An Evaluation of Machine Learning and Deep Learning Models for Drought Prediction using Weather Data

Drought is a serious natural disaster that has a long duration and a wide range of influence. To decrease the drought-caused losses, drought prediction is the basis of making the corresponding drought prevention and disaster reduction measures. While this problem has been studied in the literature, it remains unknown whether drought can be precisely predicted or not with machine learning models using weather data. To answer this question, a real-world public dataset is leveraged in this study and different drought levels are predicted using the last 90 days of 18 meteorological indicators as the predictors. In a comprehensive approach, 16 machine learning models and 16 deep learning models are evaluated and compared. The results show no single model can achieve the best performance for all evaluation metrics simultaneously, which indicates the drought prediction problem is still challenging. As benchmarks for further studies, the code and results are publicly available in a Github repository.Comment: Github link: https://github.com/jwwthu/DL4Climate/tree/main/DroughtPredictio

Black box and mechanistic modelling of electronic nose systems

Electronic nose systems have been in existence for around 20 years or more. The ability to mimic the function of the mammalian olfactory system is a very tempting goal. Such devices would offer the possibility of rapid chemical screening of samples. To gain a detailed insight into the operation of such systems it is proposed to carry out a systems modelling analysis. This thesis reports such an analysis using black box and mechanistic models. The nature and construction of electronic nose systems are discussed. The challenges presented by these systems in order to produce a truly electronic nose are analysed as a prelude to systems modelling. These may be summarised as time and environmental dependent behaviour, information extraction and computer data handling. Model building in general is investigated. It is recognised that robust parameter estimation is necessary to build good models of electronic nose systems. A number of optimisation algorithms for parameter estimation are proposed and investigated, these being gradient search, genetic algorithms and the support vector method. It is concluded that the support vector method is most robust, although the genetic algorithm approach shows promise for initial parameter value estimation. A series of investigations are reported that involve the analysis of biomedical samples. These samples are of blood, urine and bacterial cultures. The findings demonstrate that the nature of such samples, such as bacterial content and type, may be accurately identified using an electronic nose system by careful modelling of the system. These findings also highlight the advantages of data set reduction and feature extraction. A mechanistic model embodying the operating principles of carbon black-polymer sensors is developed. This is validated experimentally and is used to investigate the environmental dependencies of electronic nose systems. These findings demonstrate a clear influence of environmental conditions on the behaviour of carbon black-polymer sensors and these should be considered when designing future electronic nose systems. The findings in this thesis demonstrate that careful systems modelling and analysis of electronic nose systems allows a greater understanding of such systems

Hydro-climatic and Economic Evaluation of Seasonal Climate Forecasts for Risk Based Irrigation Management

This work is focused in the Murrumbidgee catchment to help understand the value of the seasonal forecasts to rice based cropping systems. The key activities of this project include: • An overview of water allocation in the Murrumbidgee Valley • Evaluation of commonly used seasonal forecasting methods used to predict rainfall • Development of a novel water allocation model on the basis of seasonal forecasts and historic allocation data • Economic analysis of the benefits from better irrigation forecasts in irrigated catchments The key findings include: • The current system of announcing allocations does not take into account seasonal climate forecasts of rainfall and flows in the catchment. End of the season allocations are made too late and pose a serious financial risk to farmers due to inadequate information being available at the start of the summer cropping period • The SST correlations with inflows to dams has provided promising results, which can be used to forecast flows to dams with lead times of around 1 year • Artificial Neural network (ANN) approaches which can learn from historic model simulations and SST predictions can be a way forward to link climate forecasts with risk management. Results of the ANN model show good correlations with the historic water allocation trends over any given season. This tool can be used to make informed cropping risk decisions • Irrigators utilising allocation forecast information can minimise the opportunity cost of forgone agricultural production. Undertaking decision analysis, it was estimated that the net benefit of allocation forecasts to the irrigators of the CIA is between $50,000 and$660,000 per year (equivalent to $0.68/ha and$8.56/ha). This was assuming that the CIA irrigators are collectively risk averse as their risk preference is unknown As part of this project a stakeholder workshop on climate variability, climate change and adaptation in the Murrumbidgee Basin was organised, to examine research ideas on climate research for efficient irrigation management. Participants included a number of interested participants from irrigation companies, NSW Agriculture, Department of Infrastructure Planning and Natural Resources (DIPNR), Murray Darling Basin Commission (MDBC) and the local community. There is a tremendous interest in climate and water issues due to the recent drought. The farming community needs tools which can link climate forecasts with smarter agricultural water management using a risk based approach. The key barrier to the adoption of existing climate forecast tools is their lack of proven utility and the risk adverse attitude of water allocation agencies

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed

An efficient data driven-based model for prediction of the total sediment load in rivers

Sediment load in fluvial systems is one of the critical factors shaping the river geomorphological and hydraulic characteristics. A detailed understanding of the total sediment load (TSL) is required for the protection of physical, environmental, and ecological functions of rivers. This study develops a robust methodological approach based on multiple linear regression (MLR) and support vector regression (SVR) models modified by principal component analysis (PCA) to predict the TSL in rivers. A database of sediment measurement from large-scale physical modelling tests with 4759 datapoints were used to develop the predictive model. A dimensional analysis was performed based on the literature, and ten dimensionless parameters were identified as the key drivers of the TSL in rivers. These drivers were converted to uncorrelated principal components to feed the MLR and SVR models (PCA-based MLR and PCA-based SVR models) developed within this study. A stepwise PCA-based MLR and a 10-fold PCA-based SVR model with different kernel-type functions were tuned to derive an accurate TSL predictive model. Our findings suggest that the PCA-based SVR model with the kernel-type radial basis function has the best predictive performance in terms of statistical error measures including the root-mean-square error normalized with the standard deviation (RMSE/StD) and the Nash–Sutcliffe coefficient of efficiency (NSE), for the estimation of the TSL in rivers. The PCA-based MLR and PCA-based SVR models, with an overall RMSE/StD of 0.45 and 0.35, respectively, outperform the existing well-established empirical formulae for TSL estimation. The analysis of the results confirms the robustness of the proposed PCA-based SVR model for prediction of the cases with high concentration of sediments (NSE = 0.68), where the existing sediment estimation models usually have poor performance

Deep Learning Techniques in Extreme Weather Events: A Review

Extreme weather events pose significant challenges, thereby demanding techniques for accurate analysis and precise forecasting to mitigate its impact. In recent years, deep learning techniques have emerged as a promising approach for weather forecasting and understanding the dynamics of extreme weather events. This review aims to provide a comprehensive overview of the state-of-the-art deep learning in the field. We explore the utilization of deep learning architectures, across various aspects of weather prediction such as thunderstorm, lightning, precipitation, drought, heatwave, cold waves and tropical cyclones. We highlight the potential of deep learning, such as its ability to capture complex patterns and non-linear relationships. Additionally, we discuss the limitations of current approaches and highlight future directions for advancements in the field of meteorology. The insights gained from this systematic review are crucial for the scientific community to make informed decisions and mitigate the impacts of extreme weather events

Source Apportionment and Forecasting of Aerosol in a Steel City - Case Study of Rourkela

Urban air pollution is one of the biggest problems ascending due to rapid urbanization and industrialization. The improvement of air quality in an urban area in general, constitutes of three phases, monitoring, modeling and control measures. The present research work addresses the requirements of the urban air quality management programme (UAQMP) in Rourkela steel city. A typical UAQMP contains three aspects: monitoring of air pollution, modeling of air pollution and taking control measures. The present study aims to conduct the modeling of particulate air pollution for a steel city. Modeling of particulate matter (PM) pollution is nothing but the application of different mathematical models in source apportionment and forecasting of PM. PM (PM10 and TSP) was collected twice a week for two years (2011-2012) during working hours in Rourkela. The seasonal variations study of PM showed that the aerosol concentration was high during summer and low during monsoon. A detailed chemical characterization of both PM10 and TSP was carried out to find out the concentrations of different metal ions, anions and carbon content. The Spearman rank correlation analysis between different chemical species of PM depicted the presence of both crustal and anthropogenic origins in particulate matter. The enrichment factor analysis highlighted the presence of anthropogenic sources. Three major receptor models were used for the source apportionment of PM, namely chemical mass balance model (CMB), principal component analysis (PCA) and positive matrix factorization (PMF). In selecting source profiles for CMB, an effort has been put to select the profiles which represent the local conditions. Two of the profiles, namely soil dust and road dust, were developed in the present study for better accuracy. All three receptor models have shown that industrial (40-45%) and combustion sources (30-35%) were major contributors to particulate pollution in Rourkela. Artificial neural networks (ANN) were used for the prediction of particulate pollution using meteorological parameters as inputs. The emphasis is to compare the performances of MLP and RBF algorithms in forecasting and provide a rigorous inter-comparison as a first step toward operational PM forecasting models. The training, testing and validation errors of MLP networks are significantly lower than that of RBF networks. The results indicate that both MLP and RBF have shown good prediction capabilities while MLP networks were better than that of RBF networks. There is no profound bias that can be seen in the models which may also suggest that there are very few or zero external factors that may influence the dispersion and distribution of particulate matter in the study area

Advanced Soil Moisture Predictive Methodology in the Maize Cultivation Region using Hybrid Machine Learning Algorithms

The moisture level in the soil in which maize is grown is crucial to the plant's health and production. And over 60% of India's maize cultivation comes from the states of South India. Therefore, forecasting the soil moisture of maize will emerge as a crucial factor for regulating the cultivation of maize crops with optimal irrigation. In light of this, this research provides a unique Improved Hybridized Machine Learning (IHML) model, which combines and optimizes several ML models (base learners-BL). The convergence rate of all the considered BL approaches and the preciseness of the proposed approach significantly enhances the process of determining the appropriate parameters to attain the desirable outcome. Consequently, IHML contributes to an improvement in the accuracy of the overall forecast. This research collects data from districts in South India that are primarily committed to maize agriculture to develop a model. The correlation evaluations served as the basis for the model's framework and the parameter selection. This research compares the outcomes of BL models to the IHML model in depth to ensure the model's accuracy. Results reveal that the IHML performs exceptionally well in forecasting soil moisture, comprising Correlation Coefficient (R2) of 0.9762, Root Mean Square Error (RMSE) of 0.293, and Mean Absolute Error (MAE) of 0.731 at a depth of 10 cm. Conceptual IHML models could be used to make smart farming and precise irrigation much better