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In 2017 the paper 'The Sustainable Development Oxymoron: Quantifying and Modelling the Incompatibility of Sustainable Development Goals' was published, showing that there is a conflict between socio-economic development goals and ecological sustainability goals using cross-country time-series data. The authors looked at production-based CO2 emissions to measure and model the 13th SDG goal addressing climate change. Their models showed that production-based CO2 emissions were stalling or even decreasing in rich countries, which suggests that other countries are also likely to see stalling and decrease in their CO2 emissions once they become rich. However, this conclusion can be challenged when accounting for consumption-based CO2 emissions rather than production-based CO2 emissions. In this follow-up paper, we re-run some of the analyses performed in the original paper making use of consumption-based CO2 emissions. The analysis confirms the inherent SDG conflict between socio-economic and ecological SDGs. But, this new analysis demonstrates that from a consumption perspective the trend of stalling or decreasing CO2 emissions is reversed, with natural depletion costs being exported to poorer countries. Despite this new perspective on CO2 emissions, the conflict between SDG goals can still be avoided by making investments in public health, education and renewable energy, as suggested in the original paper. [ABSTRACT FROM AUTHOR]

Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme events. And these extreme events, i.e., floods, heatwaves, hurricanes, droughts, etc., lead to a shortage of water resources, crop failures, wildfires, and economic losses. However, Global Circulation Models (GCMs) are considered the most important tools for quantifying climate change. Therefore, we selected 20 different GCMs of precipitation in our research, as the frequency of extreme events, like drought and flood, is highly related to changes in precipitation patterns. However, this research introduced a new weighting scheme — MCFSAWS-Ensemble: Monte Carlo Feature Selection Adaptive Weighting Scheme to Ensemble multiple GCMs, whereas, Monte Carlo Feature Selection (MCFS) is one of the most popular algorithms for discovering important variables. However, the proposed weighting scheme (MCFSAWS-Ensemble) is mainly based on two sources. Initially, it evaluates the prior performance of each GCM model to define their relative importance using MCFS. Then, it computes value by value difference between the observed and simulated model. In addition, the application of this paper is based on the monthly time series data of precipitation in the Tibet Plateau region of China. In addition, we used twenty GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the implications of the MCFSAWS-Ensemble. Further, we compared the performance of the MCFSAWS-Ensemble scheme with Simple Model Averaging (SMA) through Mean Average Error (MAE) and correlation statistics. The results of this research indicate that the proposed weighting scheme (MCFSAWS-Ensemble) is more accurate than the SMA approach. Consequently, we recommend the use of advanced machine learning algorithms such as MCFS for making accurate multi-model ensembles. [ABSTRACT FROM AUTHOR]

Effective selection of climate predictors is a fundamental aspect of climate modeling research. Predictor Screening (PS) plays a crucial role in identifying regional climate drivers, reducing noise, expediting convergence, and minimizing time consumption, ultimately leading to the development of robust models. This review delves into the complex landscape of PS techniques within the context of Numerical Climate Modeling (NCM), with a specific focus on their applicability across various Köppen climate classifications and PS model structures. The analysis revealed substantial variations in the performance of PS methods, shedding light on their ability to capture –and prioritize predictors related to precipitation and temperature within distinct climate contexts. Furthermore, the provided methods have been categorized into two subsections: Feature Selection (FS) and Feature Extraction (FE), with FS encompassing filter, wrapper, embedded, and ensemble/hybrid techniques, and FE covering Linear Feature Extraction (LFE), Time-Domain Analysis (TDA), deep learning, and clustering methods. The initial compilation of papers, acquired through a keyword search on Scopus, consisted of 3650 documents. Following a meticulous evaluation process, 206 papers were identified as fitting for inclusion in the literature review, covering the time frame from 1974 to November 3, 2023. In conclusion, the results provide a detailed understanding of the strengths and limitations of each approach, establishing a hierarchy of effectiveness contingent upon the specific climate context. Additionally, insights into promising avenues for future research in this field are offered. This review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) standard as its foundation. [ABSTRACT FROM AUTHOR]

Artificial neural networks are widely applied for different forecasting applications including rainfall forecasting. The climate input features and parameters for neural networks highly affect the overall performance of the prediction model. Therefore, an appropriate approach for the selection of features and parameters is needed. In this paper, a novel approach is proposed to select the input features and neural network parameters. A hybrid genetic algorithm that combines natural reproduction and particle swarm optimization characteristics was developed to select the best input features and network parameters for each month. The developed model was compared against alternative climate and network parameters feature selection model, climate feature selection model and climatology where a better accuracy was recorded with the proposed model. The skill score against the three alternative climate models was 17.41, 21.68 and 32.12%, respectively. The aggregated time series of the proposed model showed a root-mean-square error of 141.67 mm for a location with 3553.00 mm annual average. [ABSTRACT FROM AUTHOR]

Agricultural systems are constantly stressed due to higher demands for products. Consequently, water resources consumed on irrigation are increased. In combination with the climatic change, those are major obstacles to maintaining sustainable development, especially in a semi-arid land. This paper presents an end-to-end Machine Learning framework for predicting the potential profit from olive farms. The objective is to estimate the optimal economic gain while preserving water resources on irrigation by considering various related factors such as climatic conditions, crop management practices, soil characteristics, and crop yield. The case study focuses on olive tree farms located on the Hellenic Island of Crete. Real data from the farms and the weather in the area will be used. The target is to build a framework that will preprocess input data, compare the results among a group of Machine Learning algorithms and propose the best-predicted value of economic profit. Various aspects during this process will be thoroughly examined such as the bias-variance tradeoff and the problem of overfitting, data transforms, feature engineering and selection, ensemble methods as well as pursuing optimal resampling towards better model accuracy. Results indicated that through data preprocessing and resampling, Machine Learning algorithms performance is enhanced. Ultimately, prediction accuracy and reliability are greatly improved compared to algorithms' performances without the framework's operation. [ABSTRACT FROM AUTHOR]

Background: Fire behaviour simulation and prediction play a key role in supporting wildfire management and suppression activities. Aims: Using machine-learning methods, the aim of this study was to predict the onset of fire propagation (go vs no-go) and type of fire behaviour (surface vs crown fire) in southern Australian semiarid shrublands. Methods: Several machine-learning (ML) approaches were tested, including Support Vector Machine, Multinomial Naive Bayes and Multilayered Neural Networks, as was the use of augmented datasets developed with Generative Adversarial Networks (GAN) in classification of fire type. Key results: Support Vector Machine was determined as the optimum machine learning classifier based on model overall accuracy against an independent evaluation dataset. This classifier correctly predicted fire spread sustainability and active crown fire propagation in 70 and 79% of the cases, respectively. The application of synthetically generated datasets in the Support Vector Machine model fitting process resulted in an improvement of model accuracy by 20% for the fire sustainability classification and 4% for the crown fire occurrence. Conclusions: The selected ML modelling approach was shown to produce better results than logistic regression models when tested on independent datasets. Implications: Artificial intelligence frameworks have a role in the development of predictive models of fire behaviour. Models that predict wildfire behaviour in operational settings can support decision making across various fire management areas. Machine learning has the potential to develop predictive models of shrubland fire behaviour. This article tested the suitability of machine learning-based methods, which produced better results than logistic regression models. [ABSTRACT FROM AUTHOR]

Feature selection is an important component of machine learning for researchers that are confronted with high dimensional data. In the field of economics, researchers are often faced with high dimensional data, particularly in the studies that aim to understand the channels through which climate change affects the welfare of countries. This work reviews the current literature that introduces various feature selection algorithms that may be useful for applications in this area of study. The article first outlines the specific problems that researchers face in understanding the effects of climate change on countries’ macroeconomic outcomes, and then provides a discussion regarding different categories of feature selection. Emphasis is placed on two main feature selection algorithms: Least Absolute Shrinkage and Selection Operator and causality-based feature selection. I demonstrate an application of feature selection to discover the optimal heatwave definition for economic outcomes, enhancing our understanding of extreme temperatures’ impact on the economy. I argue that the literature in computer science can provide useful insights in studies concerned with climate change as well as its economic outcomes. [ABSTRACT FROM AUTHOR]

Soil erosion is a serious threat to sustainable agriculture, food production, and environmental security. The advancement of accurate models for soil erosion susceptibility and hazard assessment is of utmost importance for enhancing mitigation policies and laws. This paper proposes novel machine learning (ML) models for the susceptibility mapping of the water erosion of soil. The weighted subspace random forest (WSRF), Gaussian process with a radial basis function kernel (Gaussprradial), and naive Bayes (NB) ML methods were used in the prediction of the soil erosion susceptibility. Data included 227 samples of erosion and non-erosion locations through field surveys to advance models of the spatial distribution using predictive factors. In this study, 19 effective factors of soil erosion were considered. The critical factors were selected using simulated annealing feature selection (SAFS). The critical factors included aspect, curvature, slope length, flow accumulation, rainfall erosivity factor, distance from the stream, drainage density, fault density, normalized difference vegetation index (NDVI), hydrologic soil group, soil texture, and lithology. The dataset cells of samples (70% for training and 30% for testing) were randomly prepared to assess the robustness of the different models. The functional relevance between soil erosion and effective factors was computed using the ML models. The ML models were evaluated using different metrics, including accuracy, the kappa coefficient, and the probability of detection (POD). The accuracies of the WSRF, Gaussprradial, and NB methods were 0.91, 0.88, and 0.85, respectively, for the testing data; 0.82, 0.76, and 0.71, respectively, for the kappa coefficient; and 0.94, 0.94, and 0.94, respectively, for POD. However, the ML models, especially the WSRF, had an acceptable performance regarding producing soil erosion susceptibility maps. Maps produced with the most robust models can be a useful tool for sustainable management, watershed conservation, and the reduction of soil and water loss. [ABSTRACT FROM AUTHOR]

Prediction of aerosol optical thickness (AOT) is important to study worldwide climate changes. Researchers have built multiple AOT prediction models. However, few researches were focused on the validation of input attributes for AOT regression. In this paper, we proposed a support vector regression (SVR) model-based sensitivity analysis approach to order 35 MODIS input attributes according to their sensitivity to prediction outputs. Next, the attribute sensitivity orders are used for feature selection in the context of regression by removing insensitive attribute one at a time or by removing attributes whose sensitive orders are larger than number k. The experimental results based on the collocated data between MODIS and AERONET from 2009 to 2011 showed that the top 10 insensitive attributes can be screened to speed up prediction model computation with very little loss of accuracy. The results also suggested that the top sensitive attributes are the most informative attributes, requiring the highest precision for accurate AOT prediction. Thereby, our approach will be valuable for remote sensing scientists or atmospheric scientists to optimize the design precision of top sensitive attributes in scanning equipment like MODIS and therefore improve AOT retrieval accuracy. [ABSTRACT FROM AUTHOR]

Several countries are most reliant on agriculture either in terms of employment opportunities, national income, availability of a raw material, food production, to name but a few. However, it faces a big challenge such as climate changes, diseases, pets, weeds etc. Therefore, last decade has provided a machine learning‐based solution to the agricultural community, which helped farmers to identify the diseases at the early stages. In this article, our focus is on grape diseases, and proposes a novel framework to identify and classify the selected diseases at the early stages. A deep learning‐based solution is embedded into a conventional architecture for optimal performance. Three primary steps are involved; (a) feature extraction after applying transfer learning on pre‐trained deep models, AlexNet and ResNet101, (b) selection of best features using proposed Yager Entropy along with Kurtosis (YEaK) technique, (c) fusion of strong features using proposed parallel approach and later subject to classification step using least squared support vector machine (LS‐SVM). The simulations are performed on infected grape leaves obtained from the plant village dataset to achieving an accuracy of 99%. From the simulation results, we sincerely believe that our proposed approach performed exceptionally compared to several existing methods. [ABSTRACT FROM AUTHOR]

Climate projections simulated by Global Climate Models (GCMs) are often used for assessing the impacts of climate change. However, the relatively coarse resolutions of GCM outputs often preclude their application to accurately assessing the effects of climate change on finer regional-scale phenomena. Downscaling of climate variables from coarser to finer regional scales using statistical methods is often performed for regional climate projections. Statistical downscaling (SD) is based on the understanding that the regional climate is influenced by two factors -- the large-scale climatic state and the regional or local features. A transfer function approach of SD involves learning a regression model that relates these features (predictors) to a climatic variable of interest (predictand) based on the past observations. However, often a single regression model is not sufficient to describe complex dynamic relationships between the predictors and predictand. We focus on the covariate selection part of the transfer function approach and propose a nonparametric Bayesian mixture of sparse regression models based on Dirichlet process (DP) for simultaneous clustering and discovery of covariates within the clusters while automatically finding the number of clusters. Sparse linear models are parsimonious and hence more generalizable than non-sparse alternatives, and lend themselves to domain relevant interpretation. Applications to synthetic data demonstrate the value of the new approach and preliminary results related to feature selection for statistical downscaling show that our method can lead to new insights. [ABSTRACT FROM AUTHOR]