DELEGATED LEGISLATION: A NECESSARY EVIL

Delegated legislation is becoming a necessary evil in modern democratic countries. Due to the concept of welfare state, there has been a quantitative increase in the function of the government. In such a situation, it is not possible that he cannot do the work of legislation in all the points himself. It becomes a compulsion for the government to delegate this power to other organs of the government. But while doing so it should be kept in mind that the institution to which this power is being delegated should use it very carefully. There may be no public discussion, no press criticism and no public opinion on it. The system thus becomes undemocratic giving rise to the danger that the government may misuse its powers. For this the traditional methods of control (parliamentary, administrative and judicial) should be further strengthened. Considering the importance of the subject, there is a need of separate enactment for it. In which clearly such facts should be included as to which subject can be delegated, which cannot be delegated, under which condition the delegation should be done. &nbsp

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

Filler powder free joining of SAF 2507 using selective microwave hybrid heating technique

Microwave Hybrid Heating (MHH) based joining of super duplex stainless steel (SAF 2507) with cross-sectionaldimensions 3.5 mm ×3 mm has been carried out for the first time by using a microwave applicator of 900 W operated at2.45 GHz for 400 s. Graphite rods have been used instead of traditionally used charcoal powder to serve as susceptormaterial. Graphite rods are good susceptor of microwave radiations therefore the presence of these rods helps in initiatingand carrying forward the joining process. Moreover, the melting temperature can be achieved for specimens underinvestigations through this novel process thereby eliminating the need of using any filler powder. Absence of filler powderreduces the process cost significantly. The joints have been mechanically characterized by Vickers micro-hardness andphysically characterized by Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) tests. It hasbeen observed through these tests that micro-hardness of the joints was more than the base alloy due to transfer of carbonfrom graphite rods to the joint zone

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

Deep Air Quality Forecasts: Suspended Particulate Matter Modeling With Convolutional Neural and Long Short-Term Memory Networks

Public health risks arising from airborne pollutants, e.g ., Total Suspended Particulate ( TSP ) matter, can significantly elevate ongoing and future healthcare costs. The chaotic behaviour of air pollutants posing major difficulties in tracking their three-dimensional movements over diverse temporal domains is a significant challenge in designing practical air quality systems. This research paper builds a deep learning hybrid CLSTM model where convolutional neural network ( CNN ) is amalgamed with the long short-term memory ( LSTM ) network to forecast hourly TSP . The CNN model entails a data processer including feature extractors that draw upon statistically significant antecedent lagged predictor variables, whereas the LSTM model encapsulates a new feature mapping scheme to predict the next hourly TSP value. The hybrid CLSTM model is comprehensively benchmarked and is seen to outperform an ensemble of five machine learning models. The efficacy of the CLSTM model is elucidated in model testing phase at study sites in Queensland, Australia. Using performance metrics, visual analysis of TSP simulations relative to observations, and detailed error analysis, this study ascertains the CLSTM model’s practical utility for air pollutant forecasting systems in health risk mitigation. This study captures a feasible opportunity to emulate air quality at relatively high temporal resolutions in global regions where air pollution is a considerable threat to public health

Designing Deep-based Learning Flood Forecast Model with ConvLSTM Hybrid Algorithm

Efficient, robust, and accurate early flood warning is a pivotal decision support tool that can help save lives and protect the infrastructure in natural disasters. This research builds a hybrid deep learning (ConvLSTM) algorithm integrating the predictive merits of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network to design and evaluate a flood forecasting model to forecast the future occurrence of flood events. Derived from precipitation dataset, the work adopts a Flood Index (IF), in form of a mathematical representation, to capture the gradual depletion of water resources over time, employed in a flood monitoring system to determine the duration, severity, and intensity of any flood situation. The newly designed predictive model utilizes statistically significant lagged IF, improved by antecedent and real-time rainfall data to forecast the next daily IF value. The performance of the proposed ConvLSTM model is validated against 9 different rainfall datasets in flood prone regions in Fiji which faces flood-driven devastations almost annually. The results illustrate the superiority of ConvLSTM-based flood model over the benchmark methods, all of which were tested at the 1-day, 3-day, 7-day, and the 14-day forecast horizon. For instance, the Root Mean Squared Error (RMSE) for the study sites were 0.101, 0.150, 0.211 and 0.279 for the four forecasted periods, respectively, using ConvLSTM model. For the next best model, the RMSE values were 0.105, 0.154, 0.213 and 0.282 in that same order for the four forecast horizons. In terms of the difference in model performance for individual stations, the Legate-McCabe Efficiency Index (LME) were 0.939, 0.898, 0.832 and 0.726 for the four forecast horizons, respectively. The results demonstrated practical utility of ConvLSTM in accurately forecasting IF and its potential use in disaster management and risk mitigation in the current phase of extreme weather events

Development of Flood Monitoring Index for daily flood risk evaluation: case studies in Fiji

Both fluvial and pluvial floods are a common occurrence in Fiji with fluvial floods causing significant economic consequences for island nations. To investigate flood risk and provide a mitigation tool on daily basis, the Flood Index (IF) is developed based on the rationale that the onset and severity of an event is based on current and antecedent day’s precipitation. This mathematical methodology considers the notion that the impact of daily cumulative precipitation on a particular flood event arising from a previous day’s precipitation, decreasing gradually over time due to the interaction of hydrological factors (e.g., evaporation, percolation, seepage, surface run-off, drainage, etc.,). These are accounted for, mathematically, by a time-reduction weighted precipitation influencing the magnitude of IF. Considering the duration, severity and intensity of all identified events, the applicability of IF is tested at 9 study sites in Fiji using 30-year precipitation datasets (1990–2019) obtained from Fiji Meteorological Services. Newly developed IF is adopted at flood prone sites, with results demonstrating that flood events were common throughout the country, mostly notable between November to April (or the wet season). Upon examining the variations in daily IF, the flood properties were determined, showing that the most severe events generally started in January. Flood events with the highest severity were recorded in Lautoka [IaccF (flood severity) ≈149.14, ImaxF (peak danger) ≈3.39, DF (duration of flood) ≈151days, tonset (onset date) =23rdJanuary2012], followed by Savusavu (IaccF≈141.65,ImaxF≈1.75,DF≈195days,tonset=27thNovember1999) and Ba (IaccF≈131.57,ImaxF≈3.13,DF≈113days,tonset=9thJanuary2009). The results clearly illustrate the practicality of daily IF in determining the duration, severity, and intensity of flood situation, as well as its potential application to small island nations. The use of daily IF to quantify flood events can therefore enable a cost-effective and innovative solution to study historical floods in both developing and first world countries. Our methodology is particularly useful to governments, private organizations, non-governmental organizations and communities to help develop community-amicable policy and strategic plans to prepare for flood impacts and undertake the necessary risk mitigation measures

Deep Multi-Stage Reference Evapotranspiration Forecasting Model: Multivariate Empirical Mode Decomposition Integrated With the Boruta-Random Forest Algorithm

Evapotranspiration, as a combination of evaporation and transpiration of water vapour, is a primary component of global hydrological cycles. It accounts for significant loss of soil moisture from the earth to the atmosphere. Reliable methods to monitor and forecast evapotranspiration are required for decision-making. Reference evapotranspiration, denoted as ET , is a major parameter that is useful in quantifying soil moisture in a cropping system. This article aims to design a multi-stage deep learning hybrid Long Short-Term Memory (LSTM) predictive model that is coupled with Multivariate Empirical Mode Decomposition (MEMD) and Boruta-Random Forest (Boruta) algorithms to forecast ET in the drought-prone regions ( i.e ., Gatton, Fordsdale, Cairns) of Queensland, Australia. Daily data extracted from NASA’s Goddard Online Interactive Visualization and Analysis Infrastructure (GIOVANNI) and Scientific Information for Land Owners (SILO) repositories over 2003–2011 are used to build the proposed multi-stage deep learning hybrid model, i.e ., MEMD-Boruta-LSTM, and the model’s performance is compared against competitive benchmark models such as hybrid MEMD-Boruta-DNN, MEMD-Boruta-DT, and a standalone LSTM, DNN and DT model. The test MEMD-Boruta-LSTM hybrid model attained the lowest Relative Root Mean Square Error (≤17%), Absolute Percentage Bias (≤12.5%)and the highest Kling-Gupta Efficiency (≥0.89%) relative to benchmark models for all study sites. The proposed multi-stage deep hybrid MEMD-Boruta-LSTM model also outperformed all other benchmark models in terms of predictive efficacy, demonstrating its usefulness in the forecasting of the daily ET dataset. This MEMD-Boruta-LSTM hybrid model could therefore be employed in practical environments such as irrigation management systems to estimate evapotranspiration or to forecast ET