Sustainability

Research

19 pages, 2974 KiB

Open AccessArticle

Using Probabilistic Machine Learning Methods to Improve Beef Cattle Price Modeling and Promote Beef Production Efficiency and Sustainability in Canada

by Elham Rahmani, Mohammad Khatami and Emma Stephens

Sustainability 2024, 16(5), 1789; https://doi.org/10.3390/su16051789 - 22 Feb 2024

Cited by 2 | Viewed by 1634

Abstract

Accurate agricultural commodity price models enable efficient allocation of limited natural resources, leading to improved sustainability in agriculture. Because of climate change, price volatility and uncertainty in the sector are expected to increase in the future, increasing the need for improved price modeling. [...] Read more.

Accurate agricultural commodity price models enable efficient allocation of limited natural resources, leading to improved sustainability in agriculture. Because of climate change, price volatility and uncertainty in the sector are expected to increase in the future, increasing the need for improved price modeling. With the emergence of machine learning (ML) algorithms, novel tools are now available to enhance the modeling of agricultural commodity prices. This research explores both univariate and multivariate ML techniques to perform probabilistic price prediction modeling for the Canadian beef industry, taking into account beef production, commodity markets, and international trade features to enhance accuracy. We model Alberta fed steer prices using three multivariate ML algorithms (support vector regression (SVR), random forest (RF), and Adaboost (AB)) and three univariate ML algorithms (autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), and the seasonal autoregressive integrated moving average with exogenous factors (SARIMAX)). We apply these models to monthly fed steer price data between January 2005 and September 2023 and compare predicted prices with observed prices using several validation metrics. The outcomes indicate that both random forest (RF) and Adaboost (AB) show superior overall performance in accurately predicting Alberta fed steer prices in comparison to other algorithms. To better account for the variance of the best model performance, we subsequently adopted a probabilistic approach by considering uncertainty in our best-selected ML model. The beef industry can use these improved price models to minimize resource waste and inefficiency in the sector and improve the long-term sustainability prospects for beef producers in Canada. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sustainability)

► Show Figures

Figure 1

18 pages, 4377 KiB

Open AccessArticle

A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification

by Hatef Dastour and Quazi K. Hassan

Sustainability 2023, 15(10), 7854; https://doi.org/10.3390/su15107854 - 11 May 2023

Cited by 10 | Viewed by 2972

Abstract

The pace of Land Use/Land Cover (LULC) change has accelerated due to population growth, industrialization, and economic development. To understand and analyze this transformation, it is essential to examine changes in LULC meticulously. LULC classification is a fundamental and complex task that plays [...] Read more.

The pace of Land Use/Land Cover (LULC) change has accelerated due to population growth, industrialization, and economic development. To understand and analyze this transformation, it is essential to examine changes in LULC meticulously. LULC classification is a fundamental and complex task that plays a significant role in farming decision making and urban planning for long-term development in the earth observation system. Recent advances in deep learning, transfer learning, and remote sensing technology have simplified the LULC classification problem. Deep transfer learning is particularly useful for addressing the issue of insufficient training data because it reduces the need for equally distributed data. In this study, thirty-nine deep transfer learning models were systematically evaluated alongside multiple deep transfer learning models for LULC classification using a consistent set of criteria. Our experiments will be conducted under controlled conditions to provide valuable insights for future research on LULC classification using deep transfer learning models. Among our models, ResNet50, EfficientNetV2B0, and ResNet152 were the top performers in terms of kappa and accuracy scores. ResNet152 required three times longer training time than EfficientNetV2B0 on our test computer, while ResNet50 took roughly twice as long. ResNet50 achieved an overall f1-score of 0.967 on the test set, with the Highway class having the lowest score and the Sea Lake class having the highest. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sustainability)

► Show Figures

Figure 1

14 pages, 6776 KiB

Open AccessArticle

Adaptive Intelligent Model Predictive Control for Microgrid Load Frequency

by Dong Zhao, Shuyan Sun, Ardashir Mohammadzadeh and Amir Mosavi

Sustainability 2022, 14(18), 11772; https://doi.org/10.3390/su141811772 - 19 Sep 2022

Cited by 4 | Viewed by 2266

Abstract

In this paper, self-tuning model predictive control (MPC) based on a type-2 fuzzy system for microgrid frequency is presented. The type-2 fuzzy system calculates the parameters and coefficients of the control system online. In the microgrid examined, there are sources of photovoltaic power [...] Read more.

In this paper, self-tuning model predictive control (MPC) based on a type-2 fuzzy system for microgrid frequency is presented. The type-2 fuzzy system calculates the parameters and coefficients of the control system online. In the microgrid examined, there are sources of photovoltaic power generation, wind, diesel, fuel cells (with a hydrogen electrolyzer), batteries and flywheels. In simulating the load changes, changes in the production capacity of solar and wind resources as well as changes (uncertainty) in all parameters of the microgrid are considered. The performances of three control systems including traditional MPC, self-tuning MPC based on a type-1 fuzzy system and self-tuning MPC based on a type-2 fuzzy system are compared. The results show that type-2 fuzzy MPC has the best performance, followed by type-1 fuzzy MPC, with a slight difference between the two results. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sustainability)

► Show Figures

Figure 1

12 pages, 698 KiB

Open AccessArticle

Effects of Temperature Rise on Clean Energy-Based Capital Market Investments: Neural Network-Based Granger Causality Analysis

by Shivam Swarup and Gyaneshwar Singh Kushwaha

Sustainability 2022, 14(18), 11163; https://doi.org/10.3390/su141811163 - 6 Sep 2022

Cited by 2 | Viewed by 1603

Abstract

During the past 20 years, due to climate change, the government and the private sector have significantly focused on relying on non-fossil fuel-based methods for their energy needs. Climate change-related events, such as unusual weather conditions, abnormal temperature spikes, etc., have an adverse [...] Read more.

During the past 20 years, due to climate change, the government and the private sector have significantly focused on relying on non-fossil fuel-based methods for their energy needs. Climate change-related events, such as unusual weather conditions, abnormal temperature spikes, etc., have an adverse influence on clean energy-based investments. In the given study, we intend to focus on how an incremental temperature rise could affect investors’ perceptions of clean energy assets. To understand the investor-based sentiment on climate change, we utilize prominent clean energy ETFs (exchange traded funds) and consider the temperature’s effect on them. The daily average temperatures of the three most dynamic international financial centers: New York, London and Tokyo, are taken as predictors. Deep learning-based neural networks are applied to understand both the linear and non-linear relationships between the desired variables and identify the causal effects. The results indicate that in almost all the cases with desired lags, there is some sort of non-linear causality, irrespective of linear causality effects. We hope this occurrence can help portfolio managers and environmental professionals in identifying novel climate change-related factors when considering the temperature-related risks. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sustainability)

► Show Figures

Figure 1

12 pages, 2764 KiB

Open AccessArticle

A New Deep Learning Restricted Boltzmann Machine for Energy Consumption Forecasting

by Aoqi Xu, Man-Wen Tian, Behnam Firouzi, Khalid A. Alattas, Ardashir Mohammadzadeh and Ebrahim Ghaderpour

Sustainability 2022, 14(16), 10081; https://doi.org/10.3390/su141610081 - 15 Aug 2022

Cited by 28 | Viewed by 2914

Abstract

A key issue in the desired operation and development of power networks is the knowledge of load growth and electricity demand in the coming years. Mid-term load forecasting (MTLF) has an important rule in planning and optimal use of power systems. However, MTLF [...] Read more.

A key issue in the desired operation and development of power networks is the knowledge of load growth and electricity demand in the coming years. Mid-term load forecasting (MTLF) has an important rule in planning and optimal use of power systems. However, MTLF is a complicated problem, and a lot of uncertain factors and variables disturb the load consumption pattern. This paper presents a practical approach for MTLF. A new deep learning restricted Boltzmann machine (RBM) is proposed for modelling and forecasting energy consumption. The contrastive divergence algorithm is presented for tuning the parameters. All parameters of RBMs, the number of input variables, the type of inputs, and also the layer and neuron numbers are optimized. A statistical approach is suggested to determine the effective input variables. In addition to the climate variables, such as temperature and humidity, the effects of other variables such as economic factors are also investigated. Finally, using simulated and real-world data examples, it is shown that for one year ahead, the mean absolute percentage error (MAPE) for the load peak is less than 5%. Moreover, for the 24-h pattern forecasting, the mean of MAPE for all days is less than 5%. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sustainability)

► Show Figures

Figure 1

18 pages, 1472 KiB

Open AccessArticle

The Sustainable Effect of Artificial Intelligence and Parental Control on Children’s Behavior While Using Smart Devices’ Apps: The Case of Saudi Arabia

by Othman Alrusaini and Hasan Beyari

Sustainability 2022, 14(15), 9388; https://doi.org/10.3390/su14159388 - 31 Jul 2022

Cited by 4 | Viewed by 4156

Abstract

The study was necessitated by the unprecedented consumption of smart devices by children in Saudi Arabia, which has been a concern to parents and other stakeholders. It investigated the way that game apps, social media apps, and video-streaming apps impact child development. It [...] Read more.

The study was necessitated by the unprecedented consumption of smart devices by children in Saudi Arabia, which has been a concern to parents and other stakeholders. It investigated the way that game apps, social media apps, and video-streaming apps impact child development. It also examined the roles played by artificial intelligence control and parental control in enhancing the sustainability of children’s behavior amidst smart technologies. The theories underpinning this research were the theory of reasoned action and the theory of planned behavior. The population of the study was 1,616,755 households in the eastern, western, and central regions. The researcher used an online survey to capture the sentiments of a sample of 415 parents who had given their children at least one smart device. The primary questionnaire focused on game apps, social media apps, video-streaming apps, artificial intelligence control, parental control, and the sustainability of child behavior. On the other hand, a separate questionnaire designed specifically to capture demographic information was also drafted. The structural equation model (SEM) was preferred, as it depicted the moderating roles of artificial intelligence control and parental control by using SPSS AMOS software. Findings established that games, social media, and video-streaming applications negatively affected the sustainability of child behavior. The findings presented in this paper show that the moderating effect of artificial intelligence control was more statistically significant than parental controls in influencing the sustainability of child behavior. Moreover, the results show that the greatest effect on children’s behavior were social media, video-streaming, and games apps. respectively. Nevertheless, both approaches resulted in positive child behavior. Hence, the study concluded that using artificial intelligence control is more effective than relying on parental controls to enhance the behavioral sustainability of children with smart device applications in Saudi Arabia. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sustainability)

► Show Figures

Figure 1

24 pages, 5753 KiB

Open AccessArticle

A New Clustering Method to Generate Training Samples for Supervised Monitoring of Long-Term Water Surface Dynamics Using Landsat Data through Google Earth Engine

by Alireza Taheri Dehkordi, Mohammad Javad Valadan Zoej, Hani Ghasemi, Ebrahim Ghaderpour and Quazi K. Hassan

Sustainability 2022, 14(13), 8046; https://doi.org/10.3390/su14138046 - 30 Jun 2022

Cited by 38 | Viewed by 3505

Abstract

Water resources are vital to the survival of living organisms and contribute substantially to the development of various sectors. Climatic diversity, topographic conditions, and uneven distribution of surface water flows have made reservoirs one of the primary water supply resources in Iran. This [...] Read more.

Water resources are vital to the survival of living organisms and contribute substantially to the development of various sectors. Climatic diversity, topographic conditions, and uneven distribution of surface water flows have made reservoirs one of the primary water supply resources in Iran. This study used Landsat 5, 7, and 8 data in Google Earth Engine (GEE) for supervised monitoring of surface water dynamics in the reservoir of eight Iranian dams (Karkheh, Karun-1, Karun-3, Karun-4, Dez, UpperGotvand, Zayanderud, and Golpayegan). A novel automated method was proposed for providing training samples based on an iterative K-means refinement procedure. The proposed method used the Function of the Mask (Fmask) initial water map to generate final training samples. Then, Support Vector Machines (SVM) and Random Forest (RF) models were trained with the generated samples and used for water mapping. Results demonstrated the satisfactory performance of the trained RF model with the samples of the proposed refinement procedure (with overall accuracies of 95.13%) in comparison to the trained RF with direct samples of Fmask initial water map (with overall accuracies of 78.91%), indicating the proposed approach’s success in producing training samples. The performance of three feature sets was also evaluated. Tasseled-Cap (TC) achieved higher overall accuracies than Spectral Indices (SI) and Principal Component Transformation of Image Bands (PCA). However, simultaneous use of all features (TC, SI, and PCA) boosted classification overall accuracy. Moreover, long-term surface water changes showed a downward trend in five study sites. Comparing the latest year’s water surface area (2021) with the maximum long-term extent showed that all study sites experienced a significant reduction (16–62%). Analysis of climate factors’ impacts also revealed that precipitation (

0.51 \leq R^{2} \leq 0.79

) was more correlated than the temperature (

0.22 \leq R^{2} \leq 0.39

) with water surface area changes. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sustainability)

► Show Figures

Figure 1

15 pages, 5306 KiB

Open AccessArticle

Wildfire Risk Forecasting Using Weights of Evidence and Statistical Index Models

by Ghafar Salavati, Ebrahim Saniei, Ebrahim Ghaderpour and Quazi K. Hassan

Sustainability 2022, 14(7), 3881; https://doi.org/10.3390/su14073881 - 25 Mar 2022

Cited by 25 | Viewed by 3081

Abstract

The risk of forest and pasture fires is one of the research topics of interest around the world. Applying precise strategies to prevent potential effects and minimize the occurrence of such incidents requires modeling. This research was conducted in the city of Sanandaj, [...] Read more.

The risk of forest and pasture fires is one of the research topics of interest around the world. Applying precise strategies to prevent potential effects and minimize the occurrence of such incidents requires modeling. This research was conducted in the city of Sanandaj, which is located in the west of the province of Kurdistan and the west of Iran. In this study, fire risk potential was assessed using weights of evidence (WoE) and statistical index (SI) models. Information about fire incidents in Sanandaj (2011–2020) was divided into two parts: educational data (2011–2017) and validation data (2018–2020). Factors considered for potential forest and rangeland fire risk in Sanandaj city included altitude, slope percentage, slope direction, distance from the road, distance from the river, land use/land cover (LULC), average annual rainfall, and average annual temperature. Finally, in order to validate the two models used, the receiver operating characteristic (ROC) curve was used. The results for the WoE and SI models showed that about 62.96% and 52.75% of the study area, respectively, were in the moderate risk to very high risk classes. In addition, the results of the ROC curve analysis showed that the WoE and SI models had area under the curve (AUC) values of 0.741 and 0.739, respectively. Although the input parameters for both models were the same, the WoE model showed a slightly higher AUC value compared to the SI model, and can potentially be used to predict future fire risk in the study area. The results of this study can help decision makers and managers take the necessary precautions to prevent forest and rangeland fires and/or to minimize fire damage. Full article

(This article belongs to the Special Issue Artificial Intelligence and Sustainability)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Artificial Intelligence and Sustainability

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI