Maximum R2 Research Articles

Generating Fine-Scale Aerosol Data through Downscaling with an Artificial Neural Network Enhanced with Transfer Learning

Spatially and temporally resolved aerosol data are essential for conducting air quality studies and assessing the health effects associated with exposure to air pollution. As these data are often expensive to acquire and time consuming to estimate, computationally efficient methods are desirable. When coarse-scale data or imagery are available, fine-scale data can be generated through downscaling methods. We developed an Artificial Neural Network Sequential Downscaling Method (ASDM) with Transfer Learning Enhancement (ASDMTE) to translate time-series data from coarse- to fine-scale while maintaining between-scale empirical associations as well as inherent within-scale correlations. Using assimilated aerosol optical depth (AOD) from the GEOS-5 Nature Run (G5NR) (2 years, daily, 7 km resolution) and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) (20 years, daily, 50 km resolution), coupled with elevation (1 km resolution), we demonstrate the downscaling capability of ASDM and ASDMTE and compare their performances against a deep learning downscaling method, Super Resolution Deep Residual Network (SRDRN), and a traditional statistical downscaling framework called dissever ASDM/ASDMTE utilizes empirical between-scale associations, and accounts for within-scale temporal associations in the fine-scale data. In addition, within-scale temporal associations in the coarse-scale data are integrated into the ASDMTE model through the use of transfer learning to enhance downscaling performance. These features enable ASDM/ASDMTE to be trained on short periods of data yet achieve a good downscaling performance on a longer time-series. Among all the test sets, ASDM and ASDMTE had mean maximum image-wise R2 of 0.735 and 0.758, respectively, while SRDRN, dissever GAM and dissever LM had mean maximum image-wise R2 of 0.313, 0.106 and 0.095, respectively.

Effectively Increasing Pt Utilization Efficiency of the Membrane Electrode Assembly in Proton Exchange Membrane Fuel Cells through Multiparameter Optimization Guided by Machine Learning.

Although proton exchange membrane fuel cells have received attention, the high costs associated with Pt-based catalysts in membrane electrode assemblies (MEAs) remain a huge obstacle for large-scale applications. To solve this urgent problem, the utilization efficiency of Pt in MEAs must be increased. Facing numerous interacting parameters in an attempt to keep experimental costs as low as possible, we innovatively introduce machine learning (ML) to achieve this goal. Nine different ML algorithms are trained on the experimental dataset from our laboratory to precisely predict the performance and Pt utilization (maximum R2 = 0.973/0.968). To determine the best synthesis conditions, black-box interpretation methods are applied to provide reliable insights from both qualitative and quantitative perspectives. The optimized choices of ionomer/catalyst ratio, water content, organic solvent, catalyst loading, stirring method, solid content, and ultrasonic spraying flow rate are properly made with few experimental attempts under ML results' guidance. Promising Pt utilization of 0.147 gPt kW-1 and a power density of 1.02 W cm-2 are achieved at 0.6 V in a single cell (H2/air) at an ultralow total loading of 0.15 mg Pt cm-2. Therefore, this work contributes to the economy of hydrogen energy by paving the way for MEA optimization with complex parameters by ML.

Development of yield forecast models with raw data through stepwise regression analysis (forward method) and discriminant function analysis for pigeon pea crop on Chhattisgarh plain zone

Under this investigation we studied about minimization of weekly data through stratification and Simple Randon Sample technique. Individual effect of weather parameters has been considered under the study. Forecasting model developed on two statistical technique i.e. Stepwise Regression Analysis (Forward Method) technique and Discriminat Function Analysis technique. Both the techniques are found very suitable for the district wise and zone level pre-harvest forecast for the pigeonpea crop on Chhattisgarh. Models fitted with the Stepwise Regression Analysis on 35 variables and Time Trend (T) for Zone. The developed model is showing best fit on the basis of very high value of significance and maximum R2 (84%) for the plain Zone, highly significant at 0.1% level of significance and models fitted with DFA then 2 Discriminant score (ds1 and ds2) and Time Trend (T) the models found highly significant and R2 value 69% for plain zone for Pigeonpea crop. The developed model has been validated by the error parameters viz. Minimum MAE, Minimum MSE, Minimum RMSE, Minimum PE and Minimum PD along with maximum R2. These developed models are useful to farmers of Chhattisgarh to decide their future prospects and possible course of action in advance. This was very challenging task for the researchers to develop a precise & accurate and best fit model for the future forecast. May this study will clear the fog through new methodology and also will create the new way on the direction of forecast.

Prediction of growth performance of malabari goats using non-linear models

Different non-linear growth models were fitted to predict the best growth model for Malabari goat breed. Data on body weight of 1064 goats from birth to 12 months of age were collected between 2016 and 2018 from field centres of Indian Council of Agricultural Research – All India Coordinated Research Project on Goat Improvement on Malabari, Kerala Veterinary and Animal Sciences University, Kerala. The data of body weights were plotted against age and shape of the curve was examined. Based on shape of the curves, five non-linear growth models viz., Brody model, von Bertalanffy model, Logistic model, Gompertz model and Negative exponential model were fitted to the data. The Gompertz model was the best fit model for Malabari goats with maximum R2 (0.999) and least residual mean square error (0.210), mean absolute error (0.105) and mean absolute percentage error (0.016) followed by Logistic model. Thus, the Gompertz model could be considered as the best fit to describe the growth performance of Malabari goats.

Cloud based Forecast of Municipal Solid Waste Growth using AutoRegressive Integrated Moving Average Model: A Case Study for Bengaluru

Forecasting the quantity of waste growth in upcoming years is very much required for assessing the existing waste management system. In this research work, time series forecast model, ARIMA (Autoregressive Integrated Moving Average), is used to predict future waste growth from 2021 to 2028 for Bengaluru, largest city in Karnataka. Eight years old historical solid waste dataset from 2012 to 2020 is used to make predictions. This dataset is preprocessed and only time bounded variables like days, month, year and waste quantity in tons are used in this research work to obtain accurate prediction. The model is implemented in python in Google Colab free cloud’s Jupyter notebook. As ARIMA is time bounded, forecast made by the model is accurate and performance of the model is evaluated using metrics such as Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Coefficient of Determination (R2). Outcomes revealed that ARIMA (0, 1, 2) model with the lowermost RMSE (753.5742), MAD (577.4601), and MAPE (11.6484) values and the maximum R2 (0.9788) value has a greater forecast performance. The outcomes attained from the model also showed that the total volume of yearly solid waste to be produced will rise from about 50,300 tons in 2021 to 75,600 tons in 2028.

Correlates of COVID-19 mortality: A descriptive study

Background and Objectives: The enigma COVID-19 pandemic already involved major parts of the globe with a toll of 3,175,207 victims and 224,172 deaths from 215 countries/territories as on May 1, 2020. It cripples nations by the loss of human resources, economic decline, hunger, unemployment insecurities giving way to mental morbidities, and still many others to be discovered. A systematic search about correlates of its killing attribute is urgently warranted. Materials and Methods: A cross-sectional survey for 3 weeks (03/5/2020–23/5/2020) was conducted in a teaching institution at Kolkata aiming to describe the magnitude and correlates of COVID-19 mortality. Data pertaining to COVID-19 cases, deaths of affected countries, and their potential correlates were retrieved from various public domains, for example, https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports, worldpopulationreview.com, data.worldbank.org. Results: Multiple linear regressions analysis through forward method revealed a maximum R2 of 57.7% (P = 0.03) and a significant model fit (P = 0.000) for COVID-19 mortality rate per million which was revealed to have a positive association with median age of the population of the country (β = 0.073), proportion of population sustaining obesity (β = 0.051) and %of population consumed alcohol over the past 12 months (β = 0.018). It meant for 1 year increase in median age COVID-19 mortality would be increased by 8.0%. Similarly, COVID-19 mortality would be increased by 2.0% and 5.0% by inclusion in the model of 1% alcoholic, and 1% obese individual, respectively, Conclusion: Notwithstanding variations in testing, reporting, and patients' management strategy the findings of this research have some implications to the scientific fraternity and policymakers.

A Comparison of Machine Learning Methods to Forecast Tropospheric Ozone Levels in Delhi

Ground-level ozone is a pollutant that is harmful to urban populations, particularly in developing countries where it is present in significant quantities. It greatly increases the risk of heart and lung diseases and harms agricultural crops. This study hypothesized that, as a secondary pollutant, ground-level ozone is amenable to 24 h forecasting based on measurements of weather conditions and primary pollutants such as nitrogen oxides and volatile organic compounds. We developed software to analyze hourly records of 12 air pollutants and 5 weather variables over the course of one year in Delhi, India. To determine the best predictive model, eight machine learning algorithms were tuned, trained, tested, and compared using cross-validation with hourly data for a full year. The algorithms, ranked by R2 values, were XGBoost (0.61), Random Forest (0.61), K-Nearest Neighbor Regression (0.55), Support Vector Regression (0.48), Decision Trees (0.43), AdaBoost (0.39), and linear regression (0.39). When trained by separate seasons across five years, the predictive capabilities of all models increased, with a maximum R2 of 0.75 during winter. Bidirectional Long Short-Term Memory was the least accurate model for annual training, but had some of the best predictions for seasonal training. Out of five air quality index categories, the XGBoost model was able to predict the correct category 24 h in advance 90% of the time when trained with full-year data. Separated by season, winter is considerably more predictable (97.3%), followed by post-monsoon (92.8%), monsoon (90.3%), and summer (88.9%). These results show the importance of training machine learning methods with season-specific data sets and comparing a large number of methods for specific applications.

Using UAV and Multispectral Images to Estimate Peanut Maturity Variability on Irrigated and Rainfed Fields Applying Linear Models and Artificial Neural Networks

Using UAV and multispectral images has contributed to identifying field variability and improving crop management through different data modeling methods. However, knowledge on application of these tools to manage peanut maturity variability is still lacking. Therefore, the objective of this study was to compare and validate linear and multiple linear regression with models using artificial neural networks (ANN) for estimating peanut maturity under irrigated and rainfed conditions. The models were trained (80% dataset) and tested (20% dataset) using results from the 2018 and 2019 growing seasons from irrigated and rainfed fields. In each field, plant reflectance was collected weekly from 90 days after planting using a UAV-mounted multispectral camera. Images were used to develop vegetation indices (VIs). Peanut pods were collected on the same dates as the UAV flights for maturity assessment using the peanut maturity index (PMI). The precision and accuracy of the linear models to estimate PMI using VIs were, in general, greater in irrigated fields with R2 > 0.40 than in rainfed areas, which had a maximum R2 value of 0.21. Multiple linear regressions combining adjusted growing degree days (aGDD) and VIs resulted in decreased RMSE for both irrigated and rainfed conditions and increased R2 in irrigated areas. However, these models did not perform successfully in the test process. On the other hand, ANN models that included VIs and aGDD showed accuracy of R2 = 0.91 in irrigated areas, regardless of using Multilayer Perceptron (MLP; RMSE = 0.062) or Radial Basis Function (RBF; RMSE = 0.065), as well as low tendency (1:1 line). These results indicated that, regardless of the ANN architecture used to predict complex and non-linear variables, peanut maturity can be estimated accurately through models with multiple inputs using VIs and aGDD. Although the accuracy of the MLP or RBF models for irrigated and rainfed areas separately was high, the overall ANN models using both irrigated and rainfed areas can be used to predict peanut maturity with the same precision.

Use of Thoracic Ultrasonography to Improve Disease Detection in Experimental BRD Infection.

Bovine respiratory disease (BRD) is caused by complex interactions between viral and bacterial pathogens, host immune status, and environmental stressors. In both clinical and research settings, current methods for detecting BRD in calves commonly focus on visual indicators such as attitude, nasal discharge, and cough, in addition to vital signs such as rectal temperature and respiration rate. Recently, thoracic ultrasonography (TUS) has become more commonly used in clinical settings, in addition to physical examination to diagnose BRD. To assess the value of performing TUS during experimental BRD infection, 32 calves were challenged with bovine respiratory syncytial virus, to mimic a viral infection, and 30 calves were infected with Mannheimia haemolytica, to mimic a bacterial infection. TUS was performed at regular intervals using a standardized method and scoring system in addition to daily clinical scoring. Although overall correlations between clinical scores and TUS scores were generally weak (maximum R2 = 0.3212), TUS identified calves with abnormal lung pathology that would have otherwise been misclassified on the basis of clinical scoring alone, both on arrival and throughout the studies. In addition, TUS had an increased correlation with gross lung pathology on necropsy (maximum R2 = 0.5903), as compared to clinical scoring (maximum R2 = 0.3352). Our results suggest that TUS can provide additional information on calf health at enrollment and throughout a study and may provide an alternative to terminal studies, due to the high correlation with lung pathology at necropsy.

Estimation of Forest Aboveground Biomass in Karst Areas Using Multi-Source Remote Sensing Data and the K-DBN Algorithm

Accurate estimation of forest biomass is the basis for monitoring forest productivity and carbon sink function, which is of great significance for the formulation of forest carbon neutralization strategy and forest quality improvement measures. Taking Guizhou, a typical karst region in China, as the research area, this study used Landsat 8 OLI, Sentinel-1A, and China national forest resources continuous inventory data (NFCI) in 2015 to build a deep belief network (DBN) model for aboveground biomass (AGB) estimation. Based on the introduction of forest canopy density (FCD), we improved the DBN model to design the K-DBN model with the highest estimation accuracy is selected for AGB inversion and spatial mapping. The results showed that: (1) The determination coefficients R2 of DBN is 0.602, which are 0.208, 0.101 higher than that of linear regression (LR) and random forest (RF) model. (2) The K-DBN algorithm was designed based on FCD to optimize the DBN model, which can alleviate the common problems of low-value overestimation and high-value underestimation in AGB estimation to a certain extent to improve the estimation accuracy. The maximum R2 of the model reached 0.848, and we mapped the forest AGB using the K-DBN model in the study area in 2015. The conclusion of this study: Based on multi-source optical and radar data, the retrieval accuracy of forest AGB can be improved by considering the FCD, and the deep learning algorithm K-DBN is excellent in forest AGB remote sensing estimation. These research results provide a new method and data support for the spatio-temporal dynamic remote sensing monitoring of forest AGB in karst areas.

The effect of washing on the electrical performance of knitted textile strain sensors for quantifying joint motion

Successful market penetration of textile-based strain sensors requires long-term reliability which in turn relies on the washability of the sensor. First, this paper presents an evaluation of the effect of 5 washing cycles on the electrical performance of a knitted conductive transducer, over 1500 cycles of repetitive elongation. The promising behaviour of the textile sensor in this study showed that it might be possible to make a smart garment, capable of quantifying elbow flexion-extension motion, by integrating it into an elbow sleeve. Second, a prototype sleeve, incorporating a knitted sensor (the so-called smart sleeve), was tested in a simulated training/clinical setting by performing 50 flexion-extension cycles after 1, 5, 15, 25, 50 and 75 washes. In both studies, the electrical resistance of the sensor increased with the number of washes in a predictable manner and exhibited a repeatable, reliable and prompt response to elongation. In particular, the electrical pattern representing flexion-extension motion measured using the sleeve was clear and distinguishable up to the 75th wash. Moreover, resistance measurements within the same trial were repeatable at maximum flexion (≤2% variation) and at maximum extension (≤3% variation) and predictable with increasing washes (R2 = 0.992 at maximum flexion and R2 = 0.989 at maximum extension). The good washability of the smart sleeve, evidenced by its ability to detect, distinguish and measure parameters of flexion-extension motion up to 75 washes, makes it a suitable and sustainable choice for applications, such as strength conditioning or rehabilitation, where repetition count and speed are useful.

Investigation of ANN architecture for predicting the compressive strength of concrete containing GGBFS.

An extensive simulation program is used in this study to discover the best ANN model for predicting the compressive strength of concrete containing Ground Granulated Blast Furnace Slag (GGBFS). To accomplish this purpose, an experimental database of 595 samples is compiled from the literature and utilized to find the best ANN architecture. The cement content, water content, coarse aggregate content, fine aggregate content, GGBFS content, carboxylic type hyper plasticizing content, superplasticizer content, and testing age are the eight inputs in this database. As a result, the optimal selection of the ANN design is carried out and evaluated using conventional statistical metrics. The results demonstrate that utilizing the best architecture [8–14–4–1] among the 240 investigated architectures, and the best ANN model, is a very efficient predictor of the compressive strength of concrete using GGBFS, with a maximum R2 value of 0.968 on the training part and 0.965 on the testing part. Furthermore, a sensitivity analysis is performed over 500 Monte Carlo simulations using the best ANN model to determine the reliability of ANN model in predicting the compressive strength of concrete. The findings of this research may make it easier and more efficient to apply the ANN model to many civil engineering challenges.

Effective method of monitoring cerebral tissue oxygen saturation in cardiac surgery patients by combined use of tNIRS-1 and bispectral index

To continuously and noninvasively monitor the cerebral tissue oxygen saturation (StO2) and hemoglobin concentration (gasHb) in cardiac surgery patients, a method combining the use of a cerebral tissue oximeter using near infrared time-resolved spectroscopy (tNIRS-1) and the bispectral index (BIS) was developed in this study. Moreover, the correlation between the estimated hemoglobin concentration (estHb), measured via tNIRS-1, and the hemoglobin concentration (gasHb), analyzed using a blood gas analyzer, were compared. The relationship between the BIS and gasHb was also examined. Through the comparison of BIS and StO2 (r1), and estHb and gasHb (r2), the correlation between the two was clarified with maximum r1 and r2 values of 0.617 and 0.946, respectively. The relationship between BIS and gasHb (r3), showed that there was a favorable correlation with a maximum r3 value of 0.969. There was also a continuous correlation between BIS and StO2 in patients undergoing cardiac surgery. In addition, a strong correlation was found between estHb and gasHb, and between BIS and gasHb. It was therefore concluded that the combined use of BIS and tNIRS-1 is useful to evaluate cerebral hypoxia, allowing for quick response to cerebral hypoxia and reduction of hemoglobin concentration during the operation.

Establishing a Reference Baseline for Midday Stem Water Potential in Olive and Its Use for Plant-Based Irrigation Management.

Midday stem water potential (SWP) is rapidly becoming adopted as a standard tool for plant-based irrigation management in many woody perennial crops. A reference or “baseline” SWP has been used in some crops (almond, prune, grape, and walnut) to account for the climatic influence of air vapor pressure deficit (VPD) on SWP under non-limiting soil moisture conditions. The baseline can be determined empirically for field trees maintained under such non-limiting conditions, but such conditions are difficult to achieve for an entire season. We present the results of an alternative survey-based approach, using a large set of SWP and VPD data collected over multiple years, from irrigation experiments in olive orchards located in multiple countries [Spain, United States (California), Italy, and Argentina]. The relation of SWP to midday VPD across the entire data set was consistent with an upper limit SWP which declined with VPD, with the upper limit being similar to that found in Prunus. A best fit linear regression estimate for this upper limit (baseline) was found by selecting the maximum R2 and minimum probability for various upper fractions of the SWP/VPD relation. In addition to being surprisingly similar to the Prunus baseline, the olive baseline was also similar (within 0.1 MPa) to a recently published mechanistic olive soil-plant-atmosphere-continuum (SPAC) model for “super high density” orchard systems. Despite similarities in the baseline, the overall physiological range of SWP exhibited by olive extends to about −8 MPa, compared to about −4 MPa for economically producing almond. This may indicate that, despite species differences in physiological responses to low water availability (drought), there may be convergent adaptations/acclimations across species to high levels of water availability. Similar to its use in other crops, the olive baseline will enable more accurate and reproducible plant-based irrigation management for both full and deficit irrigation practices, and we present tentative SWP guidelines for this purpose.

Influence of a double vortex chamber on temperature reduction in a counter-flow vortex tube

This article reports the effect of double vortex-chambers with multiple inlet snail entries of N = 1, 4 and 6 nozzles on the energy separation referred to cold gas exit temperature difference (ΔTc) in a counter-flow vortex tube type. The experimental work focused on ascertaining the effects of entry air pressure (Pi = 2, 3 and 4 bar), distance ratios between the two vortex-chamber to the vortex tube diameter (l/D = 0.875–1.125) and the cold gas mass ratio (μc) in a vortex tube. It was found that cold gas exit temperature difference (ΔTc) increased with increasing inlet air pressure (Pi) and number of inlet nozzles (N), and decreasing l/D. Among the studied conditions, the double vortex-chamber operated at the highest Pi of 4 bar, N = 6, the smallest l/D = 0.875 and μc = 0.38 gave the highest cold gas exit temperature difference (ΔTc) of 31.5 °C. In addition, the deep learning optimization technique was also developed to predict the temperatures for different combination of parameters used in this study. It was found that the optimal models provide maximum R2 value of 0.99317.

Regression Analysis and Comparison of Economic Parameters with Different Light Index Models under Various Constraints.

Economic globalization is developing more rapidly than ever before. At the same time, economic growth is accompanied by energy consumption and carbon emissions, so it is particularly important to estimate, analyze and evaluate the economy accurately. We compared different nighttime light (NTL) index models with various constraint conditions and analyzed their relationships with economic parameters by linear correlation. In this study, three indices were selected, including original NTL, improved impervious surface index (IISI) and vegetation highlights nighttime-light index (VHNI). In the meantime, all indices were built in a linear regression relationship with gross domestic product (GDP), employed population and power consumption in southeast China. In addition, the correlation coefficient was used to represent fitting degree. Overall, comparing the regression relationships with GDP of the three indices, VHNI performed best with the value of at 0.8632. For the employed population and power consumption regression with these three indices, the maximum of VHNI are 0.8647 and 0.7824 respectively, which are also the best performances in the three indices. For each individual province, the VHNI perform better than NTL and IISI in GDP regression, too. When taking employment population as the regression object, VHNI performs best in Zhejiang and Anhui provinces, but not all provinces. Finally, for power consumption regression, the value of VHNI is better than NTL and IISI in every province except Hainan. The results show that, among the indices under different constraint conditions, the linear relationships between VHNI and GDP and power consumption are the strongest under vegetation constraint in southeast China. Therefore, VHNI index can be used for fitting analysis and prediction of economy and power consumption in the future.

Recognition of dominant driving factors behind sap flow of Liquidambar formosana based on back-propagation neural network method

Key message Three-layered back-propagation (BP) neural networks with architecture 4–10-1 (four neurons in the input layer, ten neurons in the hidden layer and one neuron in the output layer) performed better than MLR (multivariate linear regression) in modelling complex nonlinear relationships between sap flow and driving factors. The optimum BP model was achieved with an input combination of air temperature, relative humidity, average net radiation, and a phenological index. The performance of BP models indicated a small improvement with the inclusion of a phenological index. AimsThis study focused on the applicability of back-propagation (BP) neural networks in simulating sap flow (SF) using meteorological factors and a phenological index (PI) for Liquidambar formosana, a deciduous broad-leaf tree species in subtropical China, and thus providing a useful and promising alternative to traditional methods for transpiration prediction.MethodsThree-layered BP models with an architecture 4–10-1 (four neurons in the input layer, ten neurons in the hidden layer and one neuron in the output layer) were trained and tested using the Levenberg–Marquardt (LM) algorithm based on in situ observations of SF and concurrent microclimate at the Qianyanzhou Ecological Station, Jiangxi Province, Southeast China. The model performance was verified with testing data not used in model development.ResultsThe BP models with eight input combinations proved a satisfactory fit: the determination coefficients (R2) and fitting accuracies (Acc) (about 0.8 and 70%) were significantly higher than those of the multivariate linear regression (MLR) (about 0.5 and 50%), indicating their advantage in solving complex nonlinear relationships involved in transpiration. In addition, the BP models showed a bit better performance by adding PI to the input family. The best BP model was achieved taking air temperature (Ta), relative humidity (RH), average net radiation (ANR), and PI as the input and sap flux density (vs) as the output, with maximum R2 and Acc as high as 0.95 and 90%, respectively.ConclusionThe BP models with input combination of Ta, RH, ANR, and PI mirrored very well measured daily variations in vs. The results could be used to fine-tune estimations of sap flow by Liquidambar formosana, and thus shed light on the eco-hydrological process related to transpiration for deciduous broad-leaf trees.

Weather based pest forewarning models for mealybug infestation in Robusta coffee (Coffea canephora)


 An investigation was carried out to study the effect of weather parameters on mealybug infestation in Robusta coffee and to develop forewarning model using 39 years (1977 to 2015) pest data on mealy bug damage in coffee plantations collected at Regional Coffee Research Station, Chundale, Wayanad district of Kerala were recorded at fortnightly intervals during 1977 to 2015 (39 years). The results revealed that mealybug infestation was found to vary with season and also year to year. Fruit setting and budding stages of coffee were severely damaged by mealy bug. Analysis indicated that annually, average damage due to mealy bug was 6.4 per cent and ranged between 5.4 per cent in 1984 to 9.7 per cent in 2011. The mealybug damage was maximum during summer season and lowest during South-West Monsoon (June-September) season. Season wise regression models were developed using data up to 2013 for forewarning per cent damage of mealy bug and validated for 2014 and 2015. The model for summer season mealybug damage has maximum R2=0.79 which can be used for operational forecasting of mealy bug damage.

Developing a Formula to Estimate Precipitation at Ungauged Location and Analysis of Rainfall Pattern: Case Study for Rastra Bank Chowk, Pokhara, Nepal

Pokhara receives the second highest amount of rainfall in the country, according to Department of Hydrology and Meteorology. The rainfall distribution within this small region is random. The precipitation in Pokhara is measured from limited rain gauge stations. For understanding the spatial variation, it is necessary to access the daily rainfall data at the gauged as well as ungauged stations. In view of this problem, this study is aimed at developing the formula to estimate the rainfall value at an ungauged station, Rastrabank Chowk Pokhara using the calibration and validation method. For this purpose, the daily rainfall data of the sample station was measured using standard rain gauge for a period of four months from May 13, 2020 to September 15, 2020. The analysis of the spatial distribution and variation of daily rainfall at meteorological stations as well as sample station was done to select the data for the development of the formula. Calibration and validation of formula was performed for equal period using the statistical indices NSE, R2 and PBIAS. Calibration was performed using rainfall data of available meteorological stations of Pokhara in the first two consecutive months. From the numerous hit and trial procedures, the equation developed with rainfall data of Airport and Malepatan stations comprising of three constant values were obtained with the highest NSE and R2. PBIAS value was then calculated for maximum NSE and R2. In the next phase, validation of the equation for other two months (14 July- 15 September,2020) was performed to obtain maximum possible value of NSE and R2 and a smaller value of PBIAS. The equation was obtained with 0.980 NSE, 0.980 R2 and 6.437 % PBIAS value which indicated good agreement between observed and predicted rainfall value. Spatial rainfall pattern analysis using SURFER model demonstrated that there was a homogenous distribution of rainfall in Malepatan, Airport and sample stations. The temporal trend showed that the month July of 2020 had a higher amount of precipitation at Pokhara valley. From the HYSPLIT model, it was observed that the rainfall events in Pokhara valley was due to westerlies dominance. This study would provide the rainfall estimation techniques and hence would significantly contribute to meteorological investigations and water resource planning.

Agricultural soil fertility properties in the near infrared spectrum

Soil spectrum in the near infrared (NIR) wavelength region can be used to reveal fertility properties which is related to plant cultivations. The main purpose of this presented paper is to study the soil spectrum in the NIR region and its related to the fertility properties in form of heavy metals like Fe and Cu. Soil samples were obtain from several land-use including agriculture, mining and ground field. Near infrared spectrum of soil samples were acquired in wavelength range from 1000 to 2500 nm. Prediction models used to determine Fe and Cu were built by means of partial least squares regression (PLSR) followed by leverage cross validation. Prediction performance was evaluated using coefficient of determination (r2) and ratio of prediction to deviation (RPD). The results showed that both Fe and Cu can be revealed simultaneously using the NIR spectrum with maximum r2 and RPD indexes were 0.93 and 3.86 for Fe and 0.71 and 1.88 for Cu prediction respectively. Based on the achieved results, it may conclude that soil fertility properties can be revealed simultaneously and rapidly using mear infrared spectral data.