A novel intelligent deep learning predictive model for meteorological drought forecasting

Mehr, Ali Danandeh; Ghiasi, Amir Rikhtehgar; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher; Şorman, A. Ünal; Abualigah, Laith

doi:10.1007/s12652-022-03701-7

Cited by 91 publications

(35 citation statements)

References 34 publications

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“…In this continuation of the work, we also tested the use of input data related to topography and time of year. It is important to point out that we are not aiming to compare specific variations of basic architectures like [ 16 , 24 , 25 , 26 ] on different datasets but rather want to compare to the basic architectures themselves. In addition, these methods usually have a lot of parameters and will result in over-fitting on our dataset with a short time horizon.…”

Section: Resultsmentioning

confidence: 99%

Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources

Eide

Riegler

Hammer

et al. 2022

Sensors

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Many data related problems involve handling multiple data streams of different types at the same time. These problems are both complex and challenging, and researchers often end up using only one modality or combining them via a late fusion based approach. To tackle this challenge, we develop and investigate the usefulness of a novel deep learning method called tower networks. This method is able to learn from multiple input data sources at once. We apply the tower network to the problem of short-term temperature forecasting. First, we compare our method to a number of meteorological baselines and simple statistical approaches. Further, we compare the tower network with two core network architectures that are often used, namely the convolutional neural network (CNN) and convolutional long short-term memory (convLSTM). The methods are compared for the task of weather forecasting performance, and the deep learning methods are also compared in terms of memory usage and training time. The tower network performs well in comparison both with the meteorological baselines, and with the other core architectures. Compared with the state-of-the-art operational Norwegian weather forecasting service, yr.no, the tower network has an overall 11% smaller root mean squared forecasting error. For the core architectures, the tower network documents competitive performance and proofs to be more robust compared to CNN and convLSTM models.

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Section: Resultsmentioning

confidence: 99%

Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources

Eide

Riegler

Hammer

et al. 2022

Sensors

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“…However, as shown in Table 1 for both stations, the hybrid GARF model led to a satisfactory predictive skill with NSE higher than 70%. It should be noted that previous studies highlighted that this is a satisfactory range for SPEI forecasting [10,41]. A comparison between the predictive accuracy of classic RF and genetic programming for SPEI forecasting at the same stations was also performed in the relevant literature [42].…”

Section: Discussionmentioning

confidence: 99%

“…However, it is important to note that different PET estimation methods could result in different SPEI values. Details on the temperature and precipitation datasets are available in [10,26]. The SPEI-3 and SPEI-6 were aimed to forecast over the lead time of one month in this study.…”

Section: Methodsmentioning

confidence: 99%

“…More recent studies consider another class called ecological drought when a natural ecosystem is affected by the drought [7]. Each category needs some detecting variables-namely, evapotranspiration and precipitation for meteorological drought, evaporation stress and soil water content for agricultural drought, runoff scarcity and groundwater supplies fraction for hydrologic drought, and information on inflow-demand reliability and water storage resilience for socioeconomic drought [8][9][10]. Meteorological variables such as air temperature and precipitation are frequently used for meteorological drought monitoring and forecasting [11].…”

Section: Introductionmentioning

confidence: 99%

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A New Evolutionary Hybrid Random Forest Model for SPEI Forecasting

Mehr

Haghighi

Jabarnejad

et al. 2022

Water

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State-of-the-art random forest (RF) models have been documented as versatile tools to solve regression and classification problems in hydrology. They can model stochastic time series by bagging different decision trees. This article introduces a new hybrid RF model that increases the forecasting accuracy of RF-based models. The new model, called GARF, is attained by integrating genetic algorithm (GA) and hybrid random forest (RF), in which different decision trees are bagged. We applied GARF to model and forecast a multitemporal drought index (SPEI-3 and SPEI-6) at two meteorology stations (Beypazari and Nallihan) in Ankara, Turkey. We compared the associated results with classic RF, standalone extreme learning machine (ELM), and a hybrid ELM model optimized by Bat algorithm (Bat-ELM) to verify the new model accuracy. The performance assessment was performed using graphical and statistical analysis. The forecasting results demonstrated that the GARF outperformed the benchmark models. GARF achieved the least error in a quantitative assessment for the prediction of both SPEI-3 and SPEI-6, particularly in the testing period. The results of this study showed that the new model can improve the forecasting accuracy of the classic RF technique up to 30% and 40% at Beypazari and Nallihan stations, respectively.

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“…A high quality of living can be achieved when nations prioritize the growth and development of their economies in order to maintain adequate levels of public spending. In today's fast-paced economy, big businesses spring up to take advantage of untapped opportunities and adapt to the constant flux of the international marketplace [1,2]. The stock market is a marketplace where diverse assets may be bought and sold by participating investors on public, private, and mixed-ownership stock exchanges [3].…”

Section: Introductionmentioning

confidence: 99%

Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

Aldhyani

Alzahrani

2022

Electronics

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The creation of trustworthy models of the equities market enables investors to make better-informed choices. A trading model may lessen the risks that are connected with investing and make it possible for traders to choose companies that offer the highest dividends. However, due to the high degree of correlation between stock prices, analysis of the stock market is made more difficult by batch processing approaches. The prediction of the stock market has entered a technologically advanced era with the advent of technological marvels such as global digitization. For this reason, artificial intelligence models have become very important due to the continuous increase in market capitalization. The novelty of the proposed study is the development of the robustness time series model based on deep leaning for forecasting future values of stock marketing. The primary purpose of this study was to develop an intelligent framework with the capability of predicting the direction in which stock market prices will move based on financial time series as inputs. Among the cutting-edge technologies, artificial intelligence has become the backbone of many different models that predict the direction of markets. In particular, deep learning strategies have been effective at forecasting market behavior. In this article, we propose a framework based on long short-term memory (LSTM) and a hybrid of a convolutional neural network (CNN-LSTM) with LSTM to predict the closing prices of Tesla, Inc. and Apple, Inc. These predictions were made using data collected over the past two years. The mean squared error (MSE), root mean squared error (RMSE), normalization root mean squared error (NRMSE), and Pearson’s correlation (R) measures were used in the computation of the findings of the deep learning stock prediction models. Between the two deep learning models, the CNN-LSTM model scored slightly better (Tesla: R-squared = 98.37%; Apple: R-squared = 99.48%). The CNN-LSTM model showed a superior performance compared with the single deep learning LSTM and existing systems in predicting stock market prices.

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A novel intelligent deep learning predictive model for meteorological drought forecasting

Cited by 91 publications

References 34 publications

Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources

Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources

A New Evolutionary Hybrid Random Forest Model for SPEI Forecasting

Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

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