scholarly journals Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Vinushi Amaratunga ◽  
Lasini Wickramasinghe ◽  
Anushka Perera ◽  
Jeevani Jayasinghe ◽  
Upaka Rathnayake
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Climatic Factors ◽  
Computational Time ◽  
Training Algorithm ◽  
Climate Data ◽  
Nonlinear Relationships ◽  
The Future ◽  
Artificial Neural ◽  
Paddy Yield

Paddy harvest is extremely vulnerable to climate change and climate variations. It is a well-known fact that climate change has been accelerated over the past decades due to various human induced activities. In addition, demand for the food is increasing day-by-day due to the rapid growth of population. Therefore, understanding the relationships between climatic factors and paddy production has become crucial for the sustainability of the agriculture sector. However, these relationships are usually complex nonlinear relationships. Artificial Neural Networks (ANNs) are extensively used in obtaining these complex, nonlinear relationships. However, these relationships are not yet obtained in the context of Sri Lanka; a country where its staple food is rice. Therefore, this research presents an attempt in obtaining the relationships between the paddy yield and climatic parameters for several paddy grown areas (Ampara, Batticaloa, Badulla, Bandarawela, Hambantota, Trincomalee, Kurunegala, and Puttalam) with available data. Three training algorithms (Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugated Gradient (SCG)) are used to train the developed neural network model, and they are compared against each other to find the better training algorithm. Correlation coefficient (R) and Mean Squared Error (MSE) were used as the performance indicators to evaluate the performance of the developed ANN models. The results obtained from this study reveal that LM training algorithm has outperformed the other two algorithms in determining the relationships between climatic factors and paddy yield with less computational time. In addition, in the absence of seasonal climate data, annual prediction process is understood as an efficient prediction process. However, the results reveal that there is an error threshold in the prediction. Nevertheless, the obtained results are stable and acceptable under the highly unpredicted climate scenarios. The ANN relationships developed can be used to predict the future paddy yields in corresponding areas with the future climate data from various climate models.

Projecting thermal stratification and hypolimnetic oxygen conditions by coupling paleolimnological and 1D lake model approaches

2021 ◽  
Author(s):  
Jean-Philippe Jenny ◽  
Olivia Itier ◽  
Victor Frossard ◽  
David Etienne ◽  
Jean Guillard
Keyword(s):  
Climate Change ◽  
Thermal Regime ◽  
Thermal Stratification ◽  
Climatic Factors ◽  
Climate Data ◽  
The Past ◽  
Lake Model ◽  
Hypolimnetic Oxygen ◽  
The Future ◽  
Oxygen Conditions

<p>Climate change raises many questions about the future of lakes’ thermal regime and hypolimnetic oxygen conditions. One dimensional models have been widely implemented over that last years <sup>1–3</sup>, but most of these models are calibrated against very few years of limnological records, potentially limiting the robustness in long-term reconstructions and preventing inclusion of future scenarios. To analysis the variability and the effects of climate change on thermal regime and oxygen conditions of deep hard-water lakes, we relayed on paleolimnological records and 1D thermal lake model calibrated against time series of limnological data collected by the French Observatoire des LAcs (OLA). Continuous sediment records on four peri-alpine lakes (Lake Geneva, Lake Annecy, Lake Bourget and Lake Aiguebelette) were analysed using micro-XRF Mn-Fe ratio as proxy to infer near-annual trends of oxygen conditions for the past 300 years<sup>4</sup>. Past hypoxia dynamics were further inferred from varved records preserved in sediment cores<sup>5</sup>. General Lake Model (GLM), i.e. a 1-D modelling tool, has been constrained by climate data derived from meteorological observations and CMIP6 simulations in order to reconstruct and forcast stratification regims for the next century. Our paleolimnological results show that fluctuations in hypoxic volumes since the 1950s were great and that these fluctuations were essentially driven by climatic factors, legitimating the use of thermal model approaches for future projections of hypolimnetic oxygen conditions. In this line, thermal regime simulations based on GLM forecast an intensification in thermal stratification and an increase in volumes of water warmer than 9°C over the period 1850-2100 with potential consequence for hypolimnetic oxygen conditions and ecological habitats. Coupling model and paleolimnological approaches seem a promising way to examine the evolution of lakes in the past, and to realistically anticipate the future of lakes for the next decades.</p>

scholarly journals Machine Learning-Based Small Hydropower Potential Prediction under Climate Change

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3643
Author(s):  
Jaewon Jung ◽  
Heechan Han ◽  
Kyunghun Kim ◽  
Hung Soo Kim
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Energy Source ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Carbon Emission ◽  
Small Hydropower ◽  
Runoff Prediction ◽  
The Future ◽  
Artificial Neural

As the effects of climate change are becoming severe, countries need to substantially reduce carbon emissions. Small hydropower (SHP) can be a useful renewable energy source with a high energy density for the reduction of carbon emission. Therefore, it is necessary to revitalize the development of SHP to expand the use of renewable energy. To efficiently plan and utilize this energy source, there is a need to assess the future SHP potential based on an accurate runoff prediction. In this study, the future SHP potential was predicted using a climate change scenario and an artificial neural network model. The runoff was simulated accurately, and the applicability of an artificial neural network to the runoff prediction was confirmed. The results showed that the total amount of SHP potential in the future will generally a decrease compared to the past. This result is applicable as base data for planning future energy supplies and carbon emission reductions.

scholarly journals Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part II: Climate Change Impact Assessment)

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1548
Author(s):  
Suresh Marahatta ◽  
Deepak Aryal ◽  
Laxmi Prasad Devkota ◽  
Utsav Bhattarai ◽  
Dibesh Shrestha
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Models ◽  
Assessment Tool ◽  
Swat Model ◽  
Global Climate Models ◽  
Climate Data ◽  
The Future ◽  
The Impact ◽  
Mountainous River

This study aims at analysing the impact of climate change (CC) on the river hydrology of a complex mountainous river basin—the Budhigandaki River Basin (BRB)—using the Soil and Water Assessment Tool (SWAT) hydrological model that was calibrated and validated in Part I of this research. A relatively new approach of selecting global climate models (GCMs) for each of the two selected RCPs, 4.5 (stabilization scenario) and 8.5 (high emission scenario), representing four extreme cases (warm-wet, cold-wet, warm-dry, and cold-dry conditions), was applied. Future climate data was bias corrected using a quantile mapping method. The bias-corrected GCM data were forced into the SWAT model one at a time to simulate the future flows of BRB for three 30-year time windows: Immediate Future (2021–2050), Mid Future (2046–2075), and Far Future (2070–2099). The projected flows were compared with the corresponding monthly, seasonal, annual, and fractional differences of extreme flows of the simulated baseline period (1983–2012). The results showed that future long-term average annual flows are expected to increase in all climatic conditions for both RCPs compared to the baseline. The range of predicted changes in future monthly, seasonal, and annual flows shows high uncertainty. The comparative frequency analysis of the annual one-day-maximum and -minimum flows shows increased high flows and decreased low flows in the future. These results imply the necessity for design modifications in hydraulic structures as well as the preference of storage over run-of-river water resources development projects in the study basin from the perspective of climate resilience.

scholarly journals A Neural Network-Inspired Matrix Formulation of Chemical Kinetics for Acceleration on GPUs

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2710
Author(s):  
Shivam Barwey ◽  
Venkat Raman
Keyword(s):  
Neural Network ◽  
Chemical Kinetics ◽  
Graphics Processing Units ◽  
Source Term ◽  
Turbulent Flames ◽  
Peak Performance ◽  
Computational Time ◽  
Detailed Chemical Kinetics ◽  
The Matrix ◽  
Artificial Neural

High-fidelity simulations of turbulent flames are computationally expensive when using detailed chemical kinetics. For practical fuels and flow configurations, chemical kinetics can account for the vast majority of the computational time due to the highly non-linear nature of multi-step chemistry mechanisms and the inherent stiffness of combustion chemistry. While reducing this cost has been a key focus area in combustion modeling, the recent growth in graphics processing units (GPUs) that offer very fast arithmetic processing, combined with the development of highly optimized libraries for artificial neural networks used in machine learning, provides a unique pathway for acceleration. The goal of this paper is to recast Arrhenius kinetics as a neural network using matrix-based formulations. Unlike ANNs that rely on data, this formulation does not require training and exactly represents the chemistry mechanism. More specifically, connections between the exact matrix equations for kinetics and traditional artificial neural network layers are used to enable the usage of GPU-optimized linear algebra libraries without the need for modeling. Regarding GPU performance, speedup and saturation behaviors are assessed for several chemical mechanisms of varying complexity. The performance analysis is based on trends for absolute compute times and throughput for the various arithmetic operations encountered during the source term computation. The goals are ultimately to provide insights into how the source term calculations scale with the reaction mechanism complexity, which types of reactions benefit the GPU formulations most, and how to exploit the matrix-based formulations to provide optimal speedup for large mechanisms by using sparsity properties. Overall, the GPU performance for the species source term evaluations reveals many informative trends with regards to the effect of cell number on device saturation and speedup. Most importantly, it is shown that the matrix-based method enables highly efficient GPU performance across the board, achieving near-peak performance in saturated regimes.

Novel Application to Recognize a Breakdown Pressure Event on Time Series Frac Data Vs. an Artificial Intelligence Approach

2021 ◽  
Author(s):  
Alberto Jose Ramirez ◽  
Jessica Graciela Iriarte
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Service Providers ◽  
Complex Model ◽  
Rate Of Change ◽  
Computational Time ◽  
Breakdown Pressure ◽  
Constant Multiple ◽  
Artificial Neural ◽  
Rule Based Approach

Abstract Breakdown pressure is the peak pressure attained when fluid is injected into a borehole until fracturing occurs. Hydraulic fracturing operations are conducted above the breakdown pressure, at which the rock formation fractures and allows fluids to flow inside. This value is essential to obtain formation stress measurements. The objective of this study is to automate the selection of breakdown pressure flags on time series fracture data using a novel algorithm in lieu of an artificial neural network. This study is based on high-frequency treatment data collected from a cloud-based software. The comma separated (.csv) files include treating pressure (TP), slurry rate (SR), and bottomhole proppant concentration (BHPC) with defined start and end time flags. Using feature engineering, the model calculates the rate of change of treating pressure (dtp_1st) slurry rate (dsr_1st), and bottomhole proppant concentration (dbhpc_1st). An algorithm isolates the initial area of the treatment plot before proppant reaches the perforations, the slurry rate is constant, and the pressure increases. The first approach uses a neural network trained with 872 stages to isolate the breakdown pressure area. The expert rule-based approach finds the highest pressure spikes where SR is constant. Then, a refining function finds the maximum treating pressure value and returns its job time as the predicted breakdown pressure flag. Due to the complexity of unconventional reservoirs, the treatment plots may show pressure changes while the slurry rate is constant multiple times during the same stage. The diverse behavior of the breakdown pressure inhibits an artificial neural network's ability to find one "consistent pattern" across the stage. The multiple patterns found through the stage makes it difficult to select an area to find the breakdown pressure value. Testing this complex model worked moderately well, but it made the computational time too high for deployment. On the other hand, the automation algorithm uses rules to find the breakdown pressure value with its location within the stage. The breakdown flag model was validated with 102 stages and tested with 775 stages, returning the location and values corresponding to the highest pressure point. Results show that 86% of the predicted breakdown pressures are within 65 psi of manually picked values. Breakdown pressure recognition automation is important because it saves time and allows engineers to focus on analytical tasks instead of repetitive data-structuring tasks. Automating this process brings consistency to the data across service providers and basins. In some cases, due to its ability to zoom-in, the algorithm recognized breakdown pressures with higher accuracy than subject matter experts. Comparing the results from two different approaches allowed us to conclude that similar or better results with lower running times can be achieved without using complex algorithms.

Power Forecasting from Solar Panels Using Artificial Neural Network in UTHM Parit Raja

2021 ◽  
Vol 02 (01) ◽  
Author(s):  
Natasha Munirah Mohd Fahmi ◽  
◽  
Nor Aira Zambri ◽  
Norhafiz Salim ◽  
Sim Sy Yi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Climatic Factors ◽  
Activation Function ◽  
Solar Panels ◽  
Ann Model ◽  
Power Characteristics ◽  
Step Procedure ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper presents a step-by-step procedure for the simulation of photovoltaic modules with numerical values, using MALTAB/Simulink software. The proposed model is developed based on the mathematical model of PV module, which based on PV solar cell employing one-diode equivalent circuit. The output current and power characteristics curves highly depend on some climatic factors such as radiation and temperature, are obtained by simulation of the selected module. The collected data are used in developing Artificial Neural Network (ANN) model. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) are the techniques used to forecast the outputs of the PV. Various types of activation function will be applied such as Linear, Logistic Sigmoid, Hyperbolic Tangent Sigmoid and Gaussian. The simulation results show that the Logistic Sigmoid is the best technique which produce minimal root mean square error for the system.

scholarly journals Prediction of Sensory Parameters of Cured Ham: A Study of the Viability of the Use of NIR Spectroscopy and Artificial Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5624
Author(s):  
Pedro Hernández-Ramos ◽  
Ana María Vivar-Quintana ◽  
Isabel Revilla ◽  
María Inmaculada González-Martín ◽  
Miriam Hernández-Jiménez ◽  
...  
Keyword(s):  
Neural Network ◽  
Nir Spectroscopy ◽  
Sensory Profile ◽  
Training Algorithm ◽  
Ann Model ◽  
Fiber Optic Probe ◽  
Organoleptic Characteristics ◽  
Dry Cured Ham ◽  
Artificial Neural ◽  
Sensory Parameters

Dry-cured ham is a high-quality product owing to its organoleptic characteristics. Sensory analysis is an essential part of assessing its quality. However, sensory assessment is a laborious process which implies the availability of a trained tasting panel. The aim of this study was the prediction of dry-ham sensory characteristics by means of an instrumental technique. To do so, an artificial neural network (ANN) model for the prediction of sensory parameters of dry-cured hams based on NIR spectral information was developed and optimized. The NIR spectra were obtained with a fiber-optic probe applied directly to the ham sample. In order to achieve this objective, the neural network was designed using 28 sensory parameters analyzed by a trained panel for sensory profile analysis as output data. A total of 91 samples of dry-cured ham matured for 24 months were analyzed. The hams corresponded to two different breeds (Iberian and Iberian x Duroc) and two different feeding systems (feeding outdoors with acorns or feeding with concentrates). The training algorithm and ANN architecture (the number of neurons in the hidden layer) used for the training were optimized. The parameters of ANN architecture analyzed have been shown to have an effect on the prediction capacity of the network. The Levenberg–Marquardt training algorithm has been shown to be the most suitable for the application of an ANN to sensory parameters

Daily Surface Solar Radiation Prediction Mapping Using Artificial Neural Network: The Case Study of Reunion Island

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
Peng Li ◽  
Miloud Bessafi ◽  
Beatrice Morel ◽  
Jean-Pierre Chabriat ◽  
Mathieu Delsaut ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Solar Radiation ◽  
Computational Time ◽  
Reunion Island ◽  
Surface Solar Radiation ◽  
Clear Sky ◽  
Réunion Island ◽  
Artificial Neural

Abstract This paper focuses on the prediction of daily surface solar radiation maps for Reunion Island by a hybrid approach that combines principal component analysis (PCA), wavelet transform analysis, and artificial neural network (ANN). The daily surface solar radiation over 18 years (1999–2016) from CM SAF (SARAH-E with 0.05 deg × 0.05 deg spatial resolution) is first detrended using the clear sky index. Dimensionality reduction of the detrended dataset is secondly performed through PCA, which results in saving computational time by a factor of eight in comparison to not using PCA. A wavelet transform is thirdly applied onto each of the first 28 principal components (PCs) explaining 95% of the variance. The decomposed nine-wavelet components for each PC are fourthly used as input to an ANN model to perform the prediction of day-ahead surface solar radiation. The predicted decomposed components are finally returned to PCs and clear sky indices, irradiation in the end for re-mapping the surface solar radiation's distribution. It is found that the prediction accuracy is quite satisfying: root mean square error (RMSE) is 30.98 W/m2 and the (1 − RMSE_prediction/RMSE_persistence) is 0.409.

scholarly journals Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hung Vo Thanh ◽  
Yuichi Sugai ◽  
Kyuro Sasaki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oil Recovery ◽  
Early Stage ◽  
Computational Time ◽  
Residual Oil ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
And Storage

Abstract Residual Oil Zones (ROZs) become potential formations for Carbon Capture, Utilization, and Storage (CCUS). Although the growing attention in ROZs, there is a lack of studies to propose the fast tool for evaluating the performance of a CO2 injection process. In this paper, we introduce the application of artificial neural network (ANN) for predicting the oil recovery and CO2 storage capacity in ROZs. The uncertainties parameters, including the geological factors and well operations, were used for generating the training database. Then, a total of 351 numerical samples were simulated and created the Cumulative oil production, Cumulative CO2 storage, and Cumulative CO2 retained. The results indicated that the developed ANN model had an excellent prediction performance with a high correlation coefficient (R2) was over 0.98 on comparing with objective values, and the total root mean square error of less than 2%. Also, the accuracy and stability of ANN models were validated for five real ROZs in the Permian Basin. The predictive results were an excellent agreement between ANN predictions and field report data. These results indicated that the ANN model could predict the CO2 storage and oil recovery with high accuracy, and it can be applied as a robust tool to determine the feasibility in the early stage of CCUS in ROZs. Finally, the prospective application of the developed ANN model was assessed by optimization CO2-EOR and storage projects. The developed ANN models reduced the computational time for the optimization process in ROZs.

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