Cooperative neuro-evolution of Elman recurrent networks for tropical cyclone wind-intensity prediction in the South Pacific region

Chandra, Rohitash; Dayal, Kavina

doi:10.1109/cec.2015.7257103

Cited by 7 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, it can accumulate the historical information of the nonlinear dynamics of the atmospheric system by updating the weight matrix, hence improving the accuracy of typhoon track prediction. Chandra and Dayal and Chandra et al [15,16] also proved that RNNs are suitable for typhoon track prediction. Lian et al [17] proposed a novel data-driven deep-learning model composed of a multidimensional feature-selection layer, a convolution layer, and a gating-cycle unit layer.…”

Section: Deep-learning Methodsmentioning

confidence: 94%

Distributed Typhoon Track Prediction Based on Complex Features and Multitask Learning

et al. 2021

View full text Add to dashboard Cite

Typhoons are common natural phenomena that often have disastrous aftermaths, particularly in coastal areas. Consequently, typhoon track prediction has always been an important research topic. It chiefly involves predicting the movement of a typhoon according to its history. However, the formation and movement of typhoons is a complex process, which in turn makes accurate prediction more complicated; the potential location of typhoons is related to both historical and future factors. Existing works do not fully consider these factors; thus, there is significant room for improving the accuracy of predictions. To this end, we presented a novel typhoon track prediction framework comprising complex historical features—climatic, geographical, and physical features—as well as a deep-learning network based on multitask learning. We implemented the framework in a distributed system, thereby improving the training efficiency of the network. We verified the efficiency of the proposed framework on real datasets.

show abstract

Section: Deep-learning Methodsmentioning

confidence: 94%

Distributed Typhoon Track Prediction Based on Complex Features and Multitask Learning

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The prediction error of the XGBOOST model is comparable to that of a previously used model for 24 h predictions. The MAEs for a 24 h lead time in seven machine learning methods-k-nearest neighbor [21], neural network [45], fuzzy neural network [46], artificial neural network [5], multilayer feed forward neural nets [25], Elman recurrent network [24], and probabilistic neural network [2]-were determined for test samples and found to be 8.22, 3.44, 3.52, 4.74, 2.98, 3.58, and 2.93 m/s, respectively. Because the predictors used in the previous studies are not exactly the same as the predictors in this paper, we used a BPNN to predict the TC intensity under the same sample input parameter.…”

Section: Discussionmentioning

confidence: 99%

“…The MLP model may thus also be considered an alternative to the conventional operational forecast models for predicting TC intensity [23]. Other research predicted cyclone wind intensity in the South Pacific using Elman recurrent neural networks [24] and proved that the accuracy of TC intensity forecasts using a double hidden layer neural network is higher than that found by using a single layer neural network [25]. These previous models for predicting TC intensity have therefore been popular with researchers.Although these models have been used widely by many researchers to forecast TC intensity, each method has unique shortcomings in various areas, including predictive accuracy, model interpretability, and computational efficiency.…”

mentioning

confidence: 99%

Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific

et al. 2019

View full text Add to dashboard Cite

Coastal cities in China are frequently hit by tropical cyclones (TCs), which result in tremendous loss of life and property. Even though the capability of numerical weather prediction models to forecast and track TCs has considerably improved in recent years, forecasting the intensity of a TC is still very difficult; thus, it is necessary to improve the accuracy of TC intensity prediction. To this end, we established a series of predictors using the Best Track TC dataset to predict the intensity of TCs in the Western North Pacific with an eXtreme Gradient BOOSTing (XGBOOST) model. The climatology and persistence factors, environmental factors, brainstorm features, intensity categories, and TC months are considered inputs for the models while the output is the TC intensity. The performance of the XGBOOST model was tested for very strong TCs such as Hato (2017), Rammasum (2014), Mujiage (2015, and Hagupit (2014). The results obtained show that the combination of inputs chosen were the optimal predictors for TC intensification with lead times of 6, 12, 18, and 24 h. Furthermore, the mean absolute error (MAE) of the XGBOOST model was much smaller than the MAEs of a back propagation neural network (BPNN) used to predict TC intensity. The MAEs of the forecasts with 6, 12, 18, and 24 h lead times for the test samples used were 1.61, 2.44, 3.10, and 3.70 m/s, respectively, for the XGBOOST model. The results indicate that the XGBOOST model developed in this study can be used to improve TC intensity forecast accuracy and can be considered a better alternative to conventional operational forecast models for TC intensity prediction.The characteristics that affect TC intensity are nonlinear and thus difficult to predict [2]. In recent years, many researchers have studied TC intensity prediction primarily using numerical forecasting and statistical methods [6][7][8][9][10]. Numerical forecasting is the main tool used to forecast TCs around the world, and systems such as the European Centre for Medium-Range Weather Forecasts-Integrated Forecasting System (ECMWF-IFS) [11], the Japan Meteorological Agency's global spectral model (JMA-GSM) [12], and the National Centers for Environmental Prediction-Global Forecast System (NCEP-GFS) [13] have been developed as operational techniques. Statistical intensity forecast methods such as Climatology and Persistence (CLIPER) [14] and the Statistical Hurricane Intensity Prediction Scheme (SHIPS) [15] have also been developed as operational techniques. However, advancements in TC intensity prediction have been relatively slow, although notable work has been done in the past 20 years on predicting TC paths [16]. One of the reasons for this is that the internal structure of storms is not yet sufficiently understood. TCs have an asymmetric structure, which may be caused by thermal and dynamic factors such as uneven distribution of sea surface temperature and humidity, horizontal or vertical shear, and asymmetric distribution of convection [2,17]. Changes in TC intensity are controlled by many e...

show abstract

“…There has been a growing interest in computational intelligence and machine learning methods for cyclone prediction [13,23,21]. In the past, cyclone wind-intensity [5] been tackled by cooperative neuro-evolution of recurrent neural networks [7,6].…”

Section: Introductionmentioning

confidence: 99%

Stacked transfer learning for tropical cyclone intensity prediction

Deo,

Chandra,

Sharma

2017

Preprint

Self Cite

View full text Add to dashboard Cite

Tropical cyclone wind-intensity prediction is a challenging task considering drastic changes climate patterns over the last few decades. In order to develop robust prediction models, one needs to consider different characteristics of cyclones in terms of spatial and temporal characteristics. Transfer learning incorporates knowledge from a related source dataset to compliment a target datasets especially in cases where there is lack or data. Stacking is a form of ensemble learning focused for improving generalization that has been recently used for transfer learning problems which is referred to as transfer stacking. In this paper, we employ transfer stacking as a means of studying the effects of cyclones whereby we evaluate if cyclones in different geographic locations can be helpful for improving generalization performance. Moreover, we use conventional neural networks for evaluating the effects of duration on cyclones in prediction performance. Therefore, we develop an effective strategy that evaluates the relationships between different types of cyclones through transfer learning and conventional learning methods via neural networks.

show abstract

Cooperative neuro-evolution of Elman recurrent networks for tropical cyclone wind-intensity prediction in the South Pacific region

Cited by 7 publications

References 33 publications

Distributed Typhoon Track Prediction Based on Complex Features and Multitask Learning

Distributed Typhoon Track Prediction Based on Complex Features and Multitask Learning

Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific

Stacked transfer learning for tropical cyclone intensity prediction

Contact Info

Product

Resources

About