Survey on weather prediction using big data analystics

Reddy, Pundru Chandra Shaker; Babu, A. Suresh

doi:10.1109/icecct.2017.8117883

Cited by 31 publications

(13 citation statements)

References 15 publications

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“…The flood control planning formulated as CCP in (37) is transformed into an equivalence problem of CCP as shown in (5) by using the above full data set B ⊆ 3 , |B| = 10 7 . For a solution x ∈ X of the equivalence problem of CCP, the joint probability defined by (4) and (37) becomesp(x, B) 0.906. By using SRS and WSS respectively, we estimate the value of the joint probabilityp(x, B).…”

Section: Comparison Of Srs and Wssmentioning

confidence: 99%

“…As stated above, the sample size is chosen as N 482. For a sufficiency level α ∈ (0, 1) in (37), the correction level in (18) is chosen as β = α + 0.03. The population size is decided as NP = 5 D by recommendation of the literature [18].…”

Section: Solution Of Ccpmentioning

confidence: 99%

“…Moreover, the majority of the solutions x b ∈ X satisfy the constraintp(x b , B) ≥ α. Therefore, if we suppose thatp(x b , B) p(x b , Ω) holds, the solution x b ∈ X is regarded as a feasible solution of CCP in (37). We also see the trade-off between the optimality of the solution x b ∈ X evaluated by f (x b ) and the reliability specified by α ∈ (0, 1).…”

Section: Solution Of Ccpmentioning

confidence: 99%

“…The short run time of ADE is attributable to the simple forest mechanism model given by (35). If the inflow of water is estimated through a complex mathematical computation taking hours [36] or the amount of rainfall is predicted from a huge weather data set [37], we must realize the advantage of the pruning technique that surely reduces the run time of ADE without harming the quality of the obtained solution. In any case, we can confirm the expected performance of the pruning technique from the high rates shown in Tables 3 and 4.…”

Section: Performance Evaluation Of Adepmentioning

confidence: 99%

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An Approach to Chance Constrained Problems Based on Huge Data Sets Using Weighted Stratified Sampling and Adaptive Differential Evolution

Tagawa

2020

Computers

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In this paper, a new approach to solve Chance Constrained Problems (CCPs) using huge data sets is proposed. Specifically, instead of the conventional mathematical model, a huge data set is used to formulate CCP. This is because such a large data set is available nowadays due to advanced information technologies. Since the data set is too large to evaluate the probabilistic constraint of CCP, a new data reduction method called Weighted Stratified Sampling (WSS) is proposed to describe a relaxation problem of CCP. An adaptive Differential Evolution combined with a pruning technique is also proposed to solve the relaxation problem of CCP efficiently. The performance of WSS is compared with a well known method, Simple Random Sampling. Then, the proposed approach is applied to a real-world application, namely the flood control planning formulated as CCP.

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Section: Comparison Of Srs and Wssmentioning

confidence: 99%

Section: Solution Of Ccpmentioning

confidence: 99%

Section: Solution Of Ccpmentioning

confidence: 99%

Section: Performance Evaluation Of Adepmentioning

confidence: 99%

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An Approach to Chance Constrained Problems Based on Huge Data Sets Using Weighted Stratified Sampling and Adaptive Differential Evolution

Tagawa

2020

Computers

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“…Moreover, with machine learning, data analysis, and prediction can be achieved without understanding the physical processes (e.g., applying the past data to predict future data [ 12 ]). Readers may refer to [ 13 ] for a full discussion.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Weather Monitoring and Prediction Using City Buses and Machine Learning

Huang

Chen²,

Wen

2020

Sensors

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Accurate weather data are important for planning our day-to-day activities. In order to monitor and predict weather information, a two-phase weather management system is proposed, which combines information processing, bus mobility, sensors, and deep learning technologies to provide real-time weather monitoring in buses and stations and achieve weather forecasts through predictive models. Based on the sensing measurements from buses, this work incorporates the strengths of local information processing and moving buses for increasing the measurement coverage and supplying new sensing data. In Phase I, given the weather sensing data, the long short-term memory (LSTM) model and the multilayer perceptron (MLP) model are trained and verified using the data of temperature, humidity, and air pressure of the test environment. In Phase II, the trained learning model is applied to predict the time series of weather information. In order to assess the system performance, we compare the predicted weather data with the actual sensing measurements from the Environment Protection Administration (EPA) and Central Weather Bureau (CWB) of Taichung observation station to evaluate the prediction accuracy. The results show that the proposed system has reliable performance at weather monitoring and a good forecast for one-day weather prediction via the trained models.

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