Pendugaan Emisi CO2 dari Lahan Gambut dengan Menggunakan Model Artificial Neural Network (ANN)

Abstract Peatlands are the most efficient carbon sinks in large volumes. Peatland clearance makes CO2 emissions released into the air. a reference of Carbon emission had a great value compared with the results of carbon emissions measurement conducted by Indonesian researchers and academics. This study aims to Conduct a continuous estimation of CO2 emissions from peatlands over a long period of time, analyze the influence of the biophysical environment on CO2 emissions and obtain CO2 emissions based on measurements of biophysical environmental parameters using ANN model. The CO2 emissions measurements were performed by closed chamber method using Licor LI-8100 for 60 days. Biophysical environmental parameter measurements are also installed simultaneously. Biophysical environmental parameters measured include soil temperature, soil moisture and water table depth. The results showed that CO2 emissions reached 59.82 TonCO2 / ha / year with carbon emissions of 16.314 TonC / ha / year. Peatland CO2 emissions are influenced by environmental parameters of peat biophysics. Calculations using the ANN model obtained the highest correlation of R2 = 0.5545 which shows that the calculation of ANN model with measurement Emission has a high enough correlation and can be used as a reference to estimated peat CO2 peatland in Padang Island. Abstrak Lahan gambut merupakan penyimpan karbon yang paling efisien dalam jumlah besar. Pembukaan lahan gambut mengakibatkan emisi CO2 terlepas ke udara. Data emisi karbon yang menjadi rujukan memiliki nilai yang lebih besar daripada hasil pengukuran emisi karbon yang dilakukan oleh peneliti dan akademisi Indonesia. Penelitian ini bertujuan untuk Melakukan estimasi emisi CO2 dari lahan gambut secara kontinyu dalam periode waktu panjang, Menganalisis pengaruh lingkungan biofisik terhadap emisi CO2 dari lahan gambut dan Mendapatkan dugaan emisi CO2 dari lahan gambut berdasarkan hasil pengukuran parameter lingkungan biofisik dengan menggunakan model ANN. Pengukuran emisi karbon dilakukan dengan metode closed chamber menggunakan Licor LI-8100 selama 60 hari. Pengukuran parameter lingkungan biofisik juga diinstal secara bersamaan dengan pengukuran emisi CO2. Parameter lingkungan biofisik yang diukur meliputi temperatur tanah, kelembaban tanah, kedalaman water table. Hasil penelitian menunjukkan bahwa emisi CO2 lahan gambut mencapai 59.82 TonCO2/ha/tahun dengan emisi karbon adalah 16.314 TonC/ha/tahun . Emisi CO2 dipengaruhi oleh parameter lingkungan biofisik gambut yaitu suhu tanah, kelembaban tanah dan kedalaman water table. Perhitungan menggunakan model ANN diperoleh korelasi tertinggi sebesar R2 = 0.5545 yang menunjukkan bahwa hasil perhitungan model ANN dengan Emisi pengukuran memiliki korelasi yang cukup tinggi dan bisa dijadikan sebagai acuan dalam mengestimasi CO2 lahan gambut Pulau Padang.

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Enhancing water levels of degraded, bare, tropical peatland in West Kalimantan, Indonesia: Impacts on CO2 emission from soil respiration

Biodiversitas Journal of Biological Diversity ◽

10.13057/biodiv/d190221 ◽

2018 ◽

Vol 19 (2) ◽

pp. 472-477

Author(s):

DWI ASTIANI ◽

BURHANUDDIN BURHANUDDIN ◽

EVI GUSMAYANTI ◽

TRI WIDIASTUTI ◽

MUHAMMAD J. TAHERZADEH

Keyword(s):

Soil Respiration ◽

Co2 Emissions ◽

Water Table ◽

Carbon Emissions ◽

Co2 Emission ◽

Water Levels ◽

Small Scale ◽

West Kalimantan ◽

Tropical Peatland ◽

Water Tables

Astiani D, Burhanuddin, Gusmayanti E, Widiastuti T, Taherzadeh MJ. 2018. Enhancing water levels of degraded, bare, tropical peatland in West Kalimantan, Indonesia: Impacts on CO2 emission from soil respiration. Biodiversitas 19: 472-477. The major drivers of deforestation in West Kalimantan have been the development for large or small-scale expansion of agricultural activities; the establishment of oil palm and other plantations; fire; and degradation of forests particularly from industrial logging. Our previous research findings have shown that such activities in affected peatland areas have lowered the water table levels (down to 0.5-1.0 m depths), and have significantly increased CO2 emissions from the peat soils. It has been demonstrated that unmanaged, lowered water tables in peatlands act as one of the main factors inflating soil carbon emissions - an issue that has assumed global significance in recent decades. Regulating peatland water tables has the potential to mitigate degraded peatland carbon emissions as well as improve the hydrological functions for communities who farm the peatlands. However, we are still uncertain exactly how much impact controlled raising of the peatlands water tables will have on reducing soil CO2 emissions. The research described here aimed to mitigate CO2 emissions by raising and regulating water levels on drained peatland to restore and enhance its hydrological functions. The results confirmed that raising the water table significantly decreases CO2 emissions and improves water availability and management for crop production in the coastal peatland of Kubu Raya district, West Kalimantan. Water levels previously at 60cm below the soil surface were regulated to raise the watertable up to just 30 cm below the surface and this reduced peatland carbon emissions by about 49%. However, longer-term monitoring is required to ensure that the hydrological benefits and CO2 mitigation can be sustained.

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Comparison of Groundwater Level Models Based on Artificial Neural Networks and ANFIS

The Scientific World JOURNAL ◽

10.1155/2015/742138 ◽

2015 ◽

Vol 2015 ◽

pp. 1-13 ◽

Cited By ~ 11

Author(s):

Nevenka Djurovic ◽

Milka Domazet ◽

Ruzica Stricevic ◽

Vesna Pocuca ◽

Velibor Spalevic ◽

...

Keyword(s):

Water Table ◽

Groundwater Resources ◽

Danube River ◽

Left Bank ◽

Ann Model ◽

Inference System ◽

Soft Computing Techniques ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Adaptive Neurofuzzy Inference System

Water table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS) and an artificial neural network (ANN) model for one-month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high-quality models.

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Questions to the Article: An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples (Preprint)

10.2196/preprints.26774 ◽

2020 ◽

Author(s):

Shu-Chun Kuo ◽

CHIEN WEI ◽

Willy Chou

Keyword(s):

Data Entry ◽

Ann Model ◽

Model Accuracy ◽

Data Format ◽

Raw Data ◽

Probability Of Survival ◽

Testing Data ◽

Laboratory Results ◽

Data Yield ◽

Artificial Neural Network Ann

UNSTRUCTURED The recent article published on December 23 27 in 2020 is well-written and of interest, but remains several questions that are required for clarifications, including (1) 30 feature variables with normalized format(mean=0 and SD=1) required to compare model accuracy with those with the raw-data format; (2)inconsistency in variable numbers between entry and preview panels in Figure 4 and reference typos; and (3) data-entry format with raw blood laboratory results in Figure 4 inconsistent with the model designed using normalized data to estimate parameters. We conducted a study using the training and testing data provided by the previous study. An artificial neural network(ANN) model was performed to estimate parameters and compare the model accuracy with those eight models provided by the previous study. We found that (1) normalized data yield higher accuracy than that with the raw data; (2) typos definitely exist at the bottom review (=32>30 variables in the entry) panels in Figure 4 and typos in Table 6; and (3)the ANN earns a probability of survival(=0.91) higher than that(=0.71) in the previous study using the similar entry data when the raw data are assumed in the app. We also demonstrated an author-made app using the visualization to display the prediction result, which is novel and innovative to make the result improved with a dashboard in comparison with the previous study.

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Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18105110 ◽

2021 ◽

Vol 18 (10) ◽

pp. 5110

Author(s):

Shu-Farn Tey ◽

Chung-Feng Liu ◽

Tsair-Wei Chien ◽

Chin-Wei Hsu ◽

Kun-Chen Chan ◽

...

Keyword(s):

Neural Network ◽

Early Stage ◽

Characteristic Curve ◽

Patient Discharge ◽

Ann Model ◽

Healthcare Settings ◽

High Area ◽

Icd 10 ◽

Artificial Neural ◽

Artificial Neural Network Ann

Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

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Modeling Fused Filament Fabrication using Artificial Neural Networks

Production Engineering ◽

10.1007/s11740-021-01020-y ◽

2021 ◽

Author(s):

Paul Oehlmann ◽

Paul Osswald ◽

Juan Camilo Blanco ◽

Martin Friedrich ◽

Dominik Rietzel ◽

...

Keyword(s):

Real Time ◽

Large Scale ◽

Cost Effective ◽

Print Quality ◽

Ann Model ◽

Production Environment ◽

Fused Filament Fabrication ◽

Artificial Neural ◽

Using Data ◽

Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

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Economic Development, CO2 Emissions and Energy Use Nexus-Evidence from the Danube Region Countries

Energies ◽

10.3390/en14113165 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3165

Author(s):

Eva Litavcová ◽

Jana Chovancová

Keyword(s):

Economic Growth ◽

Energy Consumption ◽

Co2 Emissions ◽

Carbon Emissions ◽

Bounds Testing ◽

Mutual Cooperation ◽

Long Run ◽

Short Run ◽

Danube Region ◽

The Impact

The aim of this study is to examine the empirical cointegration, long-run and short-run dynamics and causal relationships between carbon emissions, energy consumption and economic growth in 14 Danube region countries over the period of 1990–2019. The autoregressive distributed lag (ARDL) bounds testing methodology was applied for each of the examined variables as a dependent variable. Limited by the length of the time series, we excluded two countries from the analysis and obtained valid results for the others for 26 of 36 ARDL models. The ARDL bounds reliably confirmed long-run cointegration between carbon emissions, energy consumption and economic growth in Austria, Czechia, Slovakia, and Slovenia. Economic growth and energy consumption have a significant impact on carbon emissions in the long-run in all of these four countries; in the short-run, the impact of economic growth is significant in Austria. Likewise, when examining cointegration between energy consumption, carbon emissions, and economic growth in the short-run, a significant contribution of CO2 emissions on energy consumptions for seven countries was found as a result of nine valid models. The results contribute to the information base essential for making responsible and informed decisions by policymakers and other stakeholders in individual countries. Moreover, they can serve as a platform for mutual cooperation and cohesion among countries in this region.

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A comparative study and combined application of RSM and ANN in adsorptive removal of diuron using biomass ashes

International Journal of Chemical Reactor Engineering ◽

10.1515/ijcre-2020-0227 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Sunil K. Deokar ◽

Nachiket A. Gokhale ◽

Sachin A. Mandavgane

Keyword(s):

Three Dimensional ◽

Current Approach ◽

Coefficient Of Determination ◽

Ann Model ◽

Operational Parameters ◽

Initial Concentration ◽

Combined Application ◽

Artificial Neural Network Ann ◽

Relationship Of ◽

Biomass Ashes

Abstract Biomass ashes like rice husk ash (RHA), bagasse fly ash (BFA), were used for aqueous phase removal of a pesticide, diuron. Response surface methodology (RSM) and artificial neural network (ANN) were successfully applied to estimate and optimize the conditions for the maximum diuron adsorption using biomass ashes. The effect of operational parameters such as initial concentration (10–30 mg/L); contact time (0.93–16.07 h) and adsorbent dosage (20–308 mg) on adsorption were studied using central composite design (CCD) matrix. Same design was also employed to gain a training set for ANN. The maximum diuron removal of 88.95 and 99.78% was obtained at initial concentration of 15 mg/L, time of 12 h, RHA dosage of 250 mg and at initial concentration of 14 mg/L, time of 13 h, BFA dosage of 60 mg respectively. Estimation of coefficient of determination (R 2) and mean errors obtained for ANN and RSM (R 2 RHA = 0.976, R 2 BFA = 0.943) proved ANN (R 2 RHA = 0.997, R 2 BFA = 0.982) fits better. By employing RSM coupled with ANN model, the qualitative and quantitative activity relationship of experimental data was visualized in three dimensional spaces. The current approach will be instrumental in providing quick preliminary estimations in process and product development.

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User Automotive Powertrain-Type Choice Model and Analysis Using Neural Networks

Sustainability ◽

10.3390/su13020585 ◽

2021 ◽

Vol 13 (2) ◽

pp. 585

Author(s):

Fabio Luis Marques dos Santos ◽

Paolo Tecchio ◽

Fulvio Ardente ◽

Ferenc Pekár

Keyword(s):

Internal Combustion Engine ◽

User Behavior ◽

Choice Model ◽

Combustion Engine ◽

Stated Preference ◽

Operating Cost ◽

Internal Combustion ◽

Ann Model ◽

Purchase Cost ◽

Artificial Neural Network Ann

This paper presents an artificial neural network (ANN) model that simulates user’s choice of electric or internal combustion engine automotive vehicles based on basic vehicle attributes (purchase price, range, operating cost, taxes due to emissions, time to refuel/recharge and vehicle price depreciation), with the objective of analyzing user behavior and creating a model that can be used to support policymaking. The ANN was trained using stated preference data from a survey carried out in six European countries, taking into account petrol, diesel and battery electric automotive vehicle attributes. Model results show that the electric vehicle parameters (especially purchase cost, range and recharge times), as well as the purchase cost of internal combustion engine vehicles, have the most influence on consumers’ vehicle choices. A graphical interface was created for the model, to make it easier to understand the interactions between different attributes and their impacts on consumer choices and thus help policy decisions.

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Modeling of lime production process using artificial neural network

Chemical Product and Process Modeling ◽

10.1515/cppm-2021-0032 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Abolghasem Daeichian ◽

Rana Shahramfar ◽

Elham Heidari

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Production Process ◽

High Energy ◽

Ann Model ◽

Artificial Neural ◽

Rotary Kilns ◽

Input Variables ◽

Artificial Neural Network Ann ◽

Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

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