Spatial mapping of short-term solar radiation prediction incorporating geostationary satellite images coupled with deep convolutional LSTM networks for South Korea

A practical approach to continuously monitor and provide real-time solar energy prediction can help support reliable renewable energy supply and relevant energy security systems. In this study on the Korean Peninsula, contemporaneous solar radiation images obtained from the Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) system, were used to design a convolutional neural network and a long short-term memory network predictive model, ConvLSTM. This model was applied to predict one-hour ahead solar radiation and spatially map solar energy potential. The newly designed ConvLSTM model enabled reliable prediction of solar radiation, incorporating spatial changes in atmospheric conditions and capturing the temporal sequence-to-sequence variations that are likely to influence solar driven power supply and its overall stability. Results showed that the proposed ConvLSTM model successfully captured cloud-induced variations in ground level solar radiation when compared with reference images from a physical model. A comparison with ground pyranometer measurements indicated that the short-term prediction of global solar radiation by the proposed ConvLSTM had the highest accuracy [root mean square error (RMSE) = 83.458 Wcenterdotm−2, mean bias error (MBE) = 4.466 Wcenterdotm−2, coefficient of determination (R2) = 0.874] when compared with results of conventional artificial neural network (ANN) [RMSE = 94.085 Wcenterdotm−2, MBE = −6.039 Wcenterdotm−2, R2 = 0.821] and random forest (RF) [RMSE = 95.262 Wcenterdotm−2, MBE = −11.576 Wcenterdotm−2, R2 = 0.839] models. In addition, ConvLSTM better captured the temporal variations in predicted solar radiation, mainly due to cloud attenuation effects when compared with two selected ground stations. The study showed that contemporaneous satellite images over short-term or near real-time intervals can successfully support solar energy exploration in areas without continuous environmental monitoring systems, where satellite footprints are available to model and monitor solar energy management systems supporting real-life power grid systems

Controlling factors of plant community composition with respect to the slope aspect gradient in the Qilian Mountains

Slope aspect can affect soil temperature and soil type distribution, which, in turn, is likely to influence plant community composition. Three Qilian mountains, located in the northeastern part of the Qinghai–Tibetan Plateau, China, with four distinct slope aspects including south‐facing (SF), southwest‐facing (SW), northwest‐facing (NW), and north‐facing (NF) slope aspects, were studied to investigate the impact of slope aspect on plant assemblages. The results indicated that the environmental conditions were favorable under the NF and NW slope aspects as the soil water, soil organic carbon (SOC), and soil total nitrogen (STN) contents were significantly higher, and soil temperature (ST) and soil bulk density (SBD) were significantly lower than under the SF and SW aspects. Under all slope aspects, however, SOC, STN, and soil total phosphate in the top 0.2 m of topsoil accounted for about 60% of its total quantity, to a soil depth of 0.6 m. The plant communities on the SF and SW slopes were found to be primarily composed of Poa pratensis, Potentilla anrisena, and Carex aridula. In contrast, the plant community on the NW slope was mainly composed of Kobresia humilis, Carex crebra, and Potentilla bifurca, while on the NF slope it was mainly composed of Picea crassifolia, Carex scabrirostris, and Polygonum macrophyllum. The order of the influence of environmental factors on species distributions was ST > SBD > sand > STN. Results suggest that the slope aspect has an important role in the regulation of the soil environment and plant assemblages and that ST and SBD were the main factors influencing plant community composition. Furthermore, evidence from this study suggests that these mountains will become increasingly vulnerable to global warming. Thus, the plant community composition on these mountains must be monitored continuously in order to allow for strategic adaptive management

Grassland degradation on the Qinghai-Tibetan Plateau: reevaluation of causative factors

In light of Harris (2010) finding insufficient evidence to assert a causal linkage between any of the seven previously proposed causative factors and grassland degradation on the Qinghai-Tibetan Plateau (QTP), more recent empirical studies on QTP grassland degradation were explored to ascertain whether, in fact, these factors are casually linked to grassland degradation. The mischaracterization of the underlying causes of grassland degradation among policymakers has and continues to be an obstacle to sustainable regional grassland management practices. Accumulating evidence suggests that privatization and sedentarization, small mammals, climate change, harsh environments, fragile soils, and overgrazing contribute to grassland degradation. However, neither obsolete livestock husbandry methods nor the recent conversion of rangelands to agriculture had a meaningful influence. Estimates of the total area of degraded grasslands and the establishment of grassland degradation criteria have not been properly addressed in the literature. Both omissions constitute the basis for investigating the causes of grassland degradation across the QTP and the adoption of measures to manage these grasslands sustainably

Design and construction of the MicroBooNE detector

This paper describes the design and construction of the MicroBooNE liquid argon time projection chamber and associated systems. MicroBooNE is the first phase of the Short Baseline Neutrino program, located at Fermilab, and will utilize the capabilities of liquid argon detectors to examine a rich assortment of physics topics. In this document details of design specifications, assembly procedures, and acceptance tests are reported

Stakeholders' frames and ecosystem service use in the context of a debate over rebuilding or removing a dam in New Brunswick, Canada

As many dams are starting to reach the end of their life spans, discussions about whether they should be retained or removed are becoming more common. Such debates are often controversial, but little is known about stakeholders' opinions about the issue. We use frame theory to describe how stakeholders perceive a decision on the future of the Mactaquac Dam in New Brunswick, Canada. Frames describe how people make sense of a situation by determining what is important and inside the frame, and what is outside the frame, based on their past experiences and knowledge. We explore whether the benefits that people realize from ecosystems (ecosystem services) influence their frames of dam removal. Based on interviews with 30 stakeholders, we found that participants who preferred to retain the dam aimed to prioritize the social and economic stability of the area, which relied on the ecosystem services provided by the dammed river. They emphasized the quality of the current ecosystem that has developed around the dam and preferred to avoid disturbing it. By contrast, those who preferred to remove the dam framed the decision as an opportunity to restore the ecology and social and economic activities that were present before the dam was built. These frames were influenced by participants' use of ecosystem services - both focus on the ecosystem services they use, while minimizing the benefits of others. Exploring frames allowed us to uncover the assumptions and biases implicit in their views, and identify topics for education campaigns as well as possible areas of agreement between parties. We conclude that ecosystem services are a relevant source of frames of a decision on a dam's future

Spatial and temporal scale framing of a decision on the future of the Mactaquac Dam in New Brunswick, Canada

Many large dams are coming to the end of their lifespans and decisions must be made about whether to rebuild/refurbish or remove them, which will have different implications across temporal and spatial scales. Such decisions are often controversial, but little is known about what drives differences in stakeholders' perspectives of them. Cognitive scale frames describe how people use scales in interpreting such an issue, including which of its elements they prioritize and which they minimize. Using interviews with 30 stakeholders and analysis of documents, we explored how stakeholders used spatial and temporal scales in their frames of a decision about whether to rebuild/refurbish or remove the Mactaquac Dam in New Brunswick, Canada. We found that stakeholders used multiple levels on spatial, hydrological, administrative, and temporal scales in their frames. Both those who wanted to retain the dam and those who wanted to remove it upscaled problems from local level to higher spatial levels, making problems seem widely shared and therefore legitimate. However, there were mismatches in the scales used: the retainers upscaled to the province on the administrative scale while the removers upscaled to the entire river on the hydrological scale. The results revealed the particular importance of temporal scale frames, particularly of the past, which have been little studied. Both groups framed problems as continuing into the future, but diverged strongly in how they framed various periods of the past as being relevant to understanding problems and their solutions in the present. Decision makers should be aware of differing scale frames when designing decision-making processes and conflict resolution efforts

Prediction of SPEI using MLR and ANN: a case study for Wilsons Promontory Station in Victoria

The prediction of drought is of major importance in climate-related studies, hydrologic engineering, wildlife or agricultural studies. This study explores the ability of two machine learning methods to predict 1, 3, 6 and 12 months standardized precipitation and evapotranspiration index (SPEI) for the Wilsons Promontory station in eastern Australia. The two methods are multiple linear regression (MLR) and artificial neural networks (ANN). The data-driven models were based on combinations of the input variables: mean precipitations, mean, maximum and minimum temperatures and evapotranspiration, for data between 1915 and 2012. Two performance metrics were used to compare the performance of the optimum MLR and ANN models: the coefficient of determination (R2) and the root mean square error (RMSE). It was found that ANN provided greater accuracy than MLR in forecasting the 1, 3, 6 and 12 months SPEI

Universally deployable extreme learning machines integrated with remotely sensed MODIS satellite predictors over Australia to forecast global solar radiation: a new approach

Global advocacy to mitigate climate change impacts on pristine environments, wildlife, ecology, and health has led scientists to design technologies that harness solar energy with remotely sensed, freely available data. This paper presents a study that designed a regionally adaptable and predictively efficient extreme learning machine (ELM) model to forecast long-term incident solar radiation (ISR) over Australia. The relevant satellite-based input data extracted from the Moderate Resolution Imaging Spectroradiometer (i.e., normalized vegetation index, land-surface temperature, cloud top pressure, cloud top temperature, cloud effective emissivity, cloud height, ozone and near infrared-clear water vapour), enriched by geo-temporal input variables (i.e., periodicity, latitude, longitude and elevation) are applied for a total of 41 study sites distributed approximately uniformly and paired with ground-based ISR (target). Of the 41 sites, 26 are incorporated in an ELM algorithm for the design of a universal model, and the remainder are used for model cross-validation. A universally-trained ELM (with training data as a global input matrix) is constructed, and the spatially-deployable model is applied at 15 test sites. The optimal ELM model is attained by trial and error to optimize the hidden layer activation functions for feature extraction and is benchmarked with competitive artificial intelligence algorithms: random forest (RF), M5 Tree, and multivariate adaptive regression spline (MARS). Statistical metrics show that the universally-trained ELM model has very good accuracy and outperforms RF, M5 Tree, and MARS models. With a distinct geographic signature, the ELM model registers a Legates & McCabe's Index of 0.555–0.896 vs. 0.411–0.858 (RF), 0.434–0.811 (M5 Tree), and 0.113–0.868 (MARS). The relative root-mean-square (RMS) error of ELM is low, ranging from approximately 3.715–7.191% vs. 4.907–10.784% (RF), 7.111–11.169% (M5 Tree) and 4.591–18.344% (MARS). Taylor diagrams that illustrate model preciseness in terms of RMS centred difference, error analysis, and boxplots of forecasted vs. observed ISR also confirmed the versatility of the ELM in generating forecasts over heterogeneous, remote spatial sites. This study ascertains that the proposed methodology has practical implications for regional energy modelling, particularly at national scales by utilizing remotely-sensed satellite data, and thus, may be useful for energy feasibility studies at future solar-powered sites. The approach is also important for renewable energy exploration in data-sparse or remote regions with no established measurement infrastructure but with a rich and viable satellite footprint

A new approach to predict daily pH in rivers based on the 'a trous' redundant wavelet transform algorithm

Prediction of pH is an important issue in managing water quality in surface waters (e.g., rivers, lakes) as well as drinking water. The capacity of artificial neural network (ANN), wavelet-artificial neural network (WANN), traditional multiple linear regression (MLR), and wavelet-multiple linear regression (WMLR) models to predict daily pH levels (1, 2, and 3 days ahead) at the Chattahoochee River gauging station (near Atlanta, GA, USA) was assessed. In the proposed WANN model, the original time series of pH and discharge (Q) were decomposed (after being split into training and testing series) into several sub-series by the the à trous (AT) wavelet transform algorithm. The wavelet coefficients were summed to obtain useful input time series for the ANN model to then develop the WANN model for pH prediction. The redundant à trous algorithm was used for data decomposition. Model implementation indicated the values of 1-day-ahead pH predicted by the WANN model closely matched the observed values (with a coefficient of determination, R2 = 0.956; Root Mean Square Error, RMSE = 0.019; and Mean Absolute Error, MAE = 0.015). It is therefore possible that the WANN model’s accuracy can be attributed to its better predictive ability (due to the use of the AT) to remove the noise caused by pH shifts (e.g., acid precipitation). Peak pH values predicted by the WANN model were also closer to observed values compared to the other machine learning model