Streamflow prediction using an integrated methodology based on convolutional neural network and long short‑term memory networks

Streamflow (Qflow) prediction is one of the essential steps for the reliable and robust water resources planning and management. It is highly vital for hydropower operation, agricultural planning, and flood control. In this study, the convolution neural network (CNN) and Long-Short-term Memory network (LSTM) are combined to make a new integrated model called CNN-LSTM to predict the hourly Qflow (short-term) at Brisbane River and Teewah Creek, Australia. The CNN layers were used to extract the features of Qflow time-series, while the LSTM networks use these features from CNN for Qflow time series prediction. The proposed CNN-LSTM model is benchmarked against the standalone model CNN, LSTM, and Deep Neural Network models and several conventional artificial intelligence (AI) models. Qflow prediction is conducted for different time intervals with the length of 1-Week, 2-Weeks, 4-weeks, and 9-Months, respectively. With the help of different performance metrics and graphical analysis visualization, the experimental results reveal that with small residual error between the actual and predicted Qflow, the CNN-LSTM model outperforms all the benchmarked conventional AI models as well as ensemble models for all the time intervals. With 84% of Qflow prediction error below the range of 0.05 m3 s− 1, CNN-LSTM demonstrates a better performance compared to 80% and 66% for LSTM and DNN, respectively. In summary, the results reveal that the proposed CNN-LSTM model based on the novel framework yields more accurate predictions. Thus, CNN-LSTM has significant practical value in Qflow prediction

Fluvial bedload transport modelling: advanced ensemble tree-based models or optimized deep learning algorithms?

The potential of advanced tree-based models and optimized deep learning algorithms to predict fluvial bedload transport was explored, identifying the most flexible and accurate algorithm, and the optimum set of readily available and reliable inputs. Using 926 datasets for 20 rivers, the performance of three groups of models was tested: (1) standalone tree-based models Alternating Model Tree (AMT) and Dual Perturb and Combine Tree (DPCT); (2) ensemble tree-based models Iterative Absolute Error Regression (IAER), ensembled with AMT and DPCT; and (3) optimized deep learning models Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) ensembled with Grey Wolf Optimizer. Comparison of the predictive performance of the models with that of commonly used empirical equations and sensitivity analysis of the driving variables revealed that: (i) the coarse grain-size percentile D90 was the most effective variable in bedload transport prediction (where Dx is the xth percentile of the bed surface grain size distribution), followed by D84, D50, flow discharge, D16, and channel slope and width; (ii) all tree-based models and optimized deep learning algorithms displayed ‘very good’ or ‘good’ performance, outperforming empirical equations; and (iii) all algorithms performed best when all input parameters were used. Thus, a range of different input variable combinations must be considered in the optimization of these models. Overall, ensemble algorithms provided more accurate predictions of bedload transport than their standalone counterpart. In particular, the ensemble tree-based model IAER-AMT performed most strongly, displaying great potential to produce robust predictions of bedload transport in coarse-grained rivers based on a few readily available flow and channel variables

Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms

Proximal sensing techniques can potentially survey soil and crop variables responsible for variations in crop yield. The full potential of these precision agriculture technologies may be exploited in combination with innovative methods of data processing such as machine learning (ML) algorithms for the extraction of useful information responsible for controlling crop yield. Four ML algorithms, namely linear regression (LR), elastic net (EN), k-nearest neighbor (k-NN), and support vector regression (SVR), were used to predict potato (Solanum tuberosum) tuber yield from data of soil and crop properties collected through proximal sensing. Six fields in Atlantic Canada including three fields in Prince Edward Island (PE) and three fields in New Brunswick (NB) were sampled, over two (2017 and 2018) growing seasons, for soil electrical conductivity, soil moisture content, soil slope, normalized-difference vegetative index (NDVI), and soil chemistry. Data were collected from 39–40 30 × 30 m2 locations in each field, four times throughout the growing season, and yield samples were collected manually at the end of the growing season. Four datasets, namely PE-2017, PE-2018, NB-2017, and NB-2018, were then formed by combing data points from three fields to represent the province data for the respective years. Modeling techniques were employed to generate yield predictions assessed with different statistical parameters. The SVR models outperformed all other models for NB-2017, NB-2018, PE-2017, and PE-2018 dataset with RMSE of 5.97, 4.62, 6.60, and 6.17 t/ha, respectively. The performance of k-NN remained poor in three out of four datasets, namely NB-2017, NB-2018, and PE-2017 with RMSE of 6.93, 5.23, and 6.91 t/ha, respectively. The study also showed that large datasets are required to generate useful results using either model. This information is needed for creating site-specific management zones for potatoes, which form a significant component for food security initiatives across the globe

Wild Blueberry Harvesting Losses Predicted with Selective Machine Learning Algorithms

The production of wild blueberries (Vaccinium angustifolium) contributes 112.2 million dollars yearly to Canada’s revenue, which can be further increased by reducing harvest losses. A precise prediction of blueberry harvest losses is necessary to mitigate such losses. The performance of three machine learning (ML) algorithms was assessed to predict the wild blueberry harvest losses on the ground. The data from four commercial fields in Atlantic Canada (including Tracadie, Frank Webb, Small Scott, and Cooper fields) were utilized to achieve the goal. Wild blueberry losses (fruit loss on ground, leaf losses, blower losses) and yield were measured manually from randomly selected plots during mechanical harvesting. The plant height of wild blueberry, field slope, and fruit zone readings were collected from each of the plots. For the purpose of predicting ground loss as a function of fruit zone, plant height, fruit production, slope, leaf loss, and blower damage, three ML models i.e., support vector regression (SVR), linear regression (LR), and random forest (RF)—were used. Statistical parameters i.e., mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2), were used to assess the prediction accuracy of the models. The results of the correlation matrices showed that the blueberry yield and losses (leaf loss, blower loss) had medium to strong correlations accessed based on the correlation coefficient (r) range 0.37–0.79. The LR model showed the foremost predictions of ground loss as compared to all the other models analyzed. Tracadie, Frank Webb, Small Scott, and Cooper had R2 values of 0.87, 0.91, 0.91, and 0.73, respectively. Support vector regression performed comparatively better at all the fields i.e., R2 = 0.93 (Frank Webb field), R2 = 0.88 (Tracadie), and R2 = 0.79 (Cooper) except Small Scott field with R2 = 0.07. When comparing the actual and anticipated ground loss, the SVR performed best (R2 = 0.79–0.93) as compared to the other two algorithms i.e., LR (R2 = 0.73 to 0.92), and RF (R2 = 0.53 to 0.89) for the three fields. The outcomes revealed that these ML algorithms can be useful in predicting ground losses during wild blueberry harvesting in the selected fields

A Review on the Water Dimensions, Security, and Governance for Two Distinct Regions

Non-arid region countries, including Canada, enjoy abundant water resources, while arid countries such as Qatar struggle to meet their water needs. However, climate change threats to water resources are similar for both climatic regions. Therefore, this article discusses water dimensions, security, and governance for these different regions, i.e., non-arid Canada and arid Qatar, that distinctly respond to their water-related challenges. Limitations of the article include lesser water-related literature availability for Qatar than for Canada. Canada’s water resources appear vulnerable to climate change as it is projected to face >0.6 °C above the global average of 1.6 °C for the 20th-century temperature. Qatar is extremely vulnerable to dust storms, and rising sea levels, with the maximum temperature approaching 50 °C during the summer, and flooding during the winter. The sustainable use of water resources needs to address social, economic, political, climate change, and environmental dimensions of water. Other than climate change impacts and high per capita consumption of water, Qatar faces challenges of a rise in population (~29 million as of now), acute shortage of freshwater from rainfall (~80 mm per annum), high evapotranspiration (~95% of the total rainfall), depletion of groundwater, and low agricultural productivity due to infertile lands and water scarcity, all leading to food insecurity. The sustainable use of water resources requires improved regulations for water governance and management. Comparisons of water sustainability issues, dimensions, security, and governance facilitate discussions to improve water governance structures for resource sustainability, food security, and climate change adaptability, and show how one country could learn from the experiences of the other

Diffusion of technology and renewable energy in the G10 countries: A panel threshold analysis

The paper analyzes the threshold effect of technology innovation on renewable energy in the G10 countries through the panel threshold method. The outcome shows that technology innovation has a low impact on renewable energy when technology innovation is below the threshold value. However, technology innovation has a strong positive effect on renewable energy when the threshold value is above because of the expansion of spending on energy and technology. Moreover, digitalization makes renewable integration possible, analytic and artificial intelligence improve production. The findings explore that carbon emission has a negative impact on renewable energy. However, knowledge stocks, imported oil prices, economic growth, and electricity consumption positively affect renewable energy. The countries must develop more cost-effective, mature, and accessible renewable energy technology. Also, the focus should be on implementation instead of investing in existing infrastructure. A political commitment to phase out nuclear power and fossil fuels can improve the underwhelming performance

Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning

Precise estimation of physical hydrology components including groundwater levels (GWLs) is a challenging task, especially in relatively non-contiguous watersheds. This study estimates GWLs with deep learning and artificial neural networks (ANNs), namely a multilayer perceptron (MLP), long short term memory (LSTM), and a convolutional neural network (CNN) with four different input variable combinations for two watersheds (Baltic River and Long Creek) in Prince Edward Island, Canada. Variables including stream level, stream flow, precipitation, relative humidity, mean temperature, evapotranspiration, heat degree days, dew point temperature, and evapotranspiration for the 2011–2017 period were used as input variables. Using a hit and trial approach and various hyperparameters, all ANNs were trained from scratched (2011–2015) and validated (2016–2017). The stream level was the major contributor to GWL fluctuation for the Baltic River and Long Creek watersheds (R2 = 50.8 and 49.1%, respectively). The MLP performed better in validation for Baltic River and Long Creek watersheds (RMSE = 0.471 and 1.15, respectively). Increased number of variables from 1 to 4 improved the RMSE for the Baltic River watershed by 11% and for the Long Creek watershed by 1.6%. The deep learning techniques introduced in this study to estimate GWL fluctuations are convenient and accurate as compared to collection of periodic dips based on the groundwater monitoring wells for groundwater inventory control and management

Mitigation of Greenhouse Gas Emissions from Agricultural Fields through Bioresource Management

Efficient bioresource management can alter soil biochemistry and soil physical properties, leading to reduced greenhouse gas (GHG) emissions from agricultural fields. The objective of this study was to evaluate the role of organic amendments including biodigestate (BD), biochar (BC), and their combinations with inorganic fertilizer (IF) in increasing carbon sequestration potential and mitigation of GHG emissions from potato (Solanum tuberosum) fields. Six soil amendments including BD, BC, IF, and their combinations BDIF and BCIF, and control (C) were replicated four times under a completely randomized block design during the 2021 growing season of potatoes in Prince Edward Island, Canada. An LI-COR gas analyzer was used to monitor emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) from treatment plots. Analysis of variance (ANOVA) results depicted higher soil moisture-holding capacities in plots at relatively lower elevations and comparatively lesser volumetric moisture content in plots at higher elevations. Soil moisture was also impacted by soil temperature and rainfall events. There was a significant effect of events of data collection, i.e., the length of the growing season (p-value ≤ 0.05) on soil surface temperature, leading to increased GHG emissions during the summer months. ANOVA results also revealed that BD, BC, and BCIF significantly (p-value ≤ 0.05) sequestered more soil organic carbon than other treatments. The six experimental treatments and twelve data collection events had significant effects (p-value ≤ 0.05) on the emission of CO2. However, the BD plots had the least emissions of CO2 followed by BC plots, and the emissions increased with an increase in atmospheric/soil temperature. Results concluded that organic fertilizers and their combinations with inorganic fertilizers help to reduce the emissions from the agricultural soils and enhance environmental sustainability

A Mini-Review: Biowaste-Derived Fuel Pellet by Hydrothermal Carbonization Followed by Pelletizing

This review article focuses on recent studies using hydrothermal carbonization (HTC) for producing hydrochar and its potential application as a solid fuel pellet. Due to the depletion of fossil fuels and increasing greenhouse gas (GHG) emissions, the need for carbon-neutral fuel sources has increased. Another environmental concern relates to the massive amount of industrial processing and municipal solid waste, which are often underutilized and end up in landfills to cause further environmental damage. HTC is an appealing approach to valorizing wet biomass into valuable bioproducts (e.g., hydrochar), with improved properties. In this review, the effects of the main HTC reaction parameters, including reaction temperature, residence time, and feedstock to water ratio on the properties and yield of hydrochar are described. Following this, the pelletizing of hydrochar to prepare fuel pellets is discussed by reviewing the influences of applied pressure, processing time, pellet aspect ratio, moisture content of the hydrochar, and the type and dosage of binder on the quality of the resulting fuel pellet. Overall, this review can provide research updates and useful insights regarding the preparation of biowaste-derived solid fuel pellets

A Mini-Review: Biowaste-Derived Fuel Pellet by Hydrothermal Carbonization Followed by Pelletizing

This review article focuses on recent studies using hydrothermal carbonization (HTC) for producing hydrochar and its potential application as a solid fuel pellet. Due to the depletion of fossil fuels and increasing greenhouse gas (GHG) emissions, the need for carbon-neutral fuel sources has increased. Another environmental concern relates to the massive amount of industrial processing and municipal solid waste, which are often underutilized and end up in landfills to cause further environmental damage. HTC is an appealing approach to valorizing wet biomass into valuable bioproducts (e.g., hydrochar), with improved properties. In this review, the effects of the main HTC reaction parameters, including reaction temperature, residence time, and feedstock to water ratio on the properties and yield of hydrochar are described. Following this, the pelletizing of hydrochar to prepare fuel pellets is discussed by reviewing the influences of applied pressure, processing time, pellet aspect ratio, moisture content of the hydrochar, and the type and dosage of binder on the quality of the resulting fuel pellet. Overall, this review can provide research updates and useful insights regarding the preparation of biowaste-derived solid fuel pellets