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The present research focuses on the variation of field pea production under different prevailing weather parameters, aiming to develop a reliable forecasting model. For that a field experiment was conducted in New Alluvial Zone of West Bengal during 2018-19 and 2019-20 with three different varieties (VL42, Indrira Matar, Rachana) of this region. Biomass predicting equation based on maximum temperature, minimum temperature and solar radiation was developed to estimate field pea yield for 2040-2099 period under SSP 2-4.5 and SSP 5-8.5 scenarios. It reveals that solar radiation positively influences crop biomass, while high maximum and minimum temperatures have adverse effects on yield. The developed forecasting equation demonstrated its accuracy (nRMSE=17.37%) by aligning closely with historical data, showcasing its potential for reliable predictions. Furthermore, the study delves into future climate scenarios, showing that increasing temperatures are likely to impact field pea yield negatively. Both biomass and yield showed decreasing trend for the years from 2040 to 2099. SSP 5-8.5 scenario, which is more pessimistic one, foresees a substantial reduction in crop productivity. This weather parameter-based biomass prediction equation can be effectively utilized as a method to assess the impact of climate change on agriculture. Keywords: Field pea, weather parameters, crop yield prediction, New Alluvial Zone, nRMSE

The Statistical Errors Raster Toolbox includes models of the most popular error metrics in the interdisciplinary literature, namely, root mean square error (RMSE), normalized root mean square error (NRMSE), mean bias error (MBE), normalized mean bias error (NMBE), mean absolute error (MAE) and normalized mean absolute error (NMAE), for computing the areal errors of any raster file in .tiff format as compared with a reference raster file. The models are applicable to any size of raster files, no matter if no-data pixels are included. The only prerequisites are that the two raster files share the same units, cell size, and projection system. The novelty lies in the fact that, to date, there is no such application in ArcGIS Pro 3/ArcMap 10.8. Therefore, users who work with raster files require external software, plus the relevant expertise. An application on the reference evapotranspiration (ETo) of Peloponnese peninsula (Greece) is presented. MODIS ET products and ETo raster files for empirical methods are employed. The results of the models (for 20,440 valid values) are compared to the results of external software (for 1000 random points). Considering that the different sample sizes can lead to different accuracies and the inhomogeneity of the area, it is obvious that the results are almost identical.

Mitigating climate change is increasingly seen as a pressing global concern. The current climate change and global warming issues are leading to a growing demand for information and data on carbon. Forests are one of the biggest carbon sinks on the planet. Forests play a role in lowering the levels of CO2 in the air. Teak plantation forests are forests with teak trees that have a relatively long harvesting period (long rotation) so that the possibility of carbon components absorbed from the atmosphere will be stored large enough in plant tissue, teak plantation forests have ecological potential as carbon storage for a long time. Teak is a type of tree that grows quickly and has a large amount of biomass. Even though it is widely recognized that teak forests have the potential to store carbon, there is still limited information available on their carbon storage potential across different locations and environmental conditions. This study aims to calculate the average carbon stock and identify the most suitable allometric model for teak community forests in Sungai Siring Village. The study found that the mean carbon per hectare was 0.114 kg/ha, with the effective model utilizing the equation model B = 0.0143 (D) 2.4686 using one independent variable. Where B (above-ground biomass) and D (diameter at the breast height). The equation has an RMSE value of 0.391 and an NRMSE value of 0.088, it has an accurate category because less than 10%.

Software failure prediction is a crucial task in software quality assurance. Although time series forecasting techniques and conventional software reliability models are used for the prediction of software reliability, very often these models fail to provide accurate predictions. Therefore, an accurate model for software reliability prediction is imperative. The objective of this paper is to introduce a hybrid model that merges a recurrent adaptive neuro-fuzzy inference system (RANFIS) and modified differential evolution (MDE) for software reliability prediction. The model employs inner spatial feedback loops and delayed output feedback to improve the conventional neuro-fuzzy system's prediction abilities while dealing with time series software failure data. The hybrid model is trained by MDE, where a new scheme of crossover and mutation has been proposed. We conduct an extensive simulation on a few publicly available benchmark datasets for computing the predicting ability of our hybrid model. Simulation results along with statistical analysis illustrate that our hybrid model predicts more precisely the time between successive failures in software and outperforms other traditional models.

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long‐term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C‐cycle processes. Bayesian calibration was conducted using quality‐controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass‐shrub mixture, and grass‐tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R2 > 0.6, RMSE <390 g C m−2) relative to net ecosystem exchange of CO2 (NEE) (R2 > 0.4, RMSE <180 g C m−2). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long‐term network‐based monitoring of vegetation biomass, C fluxes, and SOC stocks. Plain Language Summary Rangelands play a crucial role in providing various ecosystem services, including potential climate change mitigation through increased soil organic carbon (SOC) storage. Accurate estimates of changes in carbon (C) storage are challenging due to the heterogeneous nature of rangelands and the limited availability of field observations. In this work, we leveraged remote sensing observations, tower‐based C flux measurements from over 60 rangeland sites in the Western and Midwestern US, and other environmental data sets to build the process‐based Rangeland Carbon Tracking and Management (RCTM) modeling system. The RCTM system is designed to simulate the past 20 years of rangeland C dynamics and is regionally calibrated. The RCTM system performs well in estimating spatial and temporal rangeland C fluxes as well as spatial SOC storage. Model simulation results revealed increased SOC storage and rangeland productivity driven by annual precipitation patterns. The RCTM system developed by this work can be used to generate accurate spatial and temporal estimates of SOC storage and C fluxes at fine spatial (30 m) and temporal (every 5 days) resolutions, and is well‐suited for informing rangeland C management strategies and improving broad‐scale policy making. Key Points The Rangeland Carbon Tracking and Monitoring System was calibrated to simulate vegetation type‐specific rangeland C dynamics Regional variability in carbon fluxes and soil organic carbon is well represented by a remote sensing‐driven process modeling approach Soil organic carbon stocks in Western and Midwestern US rangelands increased over the past 20 years due to increased precipitation

Financial forecasting using news articles is an emerging field. In this paper, we proposed hybrid intelligent models for stock market prediction using the psycholinguistic variables (LIWC and TAALES) extracted from news articles as predictor variables. For prediction purpose, we employed various intelligent techniques such as Multilayer Perceptron, Group Method of Data Handling (GMDH), General Regression Neural Network (GRNN), Random Forest, Quantile Regression Random Forest, Classification and regression tree and Support Vector Regression. We experimented on the data of 12 companies’ stocks, which are listed in Bombay Stock Exchange. We employed Chi squared and maximum relevance and minimum redundancy feature selection techniques on the psycho-linguistic features obtained from the news articles etc. After extensive experimentation, using Diebold-Mariano test, we conclude that GMDH and GRNN are statistically the best techniques in that order with respect to the MAPE and NRMSE values.

This study evaluated 20 general circulation models (GCMs) of the Coupled Model Intercomparison Project, Phase 5 (CMIP5), which provide the prediction results for the period of 2006 to 2014, the period from which the observation data (the Global Precipitation Climatology Project (GPCP) data) are available. Both the GCM predictions of precipitation and the GPCP data were compared for three data structures—the global, zonal, and grid mean—with conventional statistics like the root mean square error (RMSE) and the pattern correlation coefficient of the cyclostationary empirical orthogonal functions (CSEOFs). As a result, it was possible to select a GCM which showed the best performance among the 20 GCMs considered in this study. Overall, the NorSM1-M model was found to be the most similar to the GPCP data. Additionally, the IPSL-CM5A-LR, BCC-CSM, and GFDL-CMS models were also found to be quite similar to the GPCP data.

Missing values in microarray data can significantly affect subsequent analysis, thus it is important to estimate these missing values accurately. In this paper, a sequential local least squares imputation (SLLSimpute) method is proposed to solve this problem. It estimates missing values sequentially from the gene containing the fewest missing values and partially utilizes these estimated values. In addition, an automatic parameter selection algorithm, which can generate an appropriate number of neighboring genes for each target gene, is presented for parameter estimation. Experimental results confirmed that SLLSimpute method exhibited better estimation ability compared with other currently used imputation methods.

Magnetic resonance images (MRI) require intensity standardisation if they are used for the purpose of quantitative analysis as inherent variations in image intensity levels between different image sets are manifest due to technical factors. One approach is to standardise the image intensity values using a statistically applied biological reference tissue. The aim of this study is to compare the performance of differing candidate biological reference tissues for standardising T2WI intensity distributions. Fifty-one prostate cancer patients across two centres with different scanners were evaluated using the percentage interpatient coefficient of variation (%interCV) for four different biological references; femoral bone marrow, ischioanal fossa, obturator-internus muscle and bladder urine. The tissue with the highest reproducibility (lowest %interCV) in both centres was used for intensity standardisation of prostate T2WI using three different statistical measures (mean, Z-score, median + Interquartile Range). The performance of different standardisation methods was evaluated from the assessment of image intensity histograms and the percentage normalised root mean square error (%NRSME) of the healthy peripheral zone tissue. Ischioanal fossa as a reference tissue demonstrated the highest reproducibility with %interCV of 18.9 for centre1 and 11.2 for centre2. Using ischioanal fossa for statistical intensity standardisation and the median + Interquartile Range method demonstrated the lowest %NRMSE across centres for healthy peripheral zone tissues. This study demonstrates ischioanal fossa as a preferred reference tissue for standardising intensity values from T2WI of the prostate. Subsequent image standardisation using the median + Interquartile Range intensity of the reference tissue demonstrated a robust and reliable standardisation method for quantitative image assessment.

This chapter contains sections titled: Time Series Approach in ANN ANN Model ANN- PSO Model Results and Discussion