1. Sediments and Methods of Their Study 2. Erosional Processes 2.1. Weathering 2.2. Transport 3. Depositional Processes and Sediment Types 3.1. Features of Detrital Sediments 3.2. Bedforms and Sedimentary Structures 3.3. Trace Fossils 3.4. Gravity Flows 3.5. Classification of Detrital Sediments 3.6. Origin of Chemogenic and Biogenic Sediments 3.7. Classification of Carbonate Rocks 4. Depositional Environments 4.1. Arid Continental Environments 4.2. Humid Continental Environments 4.3. Glacial Sediments 4.4. Shallow Marine Sediments 4.5. Deep Marine Sediments 5. Sedimentary Basins and Their Types and Preservation 6. The Facies Concept and Multi-Story Sedimentary Packages 7. Post-Depositional Processes 8. Sediment Utilization Glossary Bibliography Biographical Sketch

堆積岩 = Sedimentary rocks

Time series analysis and forecasting is of vital significance, owing to its widespread use in various practical domains. Time series data refers to an ordered sequence or a set of data points that a variable takes at equal time intervals. The stock market is considered to be one of the most highly complex financial systems which consist of various components or stocks, the price of which fluctuates greatly with respect to time. Stock market forecasting involves uncovering the market trends with respect to time. All the stock market investors aim to maximize the returns over their investments and minimize the risks associated. Stock markets being highly sensitive and susceptible to quick changes, the main aim of stock-trend prediction is to develop new innovative approaches to foresee the stocks that result in high profits. This research tries to analyze the time series data of the Indian stock market and build a statistical model that could efficiently predict the future stocks.

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A Prediction Approach for Stock Market Volatility Based on Time Series Data

A review paper on wireless sensor network techniques in Internet of Things (IoT)

Industrial developments in the oil and gas, petrochemical, pharmaceutical and food sector have contributed to the large production of oily wastewater worldwide. Oily wastewater pollution affects drinking water and groundwater resources, endangers aquatic life and human health, causes atmospheric pollution, and affects crop production. Several traditional and conventional methods were widely reported, and the advantages and limitations were discussed. However, with the technology innovation, new trends of coupling between techniques, use of new materials, optimization of the cleaning process, and multiphysical approach present new paths for improvement. Despite these trends of improvement and the encouraging laboratory results of modern and green methods, many challenges remain to be raised, particularly the commercialization and the global aspect of these solutions and the reliability to reduce the system’s maintenance and operational cost. In this review, the well-known oily wastewater cleaning methods and approaches are being highlighted, and the obstacles faced in the practical use of these technologies are discussed. A critical review on the technologies and future direction as the road to commercialization is also presented to persevere water resources for the benefit of mankind and all living things.

https://www.mdpi.com/2073-4441/13/7/980/pdf

Oily Wastewater Treatment: Overview of Conventional and Modern Methods, Challenges, and Future Opportunities

An early and reliable estimation of crop yield is essential in quantitative and financial evaluation at the field level for determining strategic plans in agricultural commodities for import-export policies and doubling farmer’s incomes. Crop yield predictions are carried out to estimate higher crop yield through the use of machine learning algorithms which are one of the challenging issues in the agricultural sector. Due to this developing significance of crop yield prediction, this article provides an exhaustive review on the use of machine learning algorithms to predict crop yield with special emphasis on palm oil yield prediction. Initially, the current status of palm oil yield around the world is presented, along with a brief discussion on the overview of widely used features and prediction algorithms. Then, the critical evaluation of the state-of-the-art machine learning-based crop yield prediction, machine learning application in the palm oil industry and comparative analysis of related studies are presented. Consequently, a detailed study of the advantages and difficulties related to machine learning-based crop yield prediction and proper identification of current and future challenges to the agricultural industry is presented. The potential solutions are additionally prescribed in order to alleviate existing problems in crop yield prediction. Since one of the major objectives of this study is to explore the future perspectives of machine learning-based palm oil yield prediction, the areas including application of remote sensing, plant’s growth and disease recognition, mapping and tree counting, optimum features and algorithms have been broadly discussed. Finally, a prospective architecture of machine learning-based palm oil yield prediction has been proposed based on the critical evaluation of existing related studies. This technology will fulfill its promise by performing new research challenges in the analysis of crop yield prediction and the development of an extremely effective model for the prediction of palm oil yields with the most minimal computational difficulty.

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A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction

A Markov chain is commonly used in stock market analysis, manpower planning, and in many other areas because of its efficiency in predicting long run behavior. However, the Air Quality Index (AQI) suffers from not using a Markov chain in its forecasting approach. Therefore, this paper proposes a simple forecasting tool to predict the future air quality with a Markov chain model. The proposed method introduces the Markov chain as an operator to evaluate the distribution of the pollution level in the long term. Initial state vector and state transition probability were used in forecasting the behavior of Air Pollution Index (API) that has been obtained from the observed frequency for one state shift to another. The study explores that regardless of the present status of API, in the long run, the index shows a probability of 0.9231 for a good state, and a moderate and unhealthy state with a probability of 0.0722 and 0.0037, while for very unhealthy and hazardous states a probability of 0.0001 and 0.0009. The outcome of this study reveals that the model development could be used as a forecasting method that able to help government to project a prevention action plan during hazy weather.

https://www.mdpi.com/2071-1050/11/19/5190/pdf

Markov Chain Model Development for Forecasting Air Pollution Index of Miri, Sarawak

Reservoir water level (RWL) prediction has become a challenging task due to spatio-temporal changes in climatic conditions and complicated physical process. The Red Hills Reservoir (RHR) is an important source of drinking and irrigation water supply in Thiruvallur district, Tamil Nadu, India, also expected to be converted into the other productive services in the future. However, climate change in the region is expected to have consequences over the RHR’s future prospects. As a result, accurate and reliable prediction of the RWL is crucial to develop an appropriate water release mechanism of RHR to satisfy the population’s water demand. In the current study, time series modelling technique was adopted for the RWL prediction in RHR using Box–Jenkins autoregressive seasonal autoregressive integrated moving average (SARIMA) and artificial neural network (ANN) hybrid models. In this research, the SARIMA model was obtained as SARIMA (0, 0, 1) (0, 3, 2)12 but the residual of the SARIMA model could not meet the autocorrelation requirement of the modelling approach. In order to overcome this weakness of the SARIMA model, a new SARIMA–ANN hybrid time series model was developed and demonstrated in this study. The average monthly RWL data from January 2004 to November 2020 was used for developing and testing the models. Several model assessment criteria were used to evaluate the performance of each model. The findings showed that the SARIMA–ANN hybrid model outperformed the remaining models considering all performance criteria for reservoir RWL prediction. Thus, this study conclusively proves that the SARIMA–ANN hybrid model could be a viable option for the accurate prediction of reservoir water level.

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Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study

Air pollution is one of the main environmental issues faced by most countries around the world. Forecasting air pollution occurrences is an essential topic in air quality research due to the increase in awareness of its association with public health effects, and its development is vital to managing air quality. However, most previous studies have focused on enhancing accuracy, while very few have addressed uncertainty analysis, which may lead to insufficient results. The fuzzy time-series model is a better option in air pollution forecasting. Nevertheless, it has a limitation caused by utilizing a random partitioning of the universe of discourse. This study proposes a novel Markov weighted fuzzy time-series model based on the optimum partition method. Fitting the optimum partition method has been done based on five different partition methods via two stages. The proposed model is first applied for forecasting air pollution using air pollution index (API) data collected from an air monitoring station located in Klang city, Malaysia. The performance of the proposed model is evaluated based on three statistical criteria, which are the mean absolute percentage error, mean squared error and Theil’s U statistic, using the daily API data. For further validation of the model, it is also implemented for benchmark enrolment data from the University of Alabama. According to the analysis results, the proposed model greatly improved the performance of air pollution index and enrolment prediction accuracy, for which it outperformed several state-of-the-art fuzzy time-series models and classic time-series models. Thus, the proposed model could be a better option for air quality forecasting for managing air pollution.

Markov Weighted Fuzzy Time-Series Model Based on an Optimum Partition Method for Forecasting Air Pollution

Oil palm can be reflected as the main contributor to capital investment, technology, foreign workers’ employment, and knowledge management. There are risks and uncertainties arising from instability of crude palm oil (CPO) prices. Therefore, this research develops a new CPO price forecasting method using weighted subsethood-based algorithm in order to generate fuzzy rules of forecasting. The concept of fuzzy rule-based systems was embedded in fuzzy time series application to generate fuzzy IF-THEN rules. This paper aims to enhance the efficacy of time-series forecasting, which would in turn increase the accuracy of the predictions. The CPO prices data set was used for validation purposes. The accuracy of forecasting of the proposed method was compared with previous methods. The numerical results are comparable with the previous methods. The outcomes of the proposed method have shown an increase in the accuracies of the CPO price forecasts. As such, the above-mentioned method can be utilized for the creation of a new set of fuzzy rules to better predict CPO prices.

Forecasting Crude Palm Oil Prices Using Fuzzy Rule-Based Time Series Method

Air pollution is a worldwide problem faced by most countries across the world. Prediction of air pollution is crucial in air quality research since it is related to public health effects. The symmetry concept of fuzzy data transformation from a single point (crisp) to a fuzzy number is essential for the forecasting model. Fuzzy time series (FTS) is applied for predicting air pollution; however, it has a limitation caused by utilizing an arbitrary number of intervals. This study involves predicting the daily air pollution index using the FTS Markov chain (FTSMC) model based on a grid method with an optimal number of partitions, which can greatly develop the model accuracy for air pollution. The air pollution index (API) data, which was collected from Klang, Malaysia, is considered in the analysis. The model has been validated using three statistical criteria, which are the root mean (RMSE), the mean absolute percentage error (MAPE), and the Thiels’ U statistic. Also, the model’s validation has been investigated by comparison with some of the famous statistical models. The results of the proposed model demonstrated outperformed the other models. Thus, the proposed model could be a better option in air pollution forecasting that can be useful for managing air quality.

https://www.mdpi.com/2073-8994/12/2/293/pdf

Rajalingam Sokkalingam

Papers

Markov Chain Model Development for Forecasting Air Pollution Index of Miri, Sarawak

Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study

Markov Weighted Fuzzy Time-Series Model Based on an Optimum Partition Method for Forecasting Air Pollution

Forecasting Crude Palm Oil Prices Using Fuzzy Rule-Based Time Series Method

Predicting Daily Air Pollution Index Based on Fuzzy Time Series Markov Chain Model