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Globally, the productive utilization of biomass has paid serious attention to fulfilling the energy requirements laid out by the international standards, as to reduce related carbon footprints. Therefore, this study investigates date palm waste leaves which aims to produce environment friendly H 2 gas using gasification technology. The results of 25 experimental runs exhibited that the higher H 2 produced at higher temperature which was mainly supported by water-gas-shift and steam-methane reforming reactions. H 2 prediction was modeled using response surface methodology (RSM) and artificial neural network (ANN). The RSM model exhibited a strong interaction with the regression coefficient (R2) and p-value of 0.89 and 0.000000, respectively. ANN data was disseminated thru K-fold contrivance with back-propagation algorithm. Hence, the training (80% data) and validation (20% data) datasets were found with R2 and root mean squared error (RSME) of 0.90 and 0.28, and 0.86 and 0.39, respectively. Kinetics of the process estimated the activation energies (Ea) using Ozawa-Flynn-Wall (OFW), Starink (STK), and Kissinger-Akahira-Sunose (KAS) models. Hence, the values of Ea and R2 at conversion degrees (α) 0.1 to 0.8 were ranged between 129.40 and 326.64 kJ/mol and 0.92 to 0.97, respectively. Optimum H 2 production of 49.03 vol% (with LHV of 11.10 MJ/Nm3) was produced. This finding is thought to be a better source of energy which can be an appropriate fuel for Fischer Tropsch process for manufacturing of transportation fuels. [Display omitted] • H 2 -rich syngas from date palm waste leaves in Pakistan was investigated thoroughly. • Optimum H 2 production of 49.3 vol % (with LHV 11.10 MJ/Nm3) was produced. • Temperature was ascertained as a critical parameter to enhance H 2 production. • Data was found satisfactory using machine learning tools and kinetic models. [ABSTRACT FROM AUTHOR]

Sindh province of Pakistan has a long history of severe droughts. Several large scale climate drivers (LSCD) are known for their effect on precipitation worldwide but studies in the Sindh region are missing; wide variety of LSCDs and lagged associative information. This study aimed to identify the significant LSCDs in Sindh province of Pakistan and improve the forecast skill of monthly precipitation by employing the principal component analysis (PCA), artificial neural network (ANN), Bayesian regularization neural network (BRNN), and multiple regression analysis (MRA), while considering the 12 months lagged LSCDs such as Nino-1+2, Nino-3, Nino-3.4, Nino-4, Quasi-Biennial Oscillation (QBO) at 30 and 50hPa (QBOI and QBOII), sea surface temperature (SST), 2m air temperature (T2M), 500 hPa and 850 hPa geopotential heights (H500 and H850), surface and 500 hPa zonal velocity (SU and U500), latent and sensible heat fluxes over land (LHFOL and SHFOL), and surface specific humidity (SSH). Global Land Data Assimilation System (GLDAS), Tropical Rainfall Measuring Mission (TRMM), Modern-Era Retrospective Analysis for Research and Application (MERRA-2), NOAA, Freie University Berlin, and Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) datasets were used. Results manifested that significant LSCDs with 99% confidence level were SU, U500, T2M, SST, SHFOL, LHFOL, SSH, and H850. During test period, compared with MR models of 0.39 to 0.64 and principal components of 0.31 to 0.57, the ANN and BRNN models had better predictive skills with correlation coefficients of 0.57 to 0.83 and 0.52 to 0.76, respectively. It can be concluded that the ANN and BRNN models enable us to predict monthly precipitation in Sindh region with lagged LSCDs. [ABSTRACT FROM AUTHOR]

Sedimentary rocks provide information on previous environments on the surface of the Earth. As a result, they are the principal narrators of the former climate, life, and important events on the surface of the Earth. The complexity and cost of direct destructive laboratory tests adversely affect the data scarcity problem, making the development of intelligent indirect methods an integral step in attempts to address the problem faced by rock engineering projects. This study established an artificial neural network (ANN) approach to predict the uniaxial compressive strength (UCS) in MPa of sedimentary rocks using different input parameters; i.e., dry density (ρd) in g/cm3, Brazilian tensile strength (BTS) in MPa, and wet density (ρwet) in g/cm3. The developed ANN models, M1, M2, and M3, were divided as follows: the overall dataset, 70% training dataset and 30% testing dataset, and 60% training dataset and 40% testing dataset, respectively. In addition, multiple linear regression (MLR) was performed for comparison to the proposed ANN models to verify the accuracy of the predicted values. The performance indices were also calculated by estimating the established models. The predictive performance of the M2 ANN model in terms of the coefficient of determination (R2), root mean squared error (RMSE), variance accounts for (VAF), and a20-index was 0.831, 0.27672, 0.92, and 0.80, respectively, in the testing dataset, revealing ideal results, thus it was proposed as the best-fit prediction model for UCS of sedimentary rocks at the Thar coalfield, Pakistan, among the models developed in this study. Moreover, by performing a sensitivity analysis, it was determined that BTS was the most influential parameter in predicting UCS. [ABSTRACT FROM AUTHOR]

The rising number of confirmed cases and deaths in Pakistan caused by the coronavirus have caused problems in all areas of the country, not just healthcare. For accurate policy making, it is very important to have accurate and efficient predictions of confirmed cases and death counts. In this article, we use a coronavirus dataset that includes the number of deaths, confirmed cases, and recovered cases to test an artificial neural network model and compare it to different univariate time series models. In contrast to the artificial neural network model, we consider five univariate time series models to predict confirmed cases, deaths count, and recovered cases. The considered models are applied to Pakistan's daily records of confirmed cases, deaths, and recovered cases from 10 March 2020 to 3 July 2020. Two statistical measures are considered to assess the performances of the models. In addition, a statistical test, namely, the Diebold and Mariano test, is implemented to check the accuracy of the mean errors. The results (mean error and statistical test) show that the artificial neural network model is better suited to predict death and recovered coronavirus cases. In addition, the moving average model outperforms all other confirmed case models, while the autoregressive moving average is the second-best model. [ABSTRACT FROM AUTHOR]

Sustainable and alternative energy sources of biofuel and solar power panel have been revolutionizing the lives and economy of many countries. However, these changes mainly occur in the urban areas and the rural population section has long been ignored by policy makers and government in the provision of energy. It is only recently that solar and biofuel are finally making in road to provide cheap and clean energy sources to rural population. As a result, literatures on consumer behavior of rural population towards sustainable energy sources are still very scarce. The present research aims to fulfill this gap by developing a conceptual model to investigate the adoption of solar power and biofuel energy resources in the cross-cultural setting of Malaysia and Pakistan. The data was collected from the rural areas of Pakistan and Malaysia. The two-stage data analysis method of partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) have been applied to satisfy both linear and non-linear regression assumptions, respectively. The results show that consumer in rural areas of Pakistan are willing and possess intention to adopt both biofuel and solar power for commercial and domestic use. Additionally, the results confirm that branding, economic, and altruistic factors are important in yielding intention to use towards biofuel and solar power panel in Pakistan which are validated by the results obtained in Malaysia. Other factors such as climate change awareness, retailer services quality, and ease of use are also important. The results offer wide-ranging theoretical and managerial implications. [ABSTRACT FROM AUTHOR]

Earthquake risk in urban areas has recently taken quite a lot of attention. Risk is a cross-cutting combination of vulnerability and hazard. Abbottabad district, a rapidly growing district, is an earthquake-prone area in the north of Pakistan. In 2005, the region suffered enormous casualties due to a significant earthquake (Mw ¼ 7.6). The various regional behaviours, including social, physical, and economic behaviours, are causing a vulnerable situation. At the same time, the high seismicity has the potential to invite earthquake hazard, thus ensuing a possible risk situation in the region. Consequently, this study proposes an integrated approach involving an artificial neural network (ANN), convolutional neural network (CNN), and analytic network process (ANP) to carry out the seismic risk analysis of the Abbottabad district. The proposed two integrated frameworks are ANP-ANN and ANP-CNN. This kind of methodology has not been applied before in this area. The geographic information system is used to formulate a broad range of contributing factors depicting the hazard and vulnerability of the area and potentially contributing to the seismic risk. A total of 16 contributing factors are chosen for this study. The selected factors are weighed using the ANP, and training and testing databases are generated to train and test the models, thus producing the area’s earthquake risk maps (ERMs). As per the ERMs, the northwestern regions and the borderline regions from the southeast, covering an area of 30%, possess a greater earthquake risk. The rest of the 70% of the area has less risk exposure. The AUC values of ANN and CNN are found to be 0.843 and 0.878, respectively, exhibiting a good performance by the models. The study paves the way for the concerned authorities to match and redefine their policies for developing, expanding, and managing the region’s structural, health care, and disaster management facilities. [ABSTRACT FROM AUTHOR]

Energy management benefits both consumers and utility companies alike. Utility companies remain interested in identifying and reducing energy waste and theft, whereas consumers' interest remain in lowering their energy expenses. A large supply-demand gap of over 6 GW exists in Pakistan as reported in 2018. Reducing this gap from the supply side is an expensive and complex task. However, efficient energy management and distribution on demand side has potential to reduce this gap economically. Electricity load forecasting models are increasingly used by energy managers in taking real-time tactical decisions to ensure efficient use of resources. Advancement in Machine-learning (ML) technology has enabled accurate forecasting of electricity consumption. However, the impact of computation cost afforded by these ML models is often ignored in favour of accuracy. This study considers both accuracy and computation cost as concurrently significant factors because together they shape the technology environment as well as create economic impact. Thus, a three-fold optimized load forecasting model is proposed which includes (1) application specific parameters selection, (2) impact of different dataset granularities and (3) implementation of specific data preparation. It deploys and compares the widely used back-propagation Artificial Neural Network (ANN) and Random Forest (RF) models for the prediction of electricity consumption of buildings within a university. In addition to the temporal and historical power consumption date as input parameters, the study also embeds weather data as well as university operational calendars resulting in improved performance. The outcomes are indicative that the granularity i.e. the scale of details in data, and set of reduced and full input parameters impact performance accuracies differently for ANN and RF models. Experimental results show that overall RF model performed better both in terms of accuracy as well as computational time for a 1-min, 15-min and 1-h dataset granularities with the mean absolute percentage error (MAPE) of 2.42, 3.70 and 4.62 in 11.1 s, 1.14 s and 0.3 s respectively, thus well suited for a real-time energy monitoring application. [ABSTRACT FROM AUTHOR]

The Balochistan province of Pakistan is mostly affected by severe drought events due to little amount of precipitation. “Several Large Scale Climate Drivers (LSCDs) are known for their effects on precipitation worldwide but studies in the region are missing; a wide variety of LSCDs and lagged associative information”.The current study aimed to identify the significant LSCDs in the Balochistan province of Pakistan and improve the prediction skill of monthly precipitation by employing the Principal Component Analysis, Artificial Neural Network (ANN), Bayesian Regularization Neural Network (BRNN), and Multiple Regression (MR) Analysis using the 12 months lagged LSCDs such as Nino-1+2, Nino-3, Nino-3.4, Nino-4, QBO at 30 and 50hpa (QBOI and QBOII), Sea Surface Temperature (SST), 2m air temperature (T2M), 500hpa and 850hpa geopotential heights (H500 and H850), 500hpa zonal velocity (U500), 500hpa and 850hpa meridional velocity (V500 and V850), Latent and Sensible Heat Fluxes Over Land (LHFOL and SHFOL), and Surface Specific Humidity (SSH). To collect the data, Global Land Data Assimilation System, Tropical Rainfall Measuring Mission, MERRA-2, NOAA, Freie University Berlin, and HadISST datasets were used. The results of the study showed that significant LSCDs with a 99% confidence level were SSH, SST, LHFOL, SHFOL, T2M, U500, Nino-3.4, and Nino-4. During the test period, compared with MR models of 0.15 to 0.49 and principal components of -0.16 to 0.43, the ANN and BRNN models had better predictive skills with correlation coefficients of 0.40 to 0.74 and 0.34 to 0.70, respectively. It can be concluded that the ANN and BRNN models enable us to predict monthly precipitation in the Balochistan region with lagged LSCDs. Extended Abstract Introduction Pakistan is one of the most vulnerable countries due to climate change. According to the Global Climate Risk Index (GCRI) report, with the passage of time, its vulnerability is increasing. The main threats consist of a rise in temperature, irregular patterns of rainfall, a rise in the sea level, and extreme events such as droughts, floods, and heatwaves. Precipitation plays an important role in the economy of Pakistan as an agricultural country that has experienced variations, particularly in a recent couple of decades when a sharp jump in global atmospheric temperatures was noticed. The southern part of Pakistan which comprises the Sindh and Balochistan provinces has an arid climate and is mostly affected by severe drought events due to less amount of rainfall throughout the year as compared to other parts of the country. This study’s main focus is Balochistan province which has a long history of severe droughts and is an important province in terms of agriculture. Less than 250mm is the annual average rainfall received by the region. It is predicted that throughout the 21st century if adaptation measures are not taken, there will be a continuous increase in droughts and scarcity of water which will adversely affect the lives of people and the economy. Globally, precipitation and droughts are strongly linked with the large-scale climate drivers through atmospheric associations but there are no detailed studies in Balochistan province that consider a wide variety of large-scale climate drivers and their lagged association with precipitation. The main objective of the present study is to identify the significant large-scale climate drivers in the Balochistan province of Pakistan and improve the prediction skill of monthly precipitation by applying the principal component analysis, artificial neural network, Bayesian regularization neural network, and multiple regression analysis considering the lagged association of climate drivers. Methodology In the present study, first, large-scale climate drivers from NOAA HadISST, MERRA-2, and Freie University Berlin in NetCDF files were processed using the ArcGIS model builder along with the precipitation data from GLDAS and TRMM. The data were first normalized in the range of 0 and 1 using the min-max normalization formula. For showing the 12 months’ lagged association between the large-scale climate drivers and precipitation, a cross-correlation method was employed and the heatmaps were created in Origin 2021b software to show significant lagged correlations. Principal component analysis was applied and the variances and Eigenvalues for each of the components were calculated. Multi-layer feed-forward neural network with a back propagation algorithm was used for the prediction of monthly precipitation with the first two PCs in most cases, three and one in some cases as the predictors. Bayesian Regularization Neural Network (BRNN) was applied which is a version of Artificial Neural Networks (ANN) and is a more powerful method as compared to conventional ANN. MATLAB R2015a environment was used for this purpose. Then, Multiple Regression Analysis (MRA) was carried out and was used as a benchmark for comparing ANN and BRNN models using significantly lagged climate indices in one case and in another case the selected PCs. To validate the performance of all developed models, the TRMM dataset was used. Finally, time series graphs and Radar charts were prepared for comparison. Discussions The findings of cross-correlations between monthly precipitation and large-scale climate drivers in Balochistan province showed the most dominant climate drivers as surface specific humidity, sea surface temperature, latent and sensible heat fluxes over land, 500hpa zonal velocity, 2m air temperature, Nino-3.4 and Nino-4, and the highest correlations were noticed for surface specific humidity, sea surface temperature, latent and sensible heat fluxes over land, 500hpa zonal velocity, 2m air temperature. It is noteworthy that in each district, the lagged correlation of maximum climate indices with precipitation was distinct at distinct lags. The multiple regression best models were selected on the basis of no violation of the limits of statistical significance and lower errors. The MLR model’s performance was low in the region where some of the districts had low correlations. The results from ANN and BRNN models showed that the BRNN models were on a lower side than the ANN models with higher values of correlation coefficient showing their capability of finding the pattern and trend of the GLDAS precipitation. The results of the evaluation of the generalization capability of all models on the TRMM dataset showed that both ANN and BRNN models relatively performed well as correlation and error values were closer to each other. Conclusions The results of this study showed that, first, significant LSCDs with 99% confidence level were surface specific humidity (SSH), sea surface temperature (SST), latent and sensible heat fluxes over land (LHFOL and SHFOL), 2m air temperature (T2M), 500hpa zonal velocity (U500), Nino-3.4, and Nino-4. Second, to predict the monthly precipitation using lagged LSCDs and principal components, MR models were developed. MR models’ performance was low. The highest Pearson correlation in MR models during the training set was observed for Musakhel district as 0.65 and during the test as 0.49 for Musakhel and Zhob. In MR-PC models, the highest correlation was recorded for Awaran (0.50) during the training and for Quetta (0.43) during the test. Third, ANN and BRNN models were developed using the selected PC components and gave higher correlations as compared to regression models indicating their capability of finding the pattern and trend of the observations. They generally manifested lower errors and are more reliable for the purpose of prediction in the region. Maximum correlations during training were 0.77 and 0.73 in both ANN and BRNN models, respectively, for the Lasbela district. In the test case, they were 0.74 and 0.70 for Awaran. Their generalization ability was tested on the TRMM dataset. In conclusion, this study divulged the possibility of monthly precipitation prediction using ANN and BRNN and lagged LSCDs for the study region. It is explicit that the response of precipitation to climatic factors is delayed. Comparing the results of the ANN, BRNN, and MLR, it can be concluded that the artificial intelligence approaches such as ANN and BRNN are reliable nonlinear statistical options that can generate similar and in some cases better forecasts and could be useful for the agriculture and water management in the Balochistan region as it is a very important part of Pakistan in terms of agriculture. In the future, this research will be extended by developing the prediction models by including more LSCDs and can be improved by the approaches of genetic algorithm for the best input selection and the use of observed station-based precipitation data sets. [ABSTRACT FROM AUTHOR]

Tarbela is the largest earth-filled dam in Pakistan, used for both irrigation and power production. Tarbela has already lost around 41.2% of its water storage capacity through 2019, and WAPDA predicts that it will continue to lose storage capacity. If this issue is ignored for an extended period of time, which is not far away, a huge disaster will occur. Sedimentation is one of the significant elements that impact the Tarbela reservoir's storage capacity. Therefore, it is crucial to accurately predict the sedimentation inside the Tarbela reservoir. In this paper, an Artificial Neural Network (ANN) architecture and multivariate regression technique are proposed to validate and predict the amount of sediment deposition inside the Tarbela reservoir. Four input parameters on yearly basis including rainfall (Ra), water inflow (Iw), minimum water reservoir level (Lr), and storage capacity of the reservoir (Cr) are used to evaluate the proposed machine learning models. Multivariate regression analysis is performed to undertake a parametric study for various combinations of influencing parameters. It was concluded that the proposed neural network model estimated the amount of sediment deposited inside the Tarbela reservoir more accurately as compared to the multivariate regression model because the maximum error in the case of the proposed neural network model was observed to be 4.01% whereas in the case of the multivariate regression model was observed to be 60.7%. Then, the validated neural network model was used for the prediction of the amount of sediment deposition inside the Tarbela reservoir for the next 20 years based on the time series univariate forecasting model ETS forecasted values of Ra, Iw, Lr, and Cr. It was also observed that the storage capacity of the Tarbela reservoir is the most influencing parameter in predicting the amount of sediment. [ABSTRACT FROM AUTHOR]

Rice is a fundamental staple food commodity all around the world. Globally, it is grown over 167 million hectares and occupies almost 1/5th of total cultivated land under cereals. With a total production of 782 million metric tons in 2018. In Pakistan, it is the 2nd largest crop being produced and 3rd largest food commodity after sugarcane and rice. The stem borers a type of pest in rice and other crops, Scirpophaga incertulas or the yellow stem borer is very serious pest and a major cause of yield loss, more than 90% damage is recorded in Pakistan on rice crop. Yellow stem borer population of rice could be stimulated with various environmental factors which includes relative humidity, light, and environmental temperature. Focus of this study is to find the environmental factors changes i.e., temperature, relative humidity and rainfall that can lead to cause outbreaks of yellow stem borers. this study helps to find out the hot spots of insect pest in rice field with a control of farmer's palm. Proposed system uses temperature, relative humidity, and rain sensor along with artificial neural network to predict yellow stem borer attack and generate warning to take necessary precautions. result shows 85.6% accuracy and accuracy gradually increased after repeating several training rounds. This system can be good IoT based solution for pest attack prediction which is cost effective and accurate. [ABSTRACT FROM AUTHOR]

Background and Aim: The Thalli sheep are the main breed of sheep in Pakistan, and an effective method to predict their body weight (BW) using linear body measurements has not yet been determined. Therefore, this study aims to establish an algorithm with the best predictive capability, among the Chi-square automatic interaction detector (CHAID), exhaustive CHAID, artificial neural network, and classification and regression tree (CART) algorithms, in live BW prediction using selected body measurements in female Pakistani Thalli sheep. Materials and Methods: A total of 152 BW records, including nine continuous predictors (wither height, body length [BL], head length, rump length, tail length, head width, rump width, heart girth [HG], and barrel depth), were utilized. The coefficient of determination (R2), standard deviation ratio, root-mean-square error (RMSE), etc., were calculated for each algorithm. Results: The R2 (%) values ranged from 49.28 (CART) to 64.48 (CHAID). The lowest RMSE was found for CHAID (2.61), and the highest one for CART (3.12). The most significant predictors were the HG of live BW for all algorithms. The heaviest average BW (41.12 kg) was observed in the subgroup of those having a BL of >73.91 cm (Adjusted p=0.045). Conclusion: Among the algorithms, CHAID provided the most appropriate predictive capability in the prediction of live BW for female Thalli sheep. In general, the applied algorithms accurately predicted the BW of Thalli sheep, which can be very helpful in deciding on the standards, available drug doses, and required feed amount for animals. [ABSTRACT FROM AUTHOR]

Current study was conducted in collaboration with Department of Computer Sciences and Engineering, University of Engineering and technology, Lahore, Pakistan during 2020. As a result, the focus of this work was on the development of a pre-harvest crop yield forecasting model based on soil and environmental parameters. These parameters were recorded on a monthly basis at National Tea & High Value Crops Research Institute (NTHRI) for a period of ten years. The parameters recorded were minimum and maximum temperature, humidity, rainfall, soil pH level, pesticide use, and labor expertise. To construct the feature set for model training, exploratory feature analysis, outlier analysis, feature scaling, feature transformation and feature selection using the ReliefF algorithm were performed. Through 10-fold cross validation, six regression algorithms were used for training and model evaluation. An ensemble of neural networks were used to build the final model. The ensemble were built using a novel method that trains several base learners with architectural and training data diversity. These trained models were ranked using the ReliefF algorithm and sequentially added to the ensemble until the validation performance stops improving. On the basis of four performance metrics, the final model was compared to the four best performing models. The proposed model provided mean averave error (MAE), mean squared error (MSE) and root mean squared error (RMSE) of 0.0942, 0.0145, and 0.1204, respectively, and an R-squared of 0.9461. The performance parameters were the best among the candidate models and were sufficient to justify its use as a tea crop yield prediction model. [ABSTRACT FROM AUTHOR]

Streamflow prediction is a significant undertaking for water resources planning and management. Accurate forecasting of streamflow always being a challenging task for the hydrologist due to its high stochasticity and dynamic patterns. Several traditional and the deep learning models have been applied to simulate the complex nature of the hydrological system. However, to develop and explore a better expert system for prediction is a continuous exertion for hydrological studies. In this study, a deep neural network, namely a one-dimensional convolutional neural network (1D-CNN) and extreme learning machine (ELM) are explored for one-step-ahead streamflow forecasting for three-time horizons (daily, weekly and monthly) in Gilgit River, Pakistan. The 1D-CNN model gained incredible popularity due to its state-of-the-art performance and nominal computational complexity; while ELM model performed superfast as compared to traditional/deep learning architecture, gives comparable performance with fast execution rate. A comparative analysis is presented to assess the performance of the 1D-CNN related to the ELM model. The performance measurement matrices defined as the correlation coefficient (R2), mean absolute error (MAE) and root mean square error (RMSE) computed between the observed and predicted streamflow to evaluate the 1D-CNN and ELM model efficacy. The results indicated that the ELM model performed relatively better than the 1D-CNN model based on predefined statistical measures in three-time scale. In numerical terms, the superiority of ELM over 1D-CNN model was demonstrated by R2 = 0.99, MAE = 18.8, RMSE = 50.14, and R2 = 0.97, MAE = 136.59, RMSE = 230.9, for daily streamflow (testing phase) respectively. Based on our findings, it can be concluded that the ELM model would be an alternative to the 1D-CNN model for highly accurate streamflow forecasting in mountainous regions of the world. [ABSTRACT FROM AUTHOR]

Research community has a growing interest in neural networks because of their practical applications in many fields for accurate modeling and prediction of the complex behavior of systems arising from engineering, economics, business, financial and metrological fields. Artificial neural networks (ANN) are very flexible function approximations tool used as universal modeling based on the separating of the past dynamics into clusters, in which we construct local models to capture the potential growth of the series depends on the previously known values. In this study, rain data of five major cities of Sindh province of Pakistan is considered, and summer rainfall of these five synoptic stations are statistically evaluated for prediction. The nonlinear autoregressive network with exogenous inputs (NARX) model for a time series is analyzed to evaluate the pattern of precipitation. We train a highly nonlinear NARX network model from randomly generated initial weights that converged to the best solution with the help of the Levenberg - Marquardt algorithm. A multi-step ahead NARX response time predictor is developed for rain forecasting. The performance of the NARX model is viable to capture nonlinear behavior with a high value of correlation coefficient R ranging from 0.70 to 0.99 for different synoptic stations. The results calculated using the proposed NARX neural network time series approach are accurate and reliable based on the coefficient of correlation and mean square error indices for rainfall forecasting. [ABSTRACT FROM AUTHOR]

Hypertension is becoming the major risk factor of the cardiovascular diseases. Hypertension is also known as "raised blood pressure" in medical terms. The main purpose of this study is to identify the potential risk factor of hypertension using chi-square test of association and also predict and classify the respondents into hypertension and healthy controls by Artificial Neural Network (ANN). A sample of 300 (150 cases and 150 controls) were selected using purposive sampling from Aziz Bhatti shaheed Hospital Gujrat, Pakistan. Data collected by interviewing the patients using well-designed questionnaire and checked their BP where cases considered as hypertension patients with (BP: ≥140/90) and healthy controls (BP: 120/80). High prevalence of hypertension is observed in males (54.7%) as compared to females (45.3%). Results also revealed that BMI, family income, age, extra salt intake and stress were the significant risk factors of hypertension in current study. ANN model showed good classification among hypertension and controls. It also predicted 98% chance of patient to have hypertension on the basis of age group 50-59 years, having normal weight, belongs to middle class family, presence of BP issue in family history, take stress and extra salt consumption in diet. [ABSTRACT FROM AUTHOR]

This study estimated energy use flow of buffalo farms, energy use indices, production efficiency, energy use targets, impact of energy inputs on energy output, and sensitivity analysis of energy inputs. A well-structured questionnaire was used to collect data of 360 domestic buffalo farms from Punjab Pakistan during May–July 2017. Results revealed that milk production was mainly dependent on renewable energy inputs, particularly millet, minerals, concentrates, and sorghum. Energy use efficiency (0.08) and production efficiency (0.24) indicate that energy inputs were overused. An artificial neural network (ANN) method suggested that 30.5% of total energy input could be saved if farmers followed the targeted inputs recommended by ANN. The Cobb-Douglas production function found a negative significant impact of sorghum, millet, and wheat straw; and positive significant impact of labour, concentrates, and electricity on energy output. Among the non-renewable energy sources, electricity was found to be the most wasteful use of energy input, mainly due to the mismanagement of farmers. Sensitivity analysis estimated that a unit increase in renewable energy significantly decreased milk yield by 0.02 unit. While a unit increase in non-renewable energy significantly increased milk yield by 0.01 unit. This study stresses the importance of using energy inputs at the target quantities prognosticated by ANN method, and recommends the use of energy-efficient equipment. • Energy mismanagement is common problem in livestock production system of Pakistan. • Artificial neural network (ANN) method was used to optimize energy inputs. • Renewable energy inputs were overused for milk production in the study area. • Electricity was overused by 28.2% mainly due to farmers' mismanagement. • ANN suggested 33% improvement in energy ratio by using target values of energy inputs. [ABSTRACT FROM AUTHOR]

Rapid urbanization is changing the existing patterns of Land Use Land Cover (LULC) globally which is consequently increasing the Land Surface Temperature (LST) in many regions. Present study was focused on estimating the current and simulating the future LULC and LST trends in the alpine environment of lower Himalayan region of Pakistan. Past patterns of LULC and LST were identified through the Support Vector Machine (SVM) and multi-spectral Landsat satellite images during 1987–2017 data period. The Cellular automata (CA) model and Artificial Neural Network (ANN) were applied to simulate future (years 2032 and 2047) LULC and LST changes, respectively, using their past patterns. CA model was validated for the simulated and the estimated LULC for the year 2017 with an overall Kappa (K) value of 0.77 using validation modules in QGIS and IDRISI software. ANN method was validated by correlating the observed and simulated LST for the year 2017 with correlation coefficient (R) and Mean Square Error (MSE) values of 0.81 and 0.51, respectively. Results indicated a change in the LULC and LST for instance the built-up area was increased by 4.43% while agricultural area and bare soil were reduced by 2.74% and 4.42%, respectively, from 1987 to 2017. The analysis of LST for different LULC classes indicated that built-up area has highest temperature followed by barren, agriculture and vegetation surfaces. Simulation of future LULC and LST showed that the built-up area will be increased by 2.27% (in 2032) and 4.13% (in 2047) which led 42% (in 2032) and 60% (in 2047) of the study area as compared to 26% area (in 2017) to experience LST greater than 27 °C. A strong correlation between built-up area changes and LST was thus found signifying major challenge to urban planners mitigating the consequent of Urban Heat Island (UHI) phenomenon. It is suggested that future urban planning should focus on urban plantation to counter UHI phenomena in the region of lower Himalayas. • Built-up area was increased by 4.43% from 1987 to 2017 in Abbottabad region. • Simulation indicated a further increase of 4.13% in built-up area by year 2047. • Area experiencing >27 °C surface temperature was increased by 26% over 1987 to 2017. • Simulation indicated an additional 34% area with >27 °C surface temperature in 2047. • Strong trends in land surface temperature may lead to Urban Heat Island phenomenon. [ABSTRACT FROM AUTHOR]

Arsenic contamination in the groundwater occurs in various parts of the world due to anthropogenic and natural sources, adversely affecting human health and ecosystems. The current study intends to examine the groundwater hydrogeochemistry containing elevated arsenic (As), predict As levels in groundwater, and determine the aptness of groundwater for drinking in the Vehari district, Pakistan. Four hundred groundwater samples from the study region were collected for physiochemical analysis. As levels in groundwater samples ranged from 0.1 to 52 μg/L, with an average of 11.64 μg/L, (43.5%), groundwater samples exceeded the WHO 2022 recommended limit of 10 μg/L for drinking purposes. Ion-exchange processes and the adsorption of ions significantly impacted the concentration of As. The HCO 3 - and Na + are the dominant ions in the study area, and the water types of samples were CaHCO 3 , mixed CaMgCl, and CaCl, demonstrating that rock-water contact significantly impacts hydrochemical behavior. The geochemical modeling indicated negative saturation indices with calcium carbonate and other salt minerals, encompassing aragonite, calcite, dolomite, and halite. The dissolution mechanism suggested that these minerals might have implications for the mobilization of As in groundwater. A combination of human-induced and natural sources of contamination was unveiled through principal component analysis (PCA). Artificial neural networks (ANN), random forest (RF), and logistic regression (LR) were used to predict As in the groundwater. The data have been divided into two parts for statistical analysis: 20% for testing and 80% for training. The most significant input variables for As prediction was determined using Chi-squared analysis. The receiver operating characteristic area under the curve and confusion matrix were used to evaluate the models; the RF, ANN, and LR accuracies were 0.89, 0.85, and 0.76. The permutation feature and mean decrease in impurity determine ten parameters that influence groundwater arsenic in the study region, including F - , Fe 2+ , K + , Mg 2+ , Ca 2+ , Cl - , SO 4 2- , NO 3 - , HCO 3 - , and Na + . The present study shows RF is the best model for predicting groundwater As contamination in the research area. The water quality index showed that 161 samples represent poor water, and 121 samples are unsuitable for drinking. Establishing effective strategies and regulatory measures is imperative in Vehari to ensure the sustainability of groundwater resources., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

In a developing country like Pakistan, where the electrical power demand is more than the generated power, maintaining the power system stability is a big challenge. In such cases it becomes, thus, essential to shed just the right amount of load to keep a power system stable. This paper presents a case study of Pakistan’s power system where the generated power, the load demand, frequency deviation and the load shed during a 24-h duration have been provided. The data have been analyzed using two techniques; the conventional artificial neural network (ANN) by implementing feed forward back propagation model and the Bootstrap aggregating or bagging algorithm. The simulation results reveal the superiority of the Bootstrap aggregating algorithm over a conventional ANN technique using feed forward back propagation model. [ABSTRACT FROM AUTHOR]

In the present study, using an artificial neural network (ANN), the thermal conductivity of nanofluid containing TiO 2 nanoparticles was modeled and investigated. The used experimental data to train and estimate the ANN are the thermal conductivity of the nanofluid at different volume fractions and temperatures. The ANN structure is considered with two hidden layers and five neurons for each hidden layer. To choose the best training function, several training functions available in the ANN are analyzed. The results reveal that by considering the criteria of the highest regression coefficient and the lowest mean square error (MSE), trainbr training function has the best performance with R= 99.9% 2%, MSE= 1.0211e-5 and train and test performances of 3.1198e-6 and 5.2791e-5. The maximum error of 2.5% for test data approximation in the process of k nf estimation shows the precision of designed ANN. The results also show that ANN can estimate the k nf of TiO 2 /water nanofluid (NF) at different temperatures and φ p with very good precision, and theoretical models like Pak and Cho are not able to precisely estimate the k nf. [Display omitted] [ABSTRACT FROM AUTHOR]

Real-time monitoring of fruit ripeness in storage and during logistics allows traders to minimize the chances of financial losses and maximize the quality of the fruit during storage through accurate prediction of the present condition of fruits. In Pakistan, banana production faces different difficulties from production, post-harvest management, and trade marketing due to atmosphere and mismanagement in storage containers. In recent research development, Wireless Sensor Networks (WSNs) are progressively under investigation in the field of fruit ripening due to their remote monitoring capability. Focused on fruit ripening monitoring, this paper demonstrates an Xbee-based wireless sensor nodes network. The role of the network architecture of the Xbee sensor node and sink end-node is discussed in detail regarding their ability to monitor the condition of all the required diagnosis parameters and stages of banana ripening. Furthermore, different features are extracted using the gas sensor, which is based on diverse values. These features are utilized for training in the Artificial Neural Network (ANN) through the Back Propagation (BP) algorithm for further data validation. The experimental results demonstrate that the projected WSN architecture can identify the banana condition in the storage area. The proposed Neural Network (NN) architectural design works well with selecting the feature data sets. It seems that the experimental and simulation outcomes and accuracy in banana ripening condition monitoring in the given feature vectors is attained and acceptable, through the classification performance, to make a better decision for effective monitoring of current fruit condition. [ABSTRACT FROM AUTHOR]

The accurate estimate of sediment load is important for management of the river ecosystem, designing of water infrastructures, and planning of reservoir operations. The direct measurement of sediment is the most credible method to estimate the sediments. However, this requires a lot of time and resources. Because of these two constraints, most often, it is not possible to continuously measure the daily sediments for most of the gauging sites. Nowadays, data-based sediment prediction models are famous for bridging the data gaps in the estimation of sediment loads. In data-driven sediment predictions models, the selection of input vectors is critical in determining the best structure of models for the accurate estimation of sediment yields. In this study, time series inputs of snow cover area, basin effective rainfall, mean basin average temperature, and mean basin evapotranspiration in addition to the flows were assessed for the prediction of sediment loads. The input vectors were assessed with artificial neural network (ANN), adaptive neuro-fuzzy logic inference system with grid partition (ANFIS-GP), adaptive neuro-fuzzy logic inference system with subtractive clustering (ANFIS-SC), adaptive neuro-fuzzy logic inference system with fuzzy c-means clustering (ANFIS-FCM), multiple adaptive regression splines (MARS), and sediment rating curve (SRC) models for the Gilgit River, the tributary of the Indus River in Pakistan. The comparison of different input vectors showed improvements in the prediction of sediments by using the snow cover area in addition to flows, effective rainfall, temperature, and evapotranspiration. Overall, the ANN model performed better than all other models. However, as regards sediment load peak time series, the sediment loads predicted using the ANN, ANFIS-FCM, and MARS models were found to be closer to the measured sediment loads. The ANFIS-FCM performed better in the estimation of peak sediment yields with a relative accuracy of 81.31% in comparison to the ANN and MARS models with 80.17% and 80.16% of relative accuracies, respectively. The developed multiple linear regression equation of all models show an R2 value of 0.85 and 0.74 during the training and testing period, respectively. [ABSTRACT FROM AUTHOR]

• The public policy of land allotment to agrarians has increased crop yield. • Non-agrarians have applied farm inputs more than target quantities. • Agrarians have used farm resources at targeted quantities. • Non-agricultural education is a reason for inefficient use of farm resources. • The public policy of land allotment is positively correlated to increase crop yield. The study estimates the impact of land allotment policy to agrarians on crop productivity in Punjab province of Pakistan. The term "agrarian" refers to a respondent who has formal education in agricultural sciences (agronomy, soil science, agricultrual economics, and horticulture etc.), otherwise known as "non-agrarian". In 2009 the Punjab government allotted agricultural plots to agrarians. A multistage sampling technique was used to collect data of 384 crop growers by using well-structured questionnaire from two main cop zones of Punjab province of Pakistan. From July to September 2017, the data of rice and sugarcane were collected from mixed crop zone, and cotton and wheat from cotton-wheat crop zone. Various econometric methods were used to approach study objectives. Results found that compared to non-agrarians, crop productivity of wheat, rice, cotton, and sugarcane was 28.9, 23.4, 19.8, and 8.3%, higher at agrarians' farms, respectively. Agricultural education and recommended use of farm inputs were main factors of getting higher crop yield at agrarians' farms. Artificial neural network (ANN) reported that at agrarian plots, farm inputs, particularly Pure N, Pure P, Pure K, and seed rate were used at recommended level for wheat, cotton, rice, and sugarcane crop. The overuse of Pure N and seed rate and underuse of Pure P, Pure K and farmyard manure (FYM) were found the main reasons for getting lower crop yield at non-agrarians' farms. Moreover, Cobb-Douglas (CD) production function evident that, although the farming experience of non-agrarians significantly increased the crop productivity, excessive use of Pure N and seed rate and non-agricultural education have no significant impact on crop yield. On the other hand, propensity score matching (PSM) method confirmed that public policy of land allotment to agrarians has significanlty increased the yield of wheat, rice, cotton, and sugarcane by 16.5, 14.2, 12.3, and 23.2%, respectively. The study results suggested that besides of land allotment to agrarians, the government should also focus on the provision of formal and informal agricultural education and training to non-agrarians. More specifically, it is necessary to provide knowledge of basic crop production technology to non-agrarians. Furthermore, to exploit maxim crop yield it is highly suggested to apply fertilizers at the recommend level estimated by the ANN method. [ABSTRACT FROM AUTHOR]

This study presents the main motivation to investigate the COVID-19 pandemic, a major threat to the whole world from the day when it first emerged in China city of Wuhan. Predictions on the number of cases of COVID-19 are crucial in order to prevent and control the outbreak. In this research study, an artificial neural network with rectifying linear unit-based technique is implemented to predict the number of deaths, recovered and confirmed cases of COVID-19 in Pakistan by using previous data of 137 days of COVID-19 cases from the day 25 February 2020 when the first two cases were confirmed, until 10 July 2020. The collected data were divided into training and test data which were used to test the efficiency of the proposed technique. Furthermore, future predictions have been made by the proposed technique for the next 7 days while training the model on whole available data.

Agriculture and forestry are the two major land use classes providing sustenance to the human population. With the pace of development, these two land use classes continue to change over time. Land use change is a dynamic process under the influence of multiple drivers including climate change. Therefore, tracing the trajectory of the changes is challenging. The artificial neural network (ANN) has successfully been applied for tracing such a dynamic process to capture nonlinear responses. We test the application of the multilayer perceptron neural network (MLP-NN) to project the future Agriculture, Forestry and Other Land Use (AFOLU) for the year 2050 for the South Asian Association for Regional Cooperation (SAARC) nations which is a geopolitical union of Afghanistan, Bangladesh, Bhutan, India, Nepal, Maldives, Pakistan and Sri Lanka. The Intergovernmental Panel on Climate Change (IPCC) and Food and Agriculture Organization (FAO) use much frequently the term 'AFOLU' in their policy documents. Hence, we restricted our land use classification scheme as AFOLU for assessing the influence of climate change scenarios of the IPCC fifth assessment report (RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5). Agricultural land would increase in all the SAARC nations, with the highest increase in Pakistan and Maldives; moderate increase in Afghanistan, India and Nepal; and the least increase in Bangladesh, Bhutan and Sri Lanka. The forestry land use will witness a decreasing trend under all scenarios in all of the SAARC nations with varying levels of changes. The study is expected to assist planners and policymakers to develop nations' specific strategy to proportionate land use classes to meet various needs on a sustainable basis.

The main aim of the study was to estimate the target values of agrochemical use, and its impact on crop productivity, and human health. Adoptions of protective measures and their impacts on human health during pesticide application were also evaluated. To fulfil the study objectives, using a standardised questionnaire, cross-sectional data of 480 rice growers were collected from September to October, 2017 from the Hafizabad and Sheikhupura districts of Punjab, Pakistan. Various econometric methods were used for data analysis. The artificial neural network (ANN) found an indiscriminate use of agrochemicals in the study area and suggested reducing the applied quantity of Pure N and pesticides by 43.6 and 52.6%, respectively, at a given level of rice yield, while the quantity of Pure P, Pure K, and farmyard manure (FYM) need to increase by 67.6, 15, and 21%, respectively, to obtain an efficient production level. The Cobb-Douglas (CD) production function estimated a positive and significant impact of Pure P, Pure K, FYM, education, and farming experience on rice yield. Results also showed that an indiscriminate use of pesticide not only compromised rice efficiency, it impinged on field worker health. Poisson regression found that occurrence of eye irritation, dizziness, cough, and nausea were significantly related with chemical application. Logistic regression found that education, farming experience, and extension services significantly increased in the adoption of various protective measures. Furthermore, cases of primary health exposures were significantly less among those who were used protective measures during pesticide application. Results of Poison regression function also confirmed that use of protective clothing, goggles, mask, gloves and boots during pesticide spraying significantly reduced human health exposures. It is recommended to apply the quantity of nutrients and chemicals suggested by ANN method. Use of biochemicals is a more sustainable and environmentally friendly strategy. Provision of educational programs and trainings to farmers on safe use of pesticides is required to avert occupational health exposures. Furthermore, governmental actions such as restriction and/or interdiction on use of highly toxic pesticides and enforcement to adopt safety measures during pesticide application, are needed to reduce pesticide exposures. Structure of ANN method. Image 1 • Unsafe use of agrochemicals increased the virulence of occupational health exposures. • Artificial neural network suggested to reduce overuse of agrochemicals in study area. • Use of personal protective devices was not common practice among farmers. • Education and farming experience significantly reduced occupational health exposures. • Adoption of safety measures significantly reduced the pesticide exposures. [ABSTRACT FROM AUTHOR]

Water resources planning, development, and management need reliable forecasts of river flows. In past few decades, an important dimension has been introduced in the prediction of the hydrologic phenomenon through artificial intelligence-based modeling. In this paper, the performance of three artificial neural network (ANN) and four support vector regression (SVR) models was investigated to predict streamflows in the Upper Indus River. Results from ANN models using three different optimization techniques, namely Broyden-Fletcher-Goldfarb-Shannon, Conjugate Gradient, and Back Propagation algorithms, were compared with one another. A further comparison was made between these ANNs and four types of SVR models which were based on linear, polynomial, radial basis function, and sigmoid kernels. Past 30 years' monthly data for precipitation, temperature, and streamflow obtained from Pakistan Surface Water Hydrology Department Lahore were used for this purpose. Three types of input combinations with respect to the main input variables (temperature, precipitation, and stream flow) and several types of input combinations with respect to time lag were tested. The best input for ANN and SVR models was identified using correlation coefficient analysis and genetic algorithm. The performance of the ANN and SVR models was evaluated by mean bias error, Nash-Sutcliffe efficiency, root mean square error, and correlation coefficient. The efficiency of the Broyden-Fletcher-Goldfarb-Shannon-ANN model was found to be much better than that of other models, while the SVR model based on radial basis function kernel predicted stream flows with comparatively higher accuracy than the other kernels. Finally, long-term predictions of streamflow have been made by the best ANN model. It was found that stream flow of Upper Indus River has a decreasing trend.