scholarly journals Deep learning model for daily rainfall prediction: case study of Jimma, Ethiopia

2021 ◽  
Author(s):  
Demeke Endalie ◽  
Getamesay Haile ◽  
Wondmagegn Taye
Keyword(s):  
Agricultural Sector ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Daily Rainfall ◽  
Weather Conditions ◽  
Percentage Error ◽  
Support Vector ◽  
Rainfall Forecasting ◽  
Rainfall Prediction ◽  
Proposed Model

Abstract Rainfall prediction is a critical task because many people rely on it, particularly in the agricultural sector. Rainfall forecasting is difficult due to the ever-changing nature of weather conditions. In this study, we carry out a rainfall predictive model for Jimma, a region located in southwestern Oromia, Ethiopia. We proposed a Long Short-Term Memory (LSTM)-based prediction model capable of forecasting Jimma's daily rainfall. Experiments were conducted to evaluate the proposed models using various metrics such as Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) Nash-Sutcliffe model efficiency (NSE), and R2, and the results were 0.01, 0.4786 0.81 and 0.9972, respectively. We also compared the proposed model to existing machine learning regressions like Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT). The RMSE of MLP was the lowest of the four existing learning models i.e., 0.03. The proposed LSTM model outperforms the existing models, with an RMSE of 0.01. The experimental results show that the proposed model has a lower RMSE and a higher R2.

scholarly journals Forecasting Brazilian Ethanol Spot Prices Using LSTM

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7987
Author(s):  
Gustavo Carvalho Santos ◽  
Flavio Barboza ◽  
Antônio Cláudio Paschoarelli Veiga ◽  
Mateus Ferreira Silva
Keyword(s):  
Short Term Memory ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Spot Prices ◽  
Statistical Measures ◽  
Squared Error ◽  
Proposed Model ◽  
Price Trends ◽  
Long Short Term Memory ◽  
Crisis Scenarios

Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of this biofuel in the world. The uncertainty of price direction in the future increases the risk for agents operating in this market and can affect a dependent price chain, such as food and gasoline. This paper uses the architecture of recurrent neural networks—Long short-term memory (LSTM)—to predict Brazilian ethanol spot prices for three horizon-times (12, 6 and 3 months ahead). The proposed model is compared to three benchmark algorithms: Random Forest, SVM Linear and RBF. We evaluate statistical measures such as MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and accuracy to assess the algorithm robustness. Our findings suggest LSTM outperforms the other techniques in regression, considering both MSE and MAPE but SVM Linear is better to identify price trends. Concerning predictions per se, all errors increase during the pandemic period, reinforcing the challenge to identify patterns in crisis scenarios.

scholarly journals Research on Forecast of Global COVID-19 Development Trend

2021 ◽  
Author(s):  
Yunyun Cheng ◽  
Yanping Bai ◽  
Ting Xu ◽  
Rong Cheng ◽  
Hongping Hu
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Virus Disease ◽  
Development Trend ◽  
Control Measures ◽  
Percentage Error ◽  
Support Vector ◽  
Trend Prediction ◽  
The Development Trend

Abstract The rapid spread of Corona Virus Disease 2019 (COVID-2019) has seriously threatened people’s health and brought huge challenges to the medical systems of many countries. So it is necessary to predict the epidemic trend scientifically and accurately. In this paper, the development trend prediction of the global daily cumulative confirmed case, based on data from February 2, 2020 to August 14, 2021 using linear regression and nonlinear regression methods, which are auto regressive integrated moving average (ARIMA), wavelet neural network (WNN), support vector machine (SVM), recurrent neural network (RNN) and long short-term memory (LSTM). Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to evaluate the prediction accuracy of the five models. The experimental results showed that the LSTM used in this study is more accurate for predicting the development trend of global cumulative confirmed cases. And its three evaluation indexes were 0.0936%, 0.1123%, and 0.0962% respectively, which were the smallest compared with the other four models. The LSTM model was used to predict the global cumulative confirmed cases in the next five days. The prediction results showed that the global cumulative confirmed cases will rise steadily and exceed 205 million. Therefore, the health departments of various countries should take appropriate prevention and control measures in advance.

scholarly journals Rough Set Based Affinity Propagation Model for Prediction of Future Gold Price in Indian Scenario

2019 ◽  
Vol 9 (2) ◽  
pp. 2219-2224
Keyword(s):  
Rough Set ◽  
Arima Model ◽  
Gold Price ◽  
Propagation Model ◽  
Affinity Propagation ◽  
Experimental Result ◽  
Percentage Error ◽  
Support Vector ◽  
Proposed Model ◽  
Gold Silver

In international market, trading of metals has played a vital role. Metal cost might affect the nation’s economy. There are so many base metals available which have been utilized in world trading for construction and manufacturing of goods. Among them gold, silver, platinum, palladium have been treated as precious metals which has economic values. Therefore today’s researchers have concentrated their investigation on metal prediction using diversified algorithms like Auto Regressive Integrated Moving Average (ARIMA), KNN (K-Nearest Neighbor),Artificial Neural Network (ANN) and Support Vector Machine (SVM) etc. In this paper our foremost objective is to predict gold price, so we put our research on this metal. In this work we have employed rough set based affinity propagation algorithm for predicting future gold price and we compared our proposed model with rough set and ARIMA model basing upon the performance measures such as root mean square error (RMSE) and mean absolute percentage error (MAPE). The experimental result shows that the proposed model outperforms rough set and ARIMA model

scholarly journals A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Lu Yu ◽  
Jianling Qu ◽  
Feng Gao ◽  
Yanping Tian
Keyword(s):  
Fault Diagnosis ◽  
Short Term Memory ◽  
Operating Conditions ◽  
Support Vector ◽  
Rolling Bearings ◽  
Hierarchical Algorithm ◽  
Bearing Fault Diagnosis ◽  
Rotary Machinery ◽  
Proposed Model ◽  
Series Of Experiments

Faced with severe operating conditions, rolling bearings tend to be one of the most vulnerable components in mechanical systems. Due to the requirements of economic efficiency and reliability, effective fault diagnosis methods for rolling bearings have long been a hot research topic of rotary machinery fields. However, traditional methods such as support vector machine (SVM) and backpropagation neural network (BP-NN) which are composed of shallow structures trap into a dilemma when further improving their accuracies. Aiming to overcome shortcomings of shallow structures, a novel hierarchical algorithm based on stacked LSTM (long short-term memory) is proposed in this text. Without any preprocessing operation or manual feature extraction, the proposed method constructs a framework of end-to-end fault diagnosis system for rolling bearings. Beneficial from the memorize-forget mechanism of LSTM, features inherent in raw temporal signals are extracted hierarchically and automatically by stacking LSTM. A series of experiments demonstrate that the proposed model can not only achieve up to 99% accuracy but also outperform some state-of-the-art intelligent fault diagnosis methods.

scholarly journals Spatiotemporal Traffic Flow Prediction with KNN and LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Xianglong Luo ◽  
Danyang Li ◽  
Yu Yang ◽  
Shengrui Zhang
Keyword(s):  
Support Vector Regression ◽  
Traffic Flow ◽  
Prediction Models ◽  
Short Term Memory ◽  
Transportation Systems ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Proposed Model

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

scholarly journals A Model Tree-Based Vehicle Emission Model at Freeway Toll Plazas

2020 ◽  
Vol 12 (21) ◽  
pp. 8959
Author(s):  
Yueru Xu ◽  
Chao Wang ◽  
Yuan Zheng ◽  
Zhuoqun Sun ◽  
Zhirui Ye
Keyword(s):  
Vehicle Emissions ◽  
Polynomial Regression ◽  
Mean Squared Error ◽  
Percentage Error ◽  
Vehicle Emission ◽  
Emission Model ◽  
Toll Plazas ◽  
Model Tree ◽  
Proposed Model ◽  
Toll Collection

With the increased concern over sustainable development, many efforts have been made to alleviate air quality deterioration. Freeway toll plazas can cause serious pollution, due to the increased emissions caused by stop-and-go operations. Different toll collections and different fuel types obviously influence the vehicle emissions at freeway toll plazas. Therefore, this paper proposes a model tree-based vehicle emission model by considering these factors. On-road emissions data and vehicle operation data were obtained from two different freeway toll plazas. The statistical analysis indicates that different methods of toll collection and fuel types have significant impacts on vehicle emissions at freeway toll plazas. The performance of the proposed model was compared with a polynomial regression method. Based on the results, the mean absolute percentage error (MAPE), root mean squared error (RMSE), and mean absolute error (MAE) of the proposed model were all smaller, while the R-squared value increased from 0.714 to 0.833. Finally, the variations of vehicle emissions at different locations of freeway toll plazas were calculated and shown in heat maps. The results of this study can help better estimate the vehicle emissions and give advice to the development of electronic toll collection (ETC) lanes and relevant policies at freeway toll plazas.

scholarly journals On the Prediction of Biogas Production from Vegetables, Fruits, and Food Wastes by ANFIS- and LSSVM-Based Models

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yong Yang ◽  
Shuaishuai Zheng ◽  
Zhilu Ai ◽  
Mohammad Mahdi Molla Jafari
Keyword(s):  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Biogas Production ◽  
Input Parameter ◽  
Machine Learning Algorithms ◽  
Least Square ◽  
Support Vector ◽  
Robust Algorithms ◽  
Inference System ◽  
Proposed Model

This study is aimed at modeling biodigestion systems as a function of the most influencing parameters to generate two robust algorithms on the basis of the machine learning algorithms, including adaptive network-based fuzzy inference system (ANFIS) and least square support vector machine (LSSVM). The models are assessed utilizing multiple statistical analyses for the actual values and model outcomes. Results from the suggested models indicate their great capability of predicting biogas production from vegetable food, fruits, and wastes for a variety of ranges of input parameters. The values that are calculated for the mean relative error (MRE %) and mean squared error (MSE) were 29.318 and 0.0039 for ANFIS, and 2.951 and 0.0001 for LSSVM which shows that the latter model has a better ability to predict the target data. Finally, in order to have additional certainty, two analyses of outlier identification and sensitivity were performed on the input parameter data that proved the proposed model in this paper has higher reliability in assessing output values compared with the previous model.

scholarly journals COLLAPSE WARNING SYSTEM USING LSTM NEURAL NETWORKS FOR CONSTRUCTION DISASTER PREVENTION IN EXTREME WIND WEATHER

2021 ◽  
Vol 27 (4) ◽  
pp. 230-245
Author(s):  
Chih-Chiang Wei
Keyword(s):  
Neural Networks ◽  
Prediction Models ◽  
Short Term Memory ◽  
Meteorological Data ◽  
Warning System ◽  
Weather Conditions ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Strong Wind ◽  
Wind Force

Strong wind during extreme weather conditions (e.g., strong winds during typhoons) is one of the natural factors that cause the collapse of frame-type scaffolds used in façade work. This study developed an alert system for use in determining whether the scaffold structure could withstand the stress of the wind force. Conceptually, the scaffolds collapsed by the warning system developed in the study contains three modules. The first module involves the establishment of wind velocity prediction models. This study employed various deep learning and machine learning techniques, namely deep neural networks, long short-term memory neural networks, support vector regressions, random forest, and k-nearest neighbors. Then, the second module contains the analysis of wind force on the scaffolds. The third module involves the development of the scaffold collapse evaluation approach. The study area was Taichung City, Taiwan. This study collected meteorological data from the ground stations from 2012 to 2019. Results revealed that the system successfully predicted the possible collapse time for scaffolds within 1 to 6 h, and effectively issued a warning time. Overall, the warning system can provide practical warning information related to the destruction of scaffolds to construction teams in need of the information to reduce the damage risk.

scholarly journals RNN-based Dimensional Speech Emotion Recognition

2020 ◽  
Author(s):  
Bagus Tris Atmaja
Keyword(s):  
Emotion Recognition ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Recognition System ◽  
Speech Emotion Recognition ◽  
Percentage Error ◽  
Concordance Correlation ◽  
Acoustic Feature ◽  
Dense System

◆ A speech emotion recognition system based on recurrent neural networks is developed using long short-term memory networks.◆ Two of acoustic feature sets are evaluated: 31 Features (3 time-domain features, 5 frequency-domain features, 13 MFCCs, 5 F0s, and 5 Harmonics) and eGeMaps feature set (23 features).◆ To evaluate the performance, some metrics are used i.e. mean squared error (MSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and concordance correlation coefficient (CCC). Among those metrics, CCC is main focus as it is used by other researchers.◆ The developed system used multi-task learning to maximize arousal, valence, and dominance at the same time using CCC loss (1 - CCC). The result shows using LSTM networks improve the CCC score compared to baseline dense system. The best CCC score isobtained on arousal followed by dominance and valence.

scholarly journals Urban Overheating Assessment through Prediction of Surface Temperatures: A Case Study of Karachi, Pakistan

2021 ◽  
Vol 10 (8) ◽  
pp. 539
Author(s):  
Bilal Aslam ◽  
Ahsen Maqsoom ◽  
Nauman Khalid ◽  
Fahim Ullah ◽  
Samad Sepasgozar
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Short Term Memory ◽  
Thermal Emission ◽  
Global Climate ◽  
Current Model ◽  
Mitigation Strategies ◽  
Percentage Error ◽  
Support Vector ◽  
Road Density

Global climate has been radically affected by the urbanization process in recent years. Karachi, Pakistan’s economic hub, is also showing signs of swift urbanization. Owing to the construction of infrastructure projects under the China-Pakistan Economic Corridor (CPEC) and associated urbanization, Karachi’s climate has been significantly affected. The associated replacement of natural surfaces by anthropogenic materials results in urban overheating and increased local temperatures leading to serious health issues and higher air pollution. Thus, these temperature changes and urban overheating effects must be addressed to minimize their impact on the city’s population. For analyzing the urban overheating of Karachi city, LST (land surface temperature) is assessed in the current study, where data of the past 20 years (2000–2020) is used. For this purpose, remote sensing data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) and Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors were utilized. The long short-term memory (LSTM) model was utilized where the road density (RD), elevation, and enhanced vegetation index (EVI) are used as input parameters. Upon comparing estimated and measured LST, the values of mean absolute error (MAE), mean square error (MSE), and mean absolute percentage error (MAPE) are 0.27 K, 0.237, and 0.15% for January, and 0.29 K, 0.261, and 0.13% for May, respectively. The low MAE, MSE, and MAPE values show a higher correlation between the predicted and observed LST values. Moreover, results show that more than 90% of the pixel data falls in the least possible error range of −1 K to +1 K. The MAE, MSE and MAPE values for Support Vector Regression (SVR) are 0.52 K, 0.453 and 0.18% and 0.76 K, 0.873, and 0.26%. The current model outperforms previous studies, shows a higher accuracy, and depicts greater reliability to predict the actual scenario. In the future, based on the accurate LST results from this model, city planners can propose mitigation strategies to reduce the harmful effects of urban overheating and associated Urban Heat Island effects (UHI).

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