Soil Moisture Prediction Using LSTM And Ensemble Learning Methods

The prediction of wind energy generation is important to enhance the performance and dependability of renewable energy systems due to the rising demand for wind-generated electricity and advancements in wind energy technology competitiveness. This study leverages advanced machine learning (ML) and some other statistical and deep learning based time series forecasting models to enhance the accuracy of wind energy predictions. This comprehensive analysis includes nine ML models—Linear Regression, Random Forests (RF), Gradient Boosting Machines (GBM), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), AdaBoost, XGBoost, Support Vector Regression (SVR), and Neural Networks—as well as Four time-series forecasting models—ARIMA, Temporal Convolutional Networks (TCNs), Long Short-Term Memory (LSTM) networks and GRU. Each ML model underwent rigorous cross-validation to ensure optimal performance. The assessment criteria utilized here comprised the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the R² Score. It was found that among the nine ML models, Random Forests, GBM and KNN consistently provided superior accuracy and robustness, making them the top choices for wind energy prediction whereas the performance of linear regression, SVM and SVR were very poor for the considered dataset. From the experiment, Random Forest, GBM, and KNN showed the best performance with low MSE values of 0.77, 1.95, and 1.51 respectively, while other models had MSEs above 7.5, with AdaBoost reaching 30. Their RMSEs (0.88, 1.40, 1.23) and MAEs (0.093, 0.73, 0.10) also indicate strong predictive accuracy compared to the rest.In this paper, time series forecasting, TCNs, LSTM and GRU networks showed strong capabilities in capturing temporal dependencies and trends within the wind energy data. Visualization techniques were employed to compare model performances comprehensively, providing clear insights into their predictive power. Therefore, this present study offers a robust framework for researchers and practitioners aiming to leverage machine learning and time series forecasting in the realm of renewable energy prediction.

Accurate real-time bus arrival information is essential for an efficient public transport network, as it significantly impacts passenger experience, system reliability, reduced waiting times, dwell times, and operational efficiency. In Sri Lanka's public transport system, current bus arrival time computations primarily rely on static data, neglecting real-time information and critical factors influencing travel times. This highlights the need to identify the unique variables affecting bus arrival times within the Sri Lankan context and to develop robust prediction models that account for these influences. While traditional methods such as Historical Average Models, Regression, Time Series Analysis, and Kalman Filtering have been used in previous research for short-term travel time predictions, Machine Learning (ML) approaches have proven to deliver superior accuracy. ML models are regarded as the most effective for heterogeneous, lane-less traffic conditions with varying traffic volumes, such as those found in Sri Lanka. ML techniques excel in processing large, high-quality datasets and provide accurate predictions by accounting for all relevant variables influencing travel times. Although research has been conducted on developing various basic ML models for travel time prediction, there is a noticeable gap in studies comparing these models to determine the most suitable one for the Sri Lankan context. A Long- Short Term Memory (LSTM) neural network is a deep learning model that is capable of handling long-term dependencies. In the context of bus travel time prediction, LSTMs can leverage historical traffic and travel data to capture temporal patterns and fluctuations that influence travel times. By evaluating LSTMs against basic machine learning models, this study seeks to explore the advantages of applying deep learning techniques to transportation forecasting, ultimately contributing to more accurate and efficient predictive systems in transit planning. The ML models selected in this study include two basic traditional models K- Nearest Neighbours (KNN) and Support Vector Regression (SVR) and four advanced models that utilize ensemble techniques and advanced optimization as Random Forest Regression (RFR), Ada Boost, XG Boost and Gradient Boosting Machine (GBM). The performance of these models was compared with the LSTM model to identify the gap in their accuracies.

Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak

ABSTRACTAccurate estimation of multi‐depth/profile soil moisture (SM) is required for sustainable water management in agriculture and hydrology. However, monitoring SM is costly and labour‐intensive, and only limited soil depths can be instrumented with soil moisture sensors. Therefore, various numerical simulation and data assimilation techniques have been used in multi‐depth soil moisture estimation. Machine learning (ML) has also gained popularity in SM estimation due to its ease of use and robustness, although proper handling of ML models also requires expertise and experience. However, the applicability of ML to estimate time series of multi‐depth SM under different land uses is mainly limited by the choice of ML models and the availability of SM data. In addition, the reliability of the trained model remains unknown when it is applied to different locations. Therefore, the objective of this study was to evaluate the widely used Long Short‐Term Memory (LSTM) model to estimate multi‐depth SM under different land covers, climates, and soils. A minimum of 10 years' daily meteorological and soil data at multiple depths were collected from six U.S. Climate Reference Network (USCRN) stations with different land covers and various climates and soils. These data were used to train the LSTM model and optimize its input parameters. Performance of the trained LSTM model was evaluated for multi‐depth SM estimation at two other “monitoring” stations with similar conditions. SM modeling at shallow depths (e.g., 5, 10 and 20 cm) was most accurate (< 10% mean absolute percent error, MAPE) with precipitation and antecedent time series of SM as inputs, while the best SM estimates at deeper depths (e.g., 50 and 100 cm) were attained with antecedent SM time series as the input. Generation of the trained LSTM model from one station to other stations emphasized on the similar soil and land cover conditions. It is hoped that this research would provide better understandings of multi‐depth SM modeling and offer new insights improving profile SM modeling accuracy for un‐instrumented sites.

The sea surface temperature (SST) is an environmental indicator closely related to climate, weather, and atmospheric events worldwide. Its forecasting is essential for supporting the decision of governments and environmental organizations. Literature has shown that single machine learning (ML) models are generally more accurate than traditional statistical models for SST time series modeling. However, the parameters tuning of these ML models is a challenging task, mainly when complex phenomena, such as SST forecasting, are addressed. Issues related to misspecification, overfitting, or underfitting of the ML models can lead to underperforming forecasts. This work proposes using hybrid systems (HS) that combine (ML) models using residual forecasting as an alternative to enhance the performance of SST forecasting. In this context, two types of combinations are evaluated using two ML models: support vector regression (SVR) and long short-term memory (LSTM). The experimental evaluation was performed on three datasets from different regions of the Atlantic Ocean using three well-known measures: mean square error (MSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). The best HS based on SVR improved the MSE value for each analyzed series by 82.26%, 98.93%, and 65.03% compared to its respective single model. The HS employing the LSTM improved 92.15%, 98.69%, and 32.41% concerning the single LSTM model. Compared to literature approaches, at least one version of HS attained higher accuracy than statistical and ML models in all study cases. In particular, the nonlinear combination of the ML models obtained the best performance among the proposed HS versions.

This chapter focuses on how recurrent neural networks (RNN) can help the farmers to seek advice for their location on a given weather forecast. The main purpose of this work is to put forward the concept of using the data created over the years by various agricultural and meteorological divisions to aid computers to learn the pattern and sequences and to generate the advisory itself. The focus of the key objectives of this work is to collect and process sample data for a few districts over some months, create an RNN model to train on the data, and check the result on a few more sample data within the same district. The model is made on the long-short term memory (LSTM) architecture of the RNN. The proposed method makes use of IMD Agrimet's weekly advisory for districts by extracting forecast and advisory data from them. The data is then used in an RNN, which further can generate advisory on its own given weather forecast data. Given that IMD has no archive online for the public, the data collected over a few months is less, and the advice generated was less reliable. But this opens the field for more data collection and research. It would be a valuable task for farmers who are less connected to the internet; this can then provide on-device advice when the weather is known. Purpose: Agriculture in various parts of the world depends on the climate and terrain of the specific region. The main purpose of this work is to allow computers to evaluate and advise people on farming. This paper also encourages the development of finding the relation and collecting data of meteorology and agriculture. Design/Methodology: Data collection is done manually by utilizing agro-meteorological division's weekly advisory. The model is based on RNN architecture where the previous result persists to the next result, which is best achieved using LSTM. The proposed work is implemented in Python with Keras, Numpy, and Pandas libraries. Key Findings: Data available at present is not in any standard format and differs by different states. The quality of data present is quite good for use given that it is arranged abundantly. RNN model can easily take advantage of properly arranged data and learn the advisory and its relation to weather. The accuracy of the model depends upon the amount and quality of data fed for training. Originality/Key Contribution: Agro-meteorology has been in the works for a long time, and is being done by humans as computers were not able to produce quality decisions. With the power and creativity of machine learning, such results can be achieved by taking a step in the direction. With this work, we intend to uplift the hope of using a computer system in providing decisions for agriculture. Computer making meaningful statements is a field of its own, which hadn't been much used in agriculture. Most uses of machine learning in agriculture are related to crop production statistics. Practical Implication: This work was done keeping the usefulness of meteorology for farmers in mind. Practically, these machine learning models can be deployed in various ways like mobile applications, web pages, etc. Specifically, mobile apps can take full advantage by providing quick and near-accurate advice to farmers with a single click.

The ongoing Russia–Ukraine conflict has exacerbated the global crisis of natural gas supply, particularly in Europe. During the winter season, major importers of liquefied natural gas (LNG), such as South Korea and Japan, were directly affected by fluctuating spot LNG prices. This study aimed to use machine learning (ML) to predict the Japan Korea Marker (JKM), a spot LNG price index, to reduce price fluctuation risks for LNG importers such as the Korean Gas Corporation (KOGAS). Hence, price prediction models were developed based on long short-term memory (LSTM), artificial neural network (ANN), and support vector machine (SVM) algorithms, which were used for time series data prediction. Eighty-seven variables were collected for JKM prediction, of which eight were selected for modeling. Four scenarios (scenarios A, B, C, and D) were devised and tested to analyze the effect of each variable on the performance of the models. Among the eight variables, JKM, national balancing point (NBP), and Brent price indexes demonstrated the largest effects on the performance of the ML models. In contrast, the variable of LNG import volume in China had the least effect. The LSTM model showed a mean absolute error (MAE) of 0.195, making it the best-performing algorithm. However, the LSTM model demonstrated a decreased in performance of at least 57% during the COVID-19 period, which raises concerns regarding the reliability of the test results obtained during that time. The study compared the ML models’ prediction performances with those of the traditional statistical model, autoregressive integrated moving averages (ARIMA), to verify their effectiveness. The comparison results showed that the LSTM model’s performance deviated by an MAE of 15–22%, which can be attributed to the constraints of the small dataset size and conceptual structural differences between the ML and ARIMA models. However, if a sufficiently large dataset can be secured for training, the ML model is expected to perform better than the ARIMA. Additionally, separate tests were conducted to predict the trends of JKM fluctuations and comprehensively validate the practicality of the ML models. Based on the test results, LSTM model, identified as the optimal ML algorithm, achieved a performance of 53% during the regular period and 57% d during the abnormal period (i.e., COVID-19). Subject matter experts agreed that the performance of the ML models could be improved through additional studies, ultimately reducing the risk of price fluctuations when purchasing spot LNG.

Summary A meticulous interpretation of steady-state or unsteady-state relative permeability (Kr) experimental data is required to determine a complete set of Kr curves. In this work, different machine learning (ML) models were developed to assist in a faster estimation of these curves from steady-state drainage coreflooding experimental runs. These ML algorithms include gradient boosting (GB), random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN) with a main focus on and comparison of the two latter algorithms (XGB and DNN). Based on existing mathematical models, a leading-edge framework was developed where a large database of Kr and capillary pressure (Pc) curves were generated. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from these simulation runs, mainly pressure drop along with other conventional core analysis data, were used to estimate analytical Kr curves based on Darcy’s law. These analytically estimated Kr curves along with the previously generated Pc curves were fed as features into the ML model. The entire data set was split into 80% for training and 20% for testing. The k-fold cross-validation technique was applied to increase the model’s accuracy by splitting 80% of the training data into 10 folds. In this manner, for each of the 10 experiments, nine folds were used for training and the remaining fold was used for model validation. Once the model was trained and validated, it was subjected to blind testing on the remaining 20% of the data set. The ML model learns to capture fluid flow behavior inside the core from the training data set. In terms of applicability of these ML models, two sets of experimental data were needed as input; the first was the analytically estimated Kr curves from the steady-state drainage coreflooding experiments, while the other was the Pc curves estimated from centrifuge or mercury injection capillary pressure (MICP) measurements. The trained/tested model was then able to estimate Kr curves based on the experimental results fed as input. Furthermore, to test the performance of the ML model when only one set of experimental data is available to an end user, a recurrent neural network (RNN) algorithm was trained/tested to predict Kr curves in the absence of Pc curves as an input. The performance of the three developed models (XGB, DNN, and RNN) was assessed using the values of the coefficient of determination (R2) along with the loss calculated during training/validation of the model. The respective crossplots along with comparisons of ground truth vs. artificial intelligence (AI)-predicted curves indicated that the model is capable of making accurate predictions with an error percentage between 0.2% and 0.6% on history-matching experimental data for all three tested ML techniques. This implies that the AI-based model exhibits better efficiency and reliability in determining Kr curves when compared to conventional methods. The developed ML models by no means replace the need to conduct drainage coreflooding or centrifuge experiments but act as an alternative to existing commercial platforms that are used to interpret experimental data to predict Kr curves. The two main advantages of the developed ML models are their capability of predicting Kr curves within a matter of a few minutes as well as with limited intervention from the end user. The results also include a comparison between classical ML approaches, shallow neural networks, and DNNs in terms of accuracy in predicting the final Kr curves. The research presented here is an extension of the state-of-the-art framework proposed by Mathew et al. (2021). However, the two main aspects of the current study are the application of deep learning for the prediction of Kr curves and the application of feature engineering. The latter not only reduces the training/testing time for the ML models but also enables the end user to obtain the final predictions with the least set of experimental data. The various models discussed in this research work currently focus on the prediction of Kr curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

Abstract. As air pollution is regarded as the single largest environmental health risk in Europe it is important that communication to the public is up to date and accurate and provides means to avoid exposure to high air pollution levels. Long- and short-term exposure to outdoor air pollution is associated with increased risks of mortality and morbidity. Up-to-date information on present and coming days' air quality helps people avoid exposure during episodes with high levels of air pollution. Air quality forecasts can be based on deterministic dispersion modelling, but to be accurate this requires detailed information on future emissions, meteorological conditions and process-oriented dispersion modelling. In this paper, we apply different machine learning (ML) algorithms – random forest (RF), extreme gradient boosting (XGB), and long short-term memory (LSTM) – to improve 1, 2, and 3 d deterministic forecasts of PM10, NOx, and O3 at different sites in Greater Stockholm, Sweden. It is shown that the deterministic forecasts can be significantly improved using the ML models but that the degree of improvement of the deterministic forecasts depends more on pollutant and site than on what ML algorithm is applied. Also, four feature importance methods, namely the mean decrease in impurity (MDI) method, permutation method, gradient-based method, and Shapley additive explanations (SHAP) method, are utilized to identify significant features that are common and robust across all models and methods for a pollutant. Deterministic forecasts of PM10 are improved by the ML models through the input of lagged measurements and Julian day partly reflecting seasonal variations not properly parameterized in the deterministic forecasts. A systematic discrepancy by the deterministic forecasts in the diurnal cycle of NOx is removed by the ML models considering lagged measurements and calendar data like hour and weekday, reflecting the influence of local traffic emissions. For O3 at the urban background site, the local photochemistry is not properly accounted for by the relatively coarse Copernicus Atmosphere Monitoring Service ensemble model (CAMS) used here for forecasting O3 but is compensated for using the ML models by taking lagged measurements into account. Through multiple repetitions of the training process, the resulting ML models achieved improvements for all sites and pollutants. For NOx at street canyon sites, mean squared error (MSE) decreased by up to 60 %, and seven metrics, such as R2 and mean absolute percentage error (MAPE), exhibited consistent results. The prediction of PM10 is improved significantly at the urban background site, whereas the ML models at street sites have difficulty capturing more information. The prediction accuracy of O3 also modestly increased, with differences between metrics. Further work is needed to reduce deviations between model results and measurements for short periods with relatively high concentrations (peaks) at the street canyon sites. Such peaks can be due to a combination of non-typical emissions and unfavourable meteorological conditions, which are rather difficult to forecast. Furthermore, we show that general models trained using data from selected street sites can improve the deterministic forecasts of NOx at the station not involved in model training. For PM10 this was only possible using more complex LSTM models. An important aspect to consider when choosing ML algorithms is the computational requirements for training the models in the deployment of the system. Tree-based models (RF and XGB) require fewer computational resources and yield comparable performance in comparison to LSTM. Therefore, tree-based models are now implemented operationally in the forecasts of air pollution and health risks in Stockholm. Nevertheless, there is big potential to develop generic models using advanced ML to take into account not only local temporal variation but also spatial variation at different stations.

The pooled cohort equations (PCE) predict atherosclerotic cardiovascular disease (ASCVD) risk in patients with characteristics within prespecified ranges and has uncertain performance among Asians or Hispanics. It is unknown if machine learning (ML) models can improve ASCVD risk prediction across broader diverse, real-world populations. We developed ML models for ASCVD risk prediction for multi-ethnic patients using an electronic health record (EHR) database from Northern California. Our cohort included patients aged 18 years or older with no prior CVD and not on statins at baseline (n = 262,923), stratified by PCE-eligible (n = 131,721) or PCE-ineligible patients based on missing or out-of-range variables. We trained ML models [logistic regression with L2 penalty and L1 lasso penalty, random forest, gradient boosting machine (GBM), extreme gradient boosting] and determined 5-year ASCVD risk prediction, including with and without incorporation of additional EHR variables, and in Asian and Hispanic subgroups. A total of 4309 patients had ASCVD events, with 2077 in PCE-ineligible patients. GBM performance in the full cohort, including PCE-ineligible patients (area under receiver-operating characteristic curve (AUC) 0.835, 95% confidence interval (CI): 0.825–0.846), was significantly better than that of the PCE in the PCE-eligible cohort (AUC 0.775, 95% CI: 0.755–0.794). Among patients aged 40–79, GBM performed similarly before (AUC 0.784, 95% CI: 0.759–0.808) and after (AUC 0.790, 95% CI: 0.765–0.814) incorporating additional EHR data. Overall, ML models achieved comparable or improved performance compared to the PCE while allowing risk discrimination in a larger group of patients including PCE-ineligible patients. EHR-trained ML models may help bridge important gaps in ASCVD risk prediction.

The objective of this document is to identify the most accurate machine learning (ML) models for predicting the value of a real estate property, based on a systematic literature review (SLR). It was conducted on research published between 2022 and 2023 that analyzed the accuracy of ML models in real estate valuation. Information was extracted regarding the ML models used, the databases employed, and the models highlighted for their accuracy. A variety of ML models used in real estate valuation were identified, including Random Forest (RF), XGBoost, Gradient Boosting Machine (GBM), Linear Regression (LR), and Lasso Regression. RF and LR models stood out as the most accurate in the analyzed research, showing that the accuracy of ML models varies depending on the database, property characteristics, and valuation context. It was concluded that ML models like RF and LR are promising tools for improving the accuracy of real estate valuation. Additionally, the choice of the best ML model depends on factors such as the database, property characteristics, and specific valuation objectives. Further research is needed on the application of ML in real estate valuation, considering aspects such as model interpretability and the real estate market context.

Sequence Classification is one of the on-demand research projects in the field of Natural Language Processing (NLP). Classifying a set of images or text into an appropriate category or class is a complex task that a lot of Machine Learning (ML) models fail to accomplish accurately and end up under-fitting the given dataset. Some of the ML algorithms used in text classification are KNN, Naïve Bayes, Support Vector Machines, Convolutional Neural Networks (CNNs), Recursive CNNs, Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM), etc. For this experimental study, LSTM and a few other algorithms were chosen for a more comparative study. The dataset used is the SMS Spam Collection Dataset from Kaggle and 150 more entries were additionally added from different sources. Two possible class labels for the data points are spam and ham. Each entry consists of the class label, a few sentences of text followed by a few useless features that are eliminated. After converting the text to the required format, the models are run and then evaluated using various metrics. In experimental studies, the LSTM gives much better classification accuracy than the other machine learning models. F1-Scores in the high nineties were achieved using LSTM for classifying the text. The other models showed very low F1-Scores and Cosine Similarities indicating that they had underperformed on the dataset. Another interesting observation is that the LSTM had reduced the number of false positives and false negatives than any other model.

A comparison of physical-based and machine learning modeling for soil salt dynamics in crop fields

Consolidating tasks to a smaller number of electronic control units (ECUs) is an important strategy for optimizing costs and resources in the automotive industry. In our research, we aim to enable ECU consolidation by migrating tasks at runtime between different ECUs, which adds redundancy and fail-safety capabilities to the system. In this paper, we present a setup with a generalistic and modular architecture that allows for integrating and testing different ECU architectures and machine learning (ML) models. As part of a holistic testbed, we introduce a collection of reproducible tasks, as well as a toolchain that controls the dynamic migration of tasks depending on ECU status and load. The migration is aided by the machine learning predictions on the schedulability analysis of possible future task distributions. To demonstrate the capabilities of the setup, we show its integration with FreeRTOS-based ECUs and two ML models—a long short-term memory (LSTM) network and a spiking neural network—along with a collection of tasks to distribute among the ECUs. Our approach shows a promising potential for machine-learning-based schedulability analysis and enables a comparison between different ML models.

Shale caprocks, often organic-rich, play a significant role in hydrogen sequestration, but their wettability is typically altered from water-wet to H2-wet due to the organic matter, compromising their sealing performance. Silica nanofluids can restore water-wet conditions, which is vital for effective containment of H2 in geological formations. However, laboratory experiments to test the effects of nanomaterials on wettability and interfacial tension (IFT) are resource-intensive and time-consuming. To address this challenge, data-driven machine learning (ML) models offer a promising alternative, allowing for the prediction of key UHS parameters—such as advancing and receding contact angles without the need for extensive laboratory testing. We have applied multiple ML models, including gradient boosting, extreme gradient boosting (XGboost), Catboost, random forest, and extra trees to predict the wettability of organic rich shale samples treated with silica nanofluids. The ML models were trained on a comprehensive dataset generated through laboratory experiments conducted under a wide range of pressure (0.5 – 1600psi) and temperature (298-323 K) conditions, using brine (1 wt.% KCl + 2 wt.% NaCl) and varying silica nanofluid concentrations (0.05–1.0 wt.%). Advanced visualization techniques, including swarm maps, box plots, and heatmap plots, were utilized to analyze the experimental dataset thoroughly. To enhance the generalization capabilities of the ML models, we employed Bayesian optimization for hyperparameter tuning, along with k-fold cross-validation and grid-search optimization techniques. Both statistical and graphical analyses were performed to show the reliability and performance of the models. The results demonstrated that the implemented ML models, particularly the XGboost and Catboost, accurately predicted the wettability behavior under various operating conditions. The training and testing average R² scores were above 0.998 and 0.949, respectively, confirming the high accuracy of the predictions. These findings highlight the reliability of the ML algorithms in predicting the complex interaction between shale, brine, and H2. Further investigation into the feature importance revealed that nanomaterials had the greatest influence on the wettability of the ternary system. The silica nanofluid concentrations played a key role in modifying wettability, with varying impacts on the receding contact angles, emphasizing the effect of nanomaterial treatment. The model's predictions were consistent with experimental results, suggesting that geological conditions, including pressure and temperature, have a profound influence on rock mineral wettability, directly impacting H2 geo-storage capacity. This study is the first to employ data-driven ML models to predict critical UHS parameters in organic-rich shales treated with silica nanofluids. The ability of the ML models to predict UHS parameters across a wide range of conditions provides a valuable tool for optimizing UHS system designs, offering a more efficient and scalable approach compared to traditional experimental methods.