Women and Irrigation in Nepal: Context, Issues and Prospects

Occasional Papers in Sociology and Anthropology - Volume 9, 200

Predictive modelling of global solar radiation with artificial intelligence approaches using MODIS satellites and atmospheric reanalysis data for Australia

Global solar radiation (GSR) prediction is a prerequisite task for agricultural management and agronomic decisions, including photovoltaic (PV) power generation, biofuel exploration and several other bio-physical applications. Since short-term variabilities in the GSR incorporate stochastic and intermittent behaviours (such as periodic fluctuations, jumps and trends) due to the dynamicity of atmospheric variables, GSR predictions, as required for solar energy generation, is a challenging endeavour to satisfactorily predict the solar generated electricity in a PV system. Additionally, the solar radiation data, as required for solar energy monitoring purposes, are not available in all geographic locations due to the absence of meteorological stations and this is especially true for remote and regional solar powered sites. To surmount these challenges, the universally (and freely available) atmospheric gridded datasets (e.g., reanalysis and satellite variables) integrated into solar radiation predictive models to generate reliable GSR predictions can be considered as a viable medium for future solar energy exploration, utilisation and management. Hence, this doctoral thesis aims to design and evaluate novel Artificial Intelligence (AI; Machine Learning and Deep Learning) based predictive models for GSR predictions, using the European Centre for Medium Range Weather Forecasting (ECMWF) Interim-ERA reanalysis and Moderate Resolution Imaging Spectroradiometer (MODIS) Satellite variables enriched with ground-based weather station datasets for the prediction of both long-term (i.e., monthly averaged daily) as well as the short-term (i.e., daily and half-hourly) GSR. The focus of the study region is Queensland, the sunshine state, as well as a number of major solar cities in Australia where solar energy utilisation is actively being promoted by the Australian State and Federal Government agencies. Firstly, the Artificial Neural Networks (ANN), a widely used Machine Learning model is implemented to predict daily GSR at five different cities in Australia using ECMWF Reanalysis fields obtained from the European Centre for Medium Range Weather Forecasting repository. Secondly, the Self-Adaptive Differential Evolutionary Extreme Learning Machine (i.e., SaDE-ELM) is also proposed for monthly averaged daily GSR prediction trained with ECMWF reanalysis and MODIS satellite data from the Moderate Resolution Imaging Spectroradiometer. Thirdly, a three-phase Support Vector Regression (SVR; Machine Learning) model is developed to predict monthly averaged daily GSR prediction where the MODIS data are used to train and evaluate the model and the Particle Swarm Algorithm (PSO) is used as an input selection algorithm. The PSO selected inputs are further transformed into wavelet subseries via non-decimated Discrete Wavelet Transform to unveil the embedded features leading to a hybrid PSO-W-SVR model, seen to outperform the comparative hybrid models. Fourthly, to improve the accuracy of conventional techniques adopted for GSR prediction, Deep Learning (DL) approach based on Deep Belief Network (DBN) and Deep Neural Network (DNN) algorithms are developed to predict the monthly averaged daily GSR prediction using MODIS-based dataset. Finally, the Convolutional Neural Network (CNN) integrated with a Long Short-Term Memory Network (LSTM) model is used to construct a hybrid CLSTM model which is tested to predict the half-hourly GSR values over multiple time-step horizons (i.e., 1-Day, 1-Week, 2-Week, and 1-Month periods). Here, several statistical, Machine Learning and Deep Learning models are adopted to benchmark the proposed DNN and CLSTM models against conventional models (ANN, SaDE-ELM, SVR, DBN). In this doctoral research thesis, a Global Sensitivity Analysis method that attempts to utilise the Gaussian Emulation Machine (GEM-SA) algorithm is employed for a sensitivity analysis of the model predictors. Sensitivity analysis of selected predictors ascertains that the variables: aerosol, cloud, and water vapour parameters used as input parameters for GSR prediction play a significant role and the most important predictors are seen to vary with the geographic location of the tested study site. A suite of alternative models are also developed to evaluate the input datasets classified into El Niño, La Niña and the positive and negative phases of the Indian Ocean Dipole moment. This considers the impact of synoptic-scale climate phenomenon on long-term GSR predictions. A seasonal analysis of models applied at the tested study sites showed that proposed predictive models are an ideal tool over several other comparative models used for GSR prediction. This study also ascertains that an Artificial Intelligence based predictive model integrated with ECMWF reanalysis and MODIS satellite data incorporating physical interactions of the GSR (and its variability) with the other important atmospheric variables can be considered to be an efficient method to predict GSR. In terms of their practical use, the models developed can be used to assist with solar energy modelling and monitoring in solar-rich sites that have diverse climatic conditions, to further support cleaner energy utilization. The outcomes of this doctoral research program are expected to lead to new applications of Artificial Intelligence based predictive tools for GSR prediction, as these tools are able to capture the non-linear relationships between the predictor and the target variable (GSR). The Artificial Intelligence models can therefore assist climate adaptation and energy policymakers to devise new energy management devices not only for Australia but also globally, to enable optimal management of solar energy resources and promote renewable energy to combat current issues of climate change. Additionally, the proposed predictive models may also be applied to other renewable energy areas such as wind, drought, streamflow, flood and electricity demand for prediction

Characterization of Nepalese rice (Oryza sativa L.) landraces for qualitative traits

The characterization of rice (Oryza sativa L.) landraces enables to identify phenotypically unique variables which certainly aid in rice breeding program. So, an experiment was conducted in alpha designed to characterize 188 rice landraces from NAGRC (National Agriculture Genetic Resources center) Nepal for their qualitative agromorphologies in research farm of Agriculture and Forestry University (AFU), Rampur, Chitwan in 2020 AD. Twenty-nine qualitative variables viz; twelve leaf characters, six culm characters, four panicle character and seven grain characters were observed and 26 characters revealed diverse trait expressions for each variable in experimented 188 rice accessions. Two leaf characters namely ligule colour and flag leaf attitude for early observation and one grain character (stigma colour for early observation) showed no variation among studied rice accessions. The intensity of green colour of leaf blade, culm lodging resistance and culm habit, secondary branching of panicle, and lemma and palea colour, lemma apiculus colour and sterile lemma colour, elucidated the higher variation in studied characters. The distinction revealed in qualitative characters approves the presence of abundant phenotypic diversity in the landraces assemblage and that eventually signifies the efficient and effective utilization of landrace in rice breeding programs

Retrieval and Generative Approaches for a Pregnancy Chatbot in Nepali with Stemmed and Non-Stemmed Data : A Comparative Study

The field of Natural Language Processing which involves the use of artificial intelligence to support human languages has seen tremendous growth due to its high-quality features. Its applications such as language translation, chatbots, virtual assistants, search autocomplete, and autocorrect are widely used in various domains including healthcare, advertising, customer service, and target advertising. To provide pregnancy-related information a health domain chatbot has been proposed and this work explores two different NLP-based approaches for developing the chatbot. The first approach is a multiclass classification-based retrieval approach using BERTbased multilingual BERT and multilingual DistilBERT while the other approach employs a transformer-based generative chatbot for pregnancy-related information. The performance of both stemmed and non-stemmed datasets in Nepali language has been analyzed for each approach. The experimented results indicate that BERT-based pre-trained models perform well on non-stemmed data whereas scratch transformer models have better performance on stemmed data. Among the models tested the DistilBERT model achieved the highest training and validation accuracy and testing accuracy of 0.9165 on the retrieval-based model architecture implementation on the non-stemmed dataset. Similarly, in the generative approach architecture implementation with transformer 1 gram BLEU and 2 gram BLEU scores of 0.3570 and 0.1413 respectively were achieved.Comment: 7 pages, 5 figures, 4 tables. In proceedings of the International Conference on Technologies for Computer, Electrical, Electronics & Communication (ICT-CEEL 2023), Bhaktapur, Nepa

Optimization of windspeed prediction using an artificial neural network compared with a genetic programming model

The precise prediction of windspeed is essential in order to improve and optimize wind power prediction. However, due to the sporadic and inherent complexity of weather parameters, the prediction of windspeed data using different patterns is difficult. Machine learning (ML) is a powerful tool to deal with uncertainty and has been widely discussed and applied in renewable energy forecasting. In this chapter, the authors present and compare an artificial neural network (ANN) and genetic programming (GP) model as a tool to predict windspeed of 15 locations in Queensland, Australia. After performing feature selection using neighborhood component analysis (NCA) from 11 different metrological parameters, seven of the most important predictor variables were chosen for 85 Queensland locations, 60 of which were used for training the model, 10 locations for model validation, and 15 locations for the model testing. For all 15 target sites, the testing performance of ANN was significantly superior to the GP model

Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction

Solar energy predictive models designed to emulate the long-term (e.g., monthly) global solar radiation (GSR) trained with satellite-derived predictors can be employed as decision tenets in the exploration, installation and management of solar energy production systems in remote and inaccessible solar-powered sites. In spite of a plethora of models designed for GSR prediction, deep learning, representing a state-of-the-art intelligent tool, remains an attractive approach for renewable energy exploration, monitoring and forecasting. In this paper, algorithms based on deep belief networks and deep neural networks are designed to predict long-term GSR. Deep learning algorithms trained with publicly-accessible Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data are tested in Australia’s solar cities to predict the monthly GSR: single hidden layer and ensemble models. The monthly-scale MODIS-derived predictors (2003–2018) are adopted, with 15 diverse feature selection approaches including a Gaussian Emulation Machine for sensitivity analysis used to select optimal MODIS-predictor variables to simulate GSR against ground-truth values. Several statistical score metrics are adopted to comprehensively verify surface GSR simulations to ascertain the practicality of deep belief and deep neural networks. In the testing phase, deep learning models generate significantly lower absolute percentage bias (≤3%) and high Kling–Gupta efficiency (≥97.5%) values compared to the single hidden layer and ensemble model. This study ascertains that the optimal MODIS input variables employed in GSR prediction for solar energy applications can be relatively different for diverse sites, advocating a need for feature selection prior to the modelling of GSR. The proposed deep learning approach can be adopted to identify solar energy potential proactively in locations where it is impossible to install an environmental monitoring data acquisition instrument. Hence, MODIS and other related satellite-derived predictors can be incorporated for solar energy prediction as a strategy for long-term renewable energy exploration

GPS observations of ionospheric TEC variations over Nepal during 22 July 2009 solar eclipse

As the study of ionospheric behavior during various solar activities is an important task, various studies of ionospheric changes during eclipse events have been widely performed in the different regions of the globe. This paper investigates the ionospheric responses to the solar eclipse on 22 July 2009 over Nepal using the total electron content (TEC) measured by dual-frequency Global Positioning System (GPS) receivers. The time-averaged Vertical TEC (vTEC) of ten GPS stations from Nepal is analyzed and it is found that the value of ionospheric TEC decreases due to the reduction of ionizing radiation. In addition, the deviation in the TEC value on eclipse day from the mean vTEC value of the top five quietest days is found to lie in the range ~1–5 TECu at those regions which were associated with the partial eclipse shadow. On the other hand, the region with the total eclipse (BRN2 and RMTE) faced ~6–7 TECu on average reduction in the TEC value. Considering that the eclipse of 22 July 2009 occurred just at sunrise in the Nepalese zone, a maximum reduction of about 5 TECu is very significant. Higher deviation in TEC is therefore linked with the path of totality and the obscuration rate. This study reveals that the ionospheric TEC over Nepal was altered by wave-like energy and momentum transport, as well as obscuration of the solar disc due to the partial and total solar eclipse. Furthermore, the cross-correlation results presented similar type signatures of the eclipse-induced ionospheric modification over Nepal. This research work serves a crucial future reference for the comparative study of change of ionospheric TEC variability over the Nepal region during Eclipse event

Preparing faculty for problem-based learning curriculum at Patan Academy of Health Sciences, Nepal

Introduction: Patan Academy of Health Sciences (PAHS) in Nepal has adopted problem-based learning (PBL) as principal pedagogy to foster attributes predefined for its medical graduates. This study evaluates reaction of participants in PBL tutor-training program focused on PBL process and its assessment. Methods: An orientation program was organized separately for 24 faculty members and 45 higher secondary science majoring students prior to conduction of real-time PBL tutorial sessions. Faculty’s reaction as PBL tutors was collected before and after the orientation program using a 13-item self-administered questionnaire. Internal consistency reliability of the questionnaire items and outcome of the training program were assessed using Cronbach’s alpha, coefficient of variation, Shapiro-Wilk test, paired t-test and adjusted effect size for dependent samples. Results: The pre-test internal consistency reliability was high (0.89) whereas it was acceptable (0.69) for post-test. The average score increased from 26.50 to 34.55 and standard deviation decreased from 5.39 to 2.70 between pre- and post-test. Difference between post- and pre-tests total scores followed normal distribution and suitable parametric test (paired t-test) revealed the difference was highly significant (p< 0.0001). The adjusted effect size was high (1.65) for small dependent samples. Conclusions: The faculty training for PBL and assessment was helpful  in implementing PBL pedagogy at PAHS.  Keywords: Nepal, PAHS, Problem based learning, Process assessment, Tutor training program Â

Validating a problem-based learning process assessment tool in a Nepalese medical school

Introductions: The newly established Patan Academy of Health Sciences (PAHS) has incorporated the measurement of non-cognitive skills and behaviors into the summative assessment in the setting of problem based learning (PBL). This study was conducted to validate a PBL process assessment tool for PAHS.Methods: A list of 72 items of student behaviors observable in PBL tutorials was compiled from literature review. They were categorized under ten broad dimensions consistent with predefined PAHS Graduate Attributes. A series of PBL project committee meetings and expert inputs refined the list of 72 items to 47 and categorized them under eight dimensions. These 47 items, each with a 4-point rating scale, formed the Tutor Assessment of Student Tool (TAS-Tool). Twenty-four trained faculty members used the TAS-Tool to evaluate the performance of 41 senior high school students in PBL tutorials. Results: The internal-consistency of the TAS-Tool was very high rona’s .. eoal of to inonsistent ites furter increased it to 0.975. Principal components analysis with varimax rotation applied to the remaining 45 items gave seven components and explained 69.47% of the variation between the components. These seven components (% variation) were: Immersed in the Tutorial Process (20.16%); Professional (12.71%); Communicator and Team Leader (11.25%); Critical Thinker (8.77%); Reflector (6.22%); Creative (5.95%), and Sensitive (4.41%).Conclusions: TAS-Tool was found to be reliable and valid instrument deemed applicable in formative PBL process assessment at PAHS starting with the pioneer cohort of medical students. Further validation of TASTool through longitudinal study with PAHS students is required for summative purpose.Keywords: factor analysis, problem based learning, summative assessment, tool validation, Nepa

Hybrid deep CNN-SVR algorithm for solar radiation prediction problems in Queensland, Australia

This study proposes a new hybrid deep learning (DL) model, the called CSVR, for Global Solar Radiation (GSR) predictions by integrating Convolutional Neural Network (CNN) with Support Vector Regression (SVR) approach. First, the CNN algorithm is used to extract local patterns as well as common features that occur recurrently in time series data at different intervals. Then, the SVR is subsequently adopted to replace the fully connected CNN layers to predict the daily GSR time series data at six solar farms in Queensland, Australia. To develop the hybrid CSVR model, we adopt the most pertinent meteorological variables from Global Climate Model and Scientific Information for Landowners database. From a pool of Global Climate Models variables and ground-based observations, the optimal features are selected through a metaheuristic Feature Selection algorithm, an Atom Search Optimization method. The hyperparameters of the proposed CSVR are optimized by mean of the HyperOpt method, and the overall performance of the objective algorithm is benchmarked against eight alternative DL methods, and some of the other Machine Learning approaches (LSTM, DBN, RBF, BRF, MARS, WKNNR, GPML and M5TREE) methods. The results obtained shows that the proposed CSVR model can offer several predictive advantages over the alternative DL models, as well as the conventional ML models. Specifically, we note that the CSVR model recorded a root mean square error/mean absolute error ranging between 2.172–3.305 MJ m2/1.624–2.370 MJ m2 over the six tested solar farms compared to 2.514–3.879 MJ m2/1.939–2.866 MJ m2 from alternative ML and DL algorithms. Consistent with this predicted error, the correlation between the measured and the predicted GSR, including the Willmott’s, Nash-Sutcliffe’s coefficient and Legates & McCabe’s Index was relatively higher for the proposed CSVR model compared to other DL and Machine Learning methods for all of the study sites. Accordingly, this study advocates the merits of CSVR model to provide a viable alternative to accurately predict GSR for renewable energy exploitation, energy demand or other forecasting-based applications