forecasts from a mesoscale numerical weather prediction model, to obtain frequently updated forecasts of rainfall fields. Such data assimilation helps compensate for the simplified model dynamics and, taken together, provides a practical real-time forecasting scheme for catchment scale applications. Various ways are explored for using information from a numerical weather prediction model (16.8 km grid) within the higher resolution model (5 km grid). A number of model variants is considered, ranging from simple persistence and advection methods used as a baseline, to different forms of the dynamic rainfall model. Model performance is assessed using data from the Wardon Hill radar in Dorset for two convective events, on 10 June 1993 and 16 July 1995, when thunderstorms occurred over southern Britain. The results show that (i) a simple advection-type forecast may be improved upon by using multiscan radar data in place of data from the lowest scan, and (ii) advected, steady-state predictions from the dynamic model, using "inferred updraughts", provides the best performance overall. Updraught velocity is inferred at the forecast origin from the last two radar fields, using the mass-balance equation and associated data and is held constant over the forecast period. This inference model proves superior to the buoyancy parameterisation of updraught employed in the original formulation. A selection of the different rainfall forecasts is used as input to a catchment flow forecasting model, the IH PDM (Probability Distributed Moisture) model, to assess their effect on flow forecast accuracy for the 135 km2 Brue catchment in Somerset. Keywords: rainfall forecasting, flood forecasting, weather radar, satellite, storm model

Download Full-text

Developing a short-term prediction model for asthma exacerbations from Swedish primary care patients’ data using machine learning - based on the ARCTIC study

Respiratory Medicine ◽

10.1016/j.rmed.2021.106483 ◽

2021 ◽

pp. 106483

Author(s):

Karin Lisspers ◽

Björn Ställberg ◽

Kjell Larsson ◽

Christer Janson ◽

Mario Müller ◽

...

Keyword(s):

Machine Learning ◽

Primary Care ◽

Prediction Model ◽

The Arctic ◽

Short Term ◽

Asthma Exacerbations ◽

Term Prediction ◽

Primary Care Patients ◽

Short Term Prediction

Download Full-text

Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional Generative Adversarial Networks for multiple domains

10.5194/gmd-2021-405 ◽

2021 ◽

Author(s):

Suyeon Choi ◽

Yeonjoo Kim

Keyword(s):

Machine Learning ◽

South Korea ◽

Transfer Learning ◽

Weather Prediction ◽

Generative Adversarial Networks ◽

Lead Times ◽

Successful Implementation ◽

Short Term ◽

Radar Images ◽

Learning Technique

Abstract. Numerical weather prediction models and probabilistic extrapolation methods using radar images have been widely used for precipitation nowcasting. Recently, machine-learning-based precipitation nowcasting models have also been actively developed for relatively short-term precipitation predictions. This study aimed to develop a radar-based precipitation nowcasting model using an advanced machine learning technique, conditional generative adversarial network (cGAN), which shows high performance in image generation tasks. The cGAN-based precipitation nowcasting model, named Rad-cGAN, developed in this study was trained with a radar reflectivity map of the Soyang-gang Dam region in South Korea with a spatial domain of 128 × 128 km, spatial resolution of 1 km, and temporal resolution of 10 min. The model performance was evaluated using previously developed machine-learning-based precipitation nowcasting models, namely convolutional long short-term memory (ConvLSTM) and U-Net, as well as the baseline Eulerian persistence model. We demonstrated that Rad-cGAN outperformed other models not only for the chosen site but also for the entire domain across the Soyang-gang Dam region. Additionally, the proposed model maintained good performance even with lead times up to 80 min based on the critical success index at the intensity threshold of 0.1 mm h−1, while RainNet and ConvLSTM achieved lead times of 70 and 40 min, respectively. We also demonstrated the successful implementation of the transfer learning technique to efficiently train model with the data from other dam regions in South Korea, such as the Andong and Chungju Dam regions. We used pre-trained model, which was completely trained in the Soyang-gang Dam region. This study confirms that Rad-cGAN can be successfully applied to precipitation nowcasting with longer lead times, and using the transfer learning approach it shows good performance in regions other than the originally trained region.

Download Full-text