scholarly journals An Amazon stingless bee foraging activity predicted using recurrent artificial neural networks and attribute selection

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Pedro A. B. Gomes ◽  
Yoshihiko Suhara ◽  
Patrícia Nunes-Silva ◽  
Luciano Costa ◽  
Helder Arruda ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Windows ◽  
Barometric Pressure ◽  
Environmental Data ◽  
Attribute Selection ◽  
Population Declines ◽  
Level Of Activity ◽  
Bee Foraging ◽  
Forecast Models ◽  
Recurrent Artificial Neural Networks

AbstractBees play a key role in pollination of crops and in diverse ecosystems. There have been multiple reports in recent years illustrating bee population declines worldwide. The search for more accurate forecast models can aid both in the understanding of the regular behavior and the adverse situations that may occur with the bees. It also may lead to better management and utilization of bees as pollinators. We address an investigation with Recurrent Neural Networks in the task of forecasting bees’ level of activity taking into account previous values of level of activity and environmental data such as temperature, solar irradiance and barometric pressure. We also show how different input time windows, algorithms of attribute selection and correlation analysis can help improve the accuracy of our model.

scholarly journals Air Quality Assessment and Pollution Forecasting using Recurrent Artificial Neural Networks in Metropolitan Lima-Peru

2021 ◽  
Author(s):  
Chardin Hoyos Cordova ◽  
Manuel Niño Lopez Portocarrero ◽  
Rodrigo Salas ◽  
Romina Torres ◽  
Paulo Canas Rodrigues ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Quality ◽  
Short Term Memory ◽  
Environmental Data ◽  
Air Quality Assessment ◽  
Artificial Neural ◽  
Spatio Temporal ◽  
Lstm Network ◽  
Recurrent Artificial Neural Networks

Abstract The prediction of air pollution is of great importance in highly populated areas because it has a direct impact on both the management of the city's economic activity and the health of its inhabitants. In this work, the spatio-temporal behavior of air quality in Metropolitan Lima was evaluated and predicted using the recurrent artificial neural network known as Long-Short Term Memory networks (LSTM). The LSTM was implemented for the hourly prediction of PM10 based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The model was evaluated under two validation schemes: the hold-out (HO) and the blocked-nested cross-validation (BNCV). The simulation results show that periods of low PM10 concentration are predicted with high precision. Whereas, for periods of high contamination, the LSTM network with BNCV has better predictability performance. In conclusion, recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better performance to forecast this type of environmental data, and can also be extrapolated to other pollutants.

scholarly journals A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang
Keyword(s):  
Neural Networks ◽  
Species Distribution ◽  
Best Practice ◽  
Deep Neural Networks ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Environmental Data ◽  
Distribution Model ◽  
Distribution Models ◽  
Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

scholarly journals Recurrent Artificial Neural Networks (RANN) for forecasting of forward interest rates

2000 ◽  
Vol 31 (4) ◽  
pp. 137-140
Author(s):  
Amine Bensaid ◽  
Bouchra Bouqata ◽  
Ralph Palliam
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Interest Rates ◽  
Recursive Method ◽  
Alternative Technique ◽  
Forward Rates ◽  
Artificial Neural ◽  
The One ◽  
Recurrent Artificial Neural Networks

There are numerous methods for estimating forward interest rates as well as many studies testing the accuracy of these methods. The approach proposed in this study is similar to the one in previous works in two respects: firstly, a Monte Carlo simulation is used instead of empirical data to circumvent empirical difficulties: and secondly, in this study, accuracy is measured by estimating the forward rates rather than by exploring bond prices. This is more consistent with user objectives. The method presented here departs from the others in that it uses a Recurrent Artificial Neural Network (RANN) as an alternative technique for forecasting forward interest rates. Its performance is compared to that of a recursive method which has produced some of the best results in previous studies for forecasting forward interest rates.

Reliability analysis of mooring lines for floating structures using ANN-BN inference

2020 ◽  
pp. 147509022092520
Author(s):  
Yuliang Zhao ◽  
Sheng Dong ◽  
Fengyuan Jiang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reliability Analysis ◽  
Failure Probability ◽  
Environmental Data ◽  
Mooring Lines ◽  
Widespread Application ◽  
Floating Structures ◽  
Extreme Response ◽  
Artificial Neural

The harsh marine environment is a significant threat to the safety of floating structure systems. To address this, mooring systems have seen widespread application as an important component in the stabilization of floating structures. This article proposes a methodology to assess the reliability of mooring lines under given extreme environmental conditions based on artificial neural network–Bayesian network inference. Different types of artificial neural networks, including radial basis function neural networks and back propagation neural networks, are adopted to predict the extreme response of mooring lines according to a series of measured environmental data. A failure database under extreme sea conditions is then established in accordance with the failure criterion of mooring systems. There is a failure of mooring lines when the maximum tension exceeds the allowable breaking strength. Finally, the reliability analysis of moored floating structures under different load directions is conducted using Bayesian networks. To demonstrate the proposed methodology, the failure probability of a sample semi-submersible platform at a water depth of 1500 m is estimated. This approach utilizes artificial neural networks’ capacity for calculation efficiency and validates artificial neural networks for the response prediction of floating structures. Furthermore, it can also be employed to estimate the failure probability of other complex floating structures.

Assessment and Correction of Solar Radiation Measurements with Simple Neural Networks

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1160
Author(s):  
Jason Kelley
Keyword(s):  
Neural Networks ◽  
Solar Radiation ◽  
Low Cost ◽  
Environmental Data ◽  
Data Driven ◽  
Technical Requirements ◽  
Standard Procedures ◽  
Comparable Accuracy ◽  
Radiation Sensors ◽  
Radiation Measurements

Solar radiation received at the Earth’s surface provides the energy driving all micro-meteorological phenomena. Local solar radiation measurements are used to estimate energy mediated processes such as evapotranspiration (ET); this information is important in managing natural resources. However, the technical requirements to reliably measure solar radiation limits more extensive adoption of data-driven management. High-quality radiation sensors are expensive, delicate, and require skill to maintain. In contrast, low-cost sensors are widely available, but may lack long-term reliability and intra-sensor repeatability. As weather stations measure solar radiation and other parameters simultaneously, machine learning can be used to integrate various types of environmental data, identify periods of erroneous measurements, and estimate corrected values. We demonstrate two case studies in which we use neural networks (NN) to augment direct radiation measurements with data from co-located sensors, and generate radiation estimates with comparable accuracy to the data typically available from agro-meteorology networks. NN models that incorporated radiometer data reproduced measured radiation with an R2 of 0.9–0.98, and RMSE less than 100 Wm−2, while models using only weather parameters obtained R2 less than 0.75 and RMSE greater than 140 Wm−2. These cases show that a simple NN implementation can complement standard procedures for estimating solar radiation, create opportunities to measure radiation at low-cost, and foster adoption of data-driven management.

scholarly journals Fuzzy Clustering-Based Ensemble Approach to Predicting Indian Monsoon

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Moumita Saha ◽  
Pabitra Mitra ◽  
Arun Chakraborty
Keyword(s):  
Neural Networks ◽  
Fuzzy Clustering ◽  
Prediction Models ◽  
Indian Monsoon ◽  
Single Model ◽  
Forecast Models ◽  
Climatic Event ◽  
Fuzzy Degree ◽  
Degree Of Severity ◽  
Climatic Phenomenon

Indian monsoon is an important climatic phenomenon and a global climatic marker. Both statistical and numerical prediction schemes for Indian monsoon have been widely studied in literature. Statistical schemes are mainly based on regression or neural networks. However, the variability of monsoon is significant over the years and a single model is often inadequate. Meteorologists revise their models on different years based on prevailing global climatic incidents like El-Niño. These indices often have degree of severity associated with them. In this paper, we cluster the monsoon years based on their fuzzy degree of associativity to these climatic event patterns. Next, we develop individual prediction models for the year clusters. A weighted ensemble of these individual models is used to obtain the final forecast. The proposed method performs competitively with existing forecast models.

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