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The accelerating impact of climate change on golf course conditions has led to a significant increase in pesticide dependency, underscoring the importance of innovative management strategies. The shift from Coupled Model Intercomparison Project Phase 5 (CMIP5) to the latest CMIP6 phase has drawn the attention of professionals, including engineers, decision makers, and golf course managers. This study evaluates how climate projections from CMIP6, using Canadian Earth System Models (CanESM2 and CanESM5), impact pesticide application trends on Quebec's golf courses. Through the comparison of temperature and precipitation projections, it was found that a more substantial decline in precipitation is exhibited by CanESM2 compared to CanESM5, while the latter projects higher temperature increases. A comparison between historical and projected pesticide use revealed that, in most scenarios and projected periods, the projected pesticide use was substantially higher, surpassing past usage levels. Additionally, in comparing the two climate change models, CanESM2 consistently projected higher pesticide use across various scenarios and projected periods, except for RCP2.6, which was 27% lower than SSP1-2.6 in the second projected period (PP2). For all commonly used pesticides, the projected usage levels in every projected period, according to climate change models, surpass historical levels. When comparing the two climate models, CanESM5 consistently forecasted greater pesticide use for fungicides, with a difference ranging from 65% to 222%, and for herbicides, with a difference ranging from 114% to 247%, across all projected periods. In contrast, insecticides, growth regulators, and rodenticides displayed higher AAIR values in CanESM2 during PP1 and PP3, showing a difference of 28% to 35.6%. However, CanESM5 again projected higher values in PP2, with a difference of 1.5% to 14%. [ABSTRACT FROM AUTHOR]

Accurate precipitation intensity forecasting is crucial for effective flood management and early warning systems. This study evaluates the performances of convolutional neural network (CNN) and long short-term memory (LSTM) models in predicting hourly precipitation intensity using data from Sainte Catherine de la Jacques Cartier station near Québec City. The models predict precipitation levels from one to six hours ahead, which are categorized into slight, moderate, heavy, and very heavy precipitation intensities. Our methodology involved gathering hourly precipitation data, defining input combinations for multistep ahead forecasting, and employing CNN and LSTM models. The performances of these models were assessed through qualitative and quantitative evaluations. The key findings reveal that the LSTM model excelled in the short-term (1HA to 2HA) and long-term (3HA to 6HA) forecasting, with higher R2 (up to 0.999) and NSE values (up to 0.999), while the CNN model was more computationally efficient, with lower AICc values (e.g., −16,041.1 for 1HA). The error analysis shows that the CNN demonstrated higher precision in the heavy and very heavy categories, with a lower relative error, whereas the LSTM performed better for the slight and moderate categories. The LSTM outperformed the CNN in minor- and high-intensity events, but the CNN exhibited a better performance for significant precipitation events with shorter lead times. Overall, both models were adequate, with the LSTM providing better accuracy for extended forecasts and the CNN offering efficiency for immediate predictions, highlighting their complementary roles in enhancing early warning systems and flood management strategies. [ABSTRACT FROM AUTHOR]

Bulk density is among the fundamental properties for defining quality attributes of dehydrated foods. Mathematical models offer elegant tools for predicting bulk density and therefore optimizing dehydration processes. In this contribution, three machine learning techniques, namely extreme learning machine (ELM), support vector machine (SVM), and evolutionary polynomial regression (EPR), were assessed for predicting bulk density. Comprehensive experimental data including eight variables (product, initial moisture content, initial bulk density, pretreatment, technology, temperature, pressure, and microwave power) from 191 datasets were used to feed the models. All possible combinations of input variables were examined with ELM to obtain the optimum models involving three to eight input variables. The latter were ranked from one to eight according to their impact on the performance of the models in predicting bulk density. The results showed that the product and drying technology are the essential variables for predicting bulk density. The minimum impact on predictions was observed for pretreatment and power. The initial moisture content was shown to be relevant for predicting the bulk density of freeze-dried products. In addition, the best models obtained by ELM were examined and compared with SVM and EPR models. The SVM showed the highest accuracy, but the EPR had the best performance when both the accuracy and simplicity of the models were considered simultaneously. The developed equations can be used as tools for predicting bulk density and exploited in optimizing dehydration processes without needing costly, time-consuming, and energy-demanding experiments. Opportunities and limitations of artificial intelligence have also been discussed. [ABSTRACT FROM AUTHOR]

This study addresses the critical issue of drought zoning in Canada using advanced deep learning techniques. Drought, exacerbated by climate change, significantly affects ecosystems, agriculture, and water resources. Canadian Drought Monitor (CDM) data provided by the Canadian government and ERA5-Land daily data were utilized to generate a comprehensive time series of mean monthly precipitation and air temperature for 199 sample locations in Canada from 1979 to 2023. These data were processed in the Google Earth Engine (GEE) environment and used to develop a Convolutional Neural Network (CNN) model to estimate CDM values, thereby filling gaps in historical drought data. The CanESM5 climate model, as assessed in the IPCC Sixth Assessment Report, was employed under four climate change scenarios to predict future drought conditions. Our CNN model forecasts CDM values up to 2100, enabling accurate drought zoning. The results reveal significant trends in temperature changes, indicating areas most vulnerable to future droughts, while precipitation shows a slow increasing trend. Our analysis indicates that under extreme climate scenarios, certain regions may experience a significant increase in the frequency and severity of droughts, necessitating proactive planning and mitigation strategies. These findings are critical for policymakers and stakeholders in designing effective drought management and adaptation programs. [ABSTRACT FROM AUTHOR]

Droughts have extensive consequences, affecting the natural environment, water quality, public health, and exacerbating economic losses. Precise drought forecasting is essential for promoting sustainable development and mitigating risks, especially given the frequent drought occurrences in recent decades. This study introduces the Improved Outlier Robust Extreme Learning Machine (IORELM) for forecasting drought using the Multivariate Standardized Drought Index (MSDI). For this purpose, four observation stations across British Columbia, Canada, were selected. Precipitation and soil moisture data with one up to six lags are utilized as inputs, resulting in 12 variables for the model. An exhaustive analysis of all potential input combinations is conducted using IORELM to identify the best one. The study outcomes emphasize the importance of incorporating precipitation and soil moisture data for accurate drought prediction. IORELM shows promising results in drought classification, and the best input combination was found for each station based on its results. While high Area Under Curve (AUC) values across stations, a Precision/Recall trade-off indicates variable prediction tendencies. Moreover, the F1-score is moderate, meaning the balance between Precision, Recall, and Classification Accuracy (CA) is notably high at specific stations. The results show that stations near the ocean, like Pitt Meadows, have higher predictability up to 10% in AUC and CA compared to inland stations, such as Langley, which exhibit lower values. These highlight geographic influence on model performance. [ABSTRACT FROM AUTHOR]