การพยากรณ์ระดับน้ำโดยแบบจำลองโครงข่ายประสาทเทียมด้วยข้อมูลปริมาณน้ำฝนจากแบบจำลอง WRF-ECHAM5 (WATER LEVEL FORECASTING BY ARTIFICIAL NEURAL NETWORK MODEL WITH RAINFALL DATA FROM WRF-ECHAM5 MODEL)

การเปลี่ยนแปลงสภาพภูมิอากาศในปัจจุบันส่งผลให้มีฝนตกหนักเพิ่มขึ้นและส่งผลกระทบทำให้เกิดน้ำท่วม ในระดับภูมิภาคมีการใช้แบบจำลองสภาพภูมิอากาศระดับภูมิภาค (WRF - ECHAM5) คาดการณ์ปริมาณน้ำฝนในอนาคต ขณะที่แบบจำลองโครงข่ายประสาทเทียม ได้นำมาใช้ในการพยากรณ์น้ำท่วมอย่างแพร่หลาย ในงานวิจัยนี้ได้ใช้ข้อมูลน้ำฝนจากแบบจำลอง WRF - ECHAM5 (Weather Research and Forecasting - ECHAM5) เป็นข้อมูลนำเข้าสำหรับการพยากรณ์ระดับน้ำโดยใช้แบบจำลองโครงข่ายประสาทเทียม ซึ่งได้ใช้เหตุการณ์น้ำท่วมในช่วง ค.ศ. 1980-2006 เพื่อศึกษาหาโครงสร้างสถาปัตยกรรมของแบบจำลองที่เหมาะสมสำหรับการพยากรณ์ มีการเปรียบเทียบกระบวนการเรียนรู้แบบ Levenberg-Marquardt (LM) และ Bayesian Regularization (BR) และการกำหนดจำนวนโหนดในชั้นซ่อนเร้นที่แตกต่างกันซึ่งอ้างอิงตามจำนวนตัวแปรของข้อมูลนำเข้า (50%, n, n+50% และ 2n) รวมทั้งมีการเปรียบเทียบประเภทของข้อมูลนำเข้าระหว่างการใช้ค่าน้ำฝนจากกริด และค่าน้ำฝนที่ได้ทำการหาค่าเฉลี่ยแบบเคลื่อนที่ ผลการวิจัยพบว่าโครงสร้างสถาปัตยกรรมของแบบจำลองโครงข่ายประสาทเทียมที่เหมาะสมคือ การเรียนรู้แบบ LM และจำนวนโหนดในชั้นซ่อนเร้นควรกำหนด 50% ของจำนวนข้อมูลนำเข้า และยังพบว่าการนำค่าน้ำฝนมาหาค่าเฉลี่ยแบบเคลื่อนที่จะให้ผลการพยากรณ์ได้ดีกว่าการใช้ค่าน้ำฝนจากกริดร่วมกับการหาค่าเฉลี่ยแบบเคลื่อนที่คำสำคัญ: โครงข่ายประสาทเทียม  WRF-ECHAM5  พยากรณ์  ระดับน้ำThe current climate change increases the heavy rainfall and results in flooding. A regional climate model (WRF - ECHAM5) has been used for rainfall prediction. An Artificial Neural Network Model (ANN) is used for flood forecasting worldwide. This research uses rainfall data from WRF – ECHAM5 (Weather Research and Forecasting - ECHAM5) model as the input data during 1980-2006 for water level forecasting applying Artificial Neural Network Model (ANN) to find out the suitable model architecture for forecasting by comparison learning algorithms Levenberg-Marquardt (LM) and Bayesian Regularization (BR), including setting different numbers of hidden node that are based on number of input variables (50%, n, n+50% and 2n). Moreover, different types of input data between rainfall from grid and moving average rainfall are compared. The results show that the suitable Artificial Neural Network architecture is LM learning algorithm and number of hidden nodes should be 50% of number of input variables. In addition, using moving average rainfall better than using both rainfall from grid and moving average rainfall.Keywords: Artificial Neural Network, WRF-ECHAM5, Forecasting, Water Leve

Forecasting Time Series Data Using Bayesian Regularization

Forecasting or predicting future events is important to take into account in order for an activity to proceed properly. Flights predict the weather forecast, the banking industry predicts the price of currency, the health world predicts the disease, the retail business predicts total sales. prediction or forecasting of events is calculated using past data, usually in the form of time series. Artificial neural networks are capable of forecasting time-series data. Forecasting results with artificial neural network is influenced from the network architecture model is determined, one of which determination of training function. This study uses the bayesian regularization training function to forecast time clock data with several layer count models and the number of neurons.The results obtained with the number of 3 layers and each neuron of 36, 12, 6 for the best process performance, and the number of neurons 24, 12, 6 for the shortest iteration process

Bayesian network model for flood forecasting based on atmospheric ensemble forecasts

The purpose of this study is to propose the Bayesian network (BN) model to estimate flood peaks from atmospheric ensemble forecasts (AEFs). The Weather Research and Forecasting (WRF) model was used to simulate historic storms using five cumulus parameterization schemes. The BN model was trained to compute flood peak forecasts from AEFs and hydrological pre-conditions. The mean absolute relative error was calculated as 0.076 for validation data. An artificial neural network (ANN) was applied for the same problem but showed inferior performance with a mean absolute relative error of 0.39. It seems that BN is less sensitive to small data sets, thus it is more suited for flood peak forecasting than ANN

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Bayesian rules and stochastic models for high accuracy prediction of solar radiation

It is essential to find solar predictive methods to massively insert renewable energies on the electrical distribution grid. The goal of this study is to find the best methodology allowing predicting with high accuracy the hourly global radiation. The knowledge of this quantity is essential for the grid manager or the private PV producer in order to anticipate fluctuations related to clouds occurrences and to stabilize the injected PV power. In this paper, we test both methodologies: single and hybrid predictors. In the first class, we include the multi-layer perceptron (MLP), auto-regressive and moving average (ARMA), and persistence models. In the second class, we mix these predictors with Bayesian rules to obtain ad-hoc models selections, and Bayesian averages of outputs related to single models. If MLP and ARMA are equivalent (nRMSE close to 40.5% for the both), this hybridization allows a nRMSE gain upper than 14 percentage points compared to the persistence estimation (nRMSE=37% versus 51%).Comment: Applied Energy (2013

Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model: Conventional Observation

This paper presents an approach for employing artificial neural networks (NN) to emulate an ensemble Kalman filter (EnKF) as a method of data assimilation. The assimilation methods are tested in the Simplified Parameterizations PrimitivE-Equation Dynamics (SPEEDY) model, an atmospheric general circulation model (AGCM), using synthetic observational data simulating localization of balloon soundings. For the data assimilation scheme, the supervised NN, the multilayer perceptrons (MLP-NN), is applied. The MLP-NN are able to emulate the analysis from the local ensemble transform Kalman filter (LETKF). After the training process, the method using the MLP-NN is seen as a function of data assimilation. The NN were trained with data from first three months of 1982, 1983, and 1984. A hind-casting experiment for the 1985 data assimilation cycle using MLP-NN were performed with synthetic observations for January 1985. The numerical results demonstrate the effectiveness of the NN technique for atmospheric data assimilation. The results of the NN analyses are very close to the results from the LETKF analyses, the differences of the monthly average of absolute temperature analyses is of order 0.02. The simulations show that the major advantage of using the MLP-NN is better computational performance, since the analyses have similar quality. The CPU-time cycle assimilation with MLP-NN is 90 times faster than cycle assimilation with LETKF for the numerical experiment.Comment: 17 pages, 16 figures, monthly weather revie

Probabilistic traffic breakdown forecasting through Bayesian approximation using variational LSTMs

Robust artificial intelligence models have been criticized for their lack of uncertainty control and inability to explain feature importance, which has limited their adoption. However, probabilistic machine learning and explainable artificial intelligence have shown great scientific and technical advances, and have slowly permeated other areas, such as Traffic Engineering. This thesis fulfils a literature gap related to probabilistic traffic breakdown forecasting. We propose a traffic breakdown probability calculation methodology based on probabilistic speed predictions. Since the probabilistic characteristic is absent in traditional formulations of neural networks, we suggest using Variational LSTMs to make the speed forecasts. This Recurrent Neural Network uses Dropout to produce a Bayesian approximation and generate probabilistic outputs. This thesis also investigates the effects of inclement weather on traffic breakdown probability and methods for identifying traffic breakdowns. The proposed methodology produces great control over the probability of congestion, which could not be achieved using deterministic models, resulting in important theoretical and practical contributions

Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach

Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependences, temporal dependences, and exogenous dependences need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependences within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. Experimental results, validated on real-world data provided by DiDi Chuxing, show that the FCL-Net achieves better predictive performance than traditional approaches including both classical time-series prediction models and neural network based algorithms (e.g., artificial neural network and LSTM). This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.Comment: 39 pages, 10 figure