Cooperative neuro - evolution of Elman recurrent networks for tropical cyclone wind - intensity prediction in the South Pacific region

Climate change issues are continuously on the rise and the need to build models and software systems for management of natural disasters such as cyclones is increasing. Cyclone wind-intensity prediction looks into efficient models to forecast the wind-intensification in tropical cyclones which can be used as a means of taking precautionary measures. If the wind-intensity is determined with high precision a few hours prior, evacuation and further precautionary measures can take place. Neural networks have become popular as efficient tools for forecasting. Recent work in neuro-evolution of Elman recurrent neural network showed promising performance for benchmark problems. This paper employs Cooperative Coevolution method for training Elman recurrent neural networks for Cyclone wind- intensity prediction in the South Pacific region. The results show very promising performance in terms of prediction using different parameters in time series data reconstruction

Development of statistical and geospatial-based framework for drought-risk assessment

Drought is an insidious, complex and one of the least understood natural phenomena resulting from a deficiency of water resources. While droughts cannot be prevented, its impacts, however, can be mitigated through proper design of water storage infrastructure and management strategies. A comprehensive drought management plan necessitates the development of a framework that can help reduce the drought-related risk. In Australia, there are limited drought vulnerability and risk assessment models that must (1) include the drought monitoring index that measures the supply-demand balance of water resources, (2) incorporate large-scale climate drivers influencing amplitude of drought events in the statistical prediction models, and (3) objectively quantify the drought-risk on both temporal and spatial scales. The goal of this study is to apply statistical and geospatial tools in developing a framework for assessing drought-related risks in light of improving the drought mitigation strategies. A new, temporal and spatial-explicit analytical framework for drought-risk assessment is developed based on three objectives focussed in the drought-prone southeast Queensland (SEQ) region. (1) Evaluating and affirming the suitability of the Standardised Precipitation-Evapotranspiration Index (SPEI) for the characterisation of drought events. (2) Developing a copula-based statistical, probabilistic model for predicting the SPEI and the jointly distributed drought properties (i.e., durations, severities and intensities) conditional on the large-scale climate mode indices. (3) Developing a spatially descriptive drought-risk index by combining the drought hazard, exposure and vulnerability factors using a fuzzy logic algorithm. The first objective of this study demonstrates the scientific relevance of the SPEI as a robust drought assessment metric that incorporates the influence of water supply-demand balance on drought events. Subsequently, the severity (S; accumulated negative SPEI in a drought-identified period), intensity (I; minimum SPEI) and the duration (D; number of months with continuously negative SPEI representing the below average water resources) based on run-sum approach are enumerated to identify historical water deficit periods. Significant disparities in the identified D-S-I affirms the significance of SPEI for regional drought impact assessments. Accordingly, this study advocates the SPEI as a convenient metric for detecting drought onsets and terminations, including its ability for drought ranking and drought recurrence evaluations that are considered vital for water resource management. The second objective models the joint behaviour of SPEI and D-S-I properties using copula model, conditional upon the pertinent climate mode indices (i.e., El-Niño Southern Oscillation indicators). The vine copula algorithm is employed to derive the bivariate and trivariate joint-distributions of drought variables for conditional probability-based predictions. The results yield marginal differences between the observed and the predicted drought properties, elucidating the effectiveness of copula functions in drought-risk modelling. The results have implications for drought and aridity management in agricultural regions where complex relationships between climate drivers and drought properties are likely to exacerbate the risk of a future event. The third objective develops a methodology using vulnerability, exposure and hazard indicators to provide a spatio-temporal framework for drought-risk assessment. The conditional joint probability of each drought indicator is estimated using the Bayes theorem. Various fuzzy membership functions are then applied to standardise and aggregate the indicators to derive drought vulnerability, exposure and hazard indices. The resulting indices are integrated with fuzzy GAMMA overlay operation to generate optimal drought-risk maps. The maps reveal varying levels of drought risk in different austral seasons and annually that is well represented by the drought hazard index, i.e., rainfall departure. The validation of the method with respect to the upper and lower layer soil moisture reveal significant correlations with the spatial drought-risk index. It is therefore prudent to state that the fuzzy logic-based analytical technique applied for spatio-temporal drought-risk mapping can be considered as a practical tool that can enable better drought management, drought mitigation and relief-planning decisions. The statistically and spatially relevant drought-risk assessments frameworks formulated in this study provides promising outcomes that are valuable for the mitigation of drought impacts, and therefore, sets a pathway to construct strategic planning procedures and management of water resources in drought-prone, arid or semi-arid regions

Application of cooperative neuro - evolution of Elman recurrent networks for a two - dimensional cyclone track prediction for the South Pacific region

This paper presents a two-dimensional time series prediction approach for cyclone track prediction using cooperative neuro-evolution of Elman recurrent networks in the South Pacific region. The latitude and longitude of tracks of cyclone lifetime is taken into consideration for past three decades to build a robust forecasting system. The proposed method performs one step ahead prediction of the cyclone position which is essentially a two-dimensional time series prediction problem. The results show that the Elman recurrent network is able to achieve very good accuracy in terms of prediction of the tracks which can be used as means of taking precautionary measures

Coevolutionary recurrent neural networks for prediction of rapid intensification in wind intensity of tropical cyclones in the South Pacific region

Rapid intensification in tropical cyclones occur where there is dramatic change in wind-intensity over a short period of time. Recurrent neural networks trained using cooperative coevolution have shown very promising performance for time series prediction problems. In this paper, they are used for prediction of rapid intensification in tropical cyclones in the South Pacific region. An analysis of the tropical cyclones and the occurrences of rapid intensification cases is assessed and then data is gathered for recurrent neural network for rapid intensification predication. The results are promising that motivate the implementation of the system in future using cloud computing infrastructure linked with mobile applications to create awareness

Climate variations of genesis and rapid intensification of tropical cyclones in the southern hemisphere

M.S. University of Hawaii at Manoa 2014.Includes bibliographical references.This thesis focuses on short-term climate variation of tropical cyclones in the Southern Hemispheric Ocean. Two major research areas are explored: (1) the modulation of tropical cyclone genesis by the Madden-Julian Oscillation (MJO), and (2) seasonal and intraseasonal variability of rapid intensification (RI). The observed modulation of tropical cyclone (TC) genesis is examined using 32-years of outgoing longwave radiation (OLR), reanalysis winds and TC best track data. A newly introduced MJO index based on the convective anomalies of large OLR variability centers shows a much stronger modulation of TC genesis by MJO than previously detected. An increased number of TC formations are observed during the enhanced convective phase of MJO than during a dry phase. The modulation is more pronounced to the east of 70°E in South-Indian Ocean (SIO) with a modulation ratio of 2:1 and to the west of 170°W in the SPO with a modulation ratio of 7:1. The stronger modulation in SPO is mainly due to: (1) MJO-induced wind fields are larger than the background mean flow, (2) TC genesis location being consistent with MJO basic state, i.e., TCs co-occur over the region of MJO-induced low-level circulation and enhanced convection, and (3) TC genesis occurs in the South-Pacific convergence zone upon which MJO has a strong modulation. Analysis of large-scale dynamic and thermodynamic environmental conditions reveal low-level relative vorticity is strongly attributable to TC genesis modulation in both ocean basins where SPO has an additional contribution from mid-tropospheric relative humidity that is also modulated by MJO-induced perturbations. The MJO has little effect on TC genesis in SW Indian Ocean because of the existence of favorable climatological environmental conditions throughout the TC season, and TCs form further away from the equator where the MJO signal is very weak or non-existent

Drought modelling based on artificial intelligence and neural network algorithms: a case study in Queensland, Australia

The search for better climate change adaptation techniques for addressing environmental and economic issues due to changing climate is of paramount interest in the current era. One of the many ways Pacific Island regions and its people get affected is by dry spells and drought events from extreme climates. A drought is simply a prolonged shortage of water supply in an area. The impact of drought varies both temporally and spatially that can be catastrophic for such regions with lack of resources and facilities to mitigate the drought impacts. Therefore, forecasting drought events using predictive models that have practical implications for understanding drought hydrology and water resources management can allow enough time to take appropriate adaption measures. This study investigates the feasibility of the Artificial Neural Network (ANN) algorithms for prediction of a drought index: Standardized Precipitation-Evapotranspiration Index (SPEI). The purpose of the study was to develop an ANN model to predict the index in two selected regions in Queensland, Australia. The first region, is named as the grassland and the second as the temperate region. The monthly gridded meteorological variables (precipitation, maximum and minimum temperature) that acted as input parameters in ANN model were obtained from Australian Water Availability Project (AWAP) for 1915–2013 period. The potential evapotranspiration (PET), calculated using thornthwaite method, was also an input variable, while SPEI was the predictand for the ANN model. The input data were divided into training (80%), validation (10%) and testing (10%) sets. To determine the optimum ANN model, the Levenberg-Marquardt and Broyden-Fletcher-Goldfarb-Shanno quasi-Newton backpropagation algorithms were used for training the ANN network and the tangent sigmoid, logarithmic sigmoid and linear activation algorithms were used for hidden transfer and output functions. The best architecture of input-hidden neuron-output neurons was 4-28-1 and 4-27-1 for grassland and temperate region, respectively. For evaluation and selection of the optimum ANN model, the statistical metrics: Coefficient of Determination (R 2 ), Willmott’s Index of Agreement (d), Nash-Sutcliffe Coefficient of Efficiency (E), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were employed. The R 2 , d, E, RMSE and MAE for optimum ANN models were 0.9839, 0.9909, 0.9838, 0.1338, 0.0882 and 0.9886, 0.9935, 0.9874, 0.1198, 0.0814 for grassland and temperate region, respectively. When prediction errors were analysed, a value of 0.0025 to 0.8224 was obtained for the grassland region, and a value of 0.0113 to 0.6667 was obtained for the temperate region, indicating that the ANN model exhibit a good skill in predicting the monthly SPEI. Based on the evaluation and statistical analysis of the predicted SPEI and its errors in the test period, we conclude that the ANN model can be used as a useful data-driven tool for forecasting drought events. Broadly, the ANN model can be applied for prediction of other climate related variables, and therefore can play a vital role in the development of climate change adaptation and mitigation plans in developed and developing nations, and most importantly, in the Pacific Island Nations where drought events have a detrimental impact on economic development

Modulation of tropical cyclone genesis by Madden–Julian Oscillation in the Southern Hemisphere

This chapter advances intelligent methodologies to study the modulation of cyclone genesis. Tropical cyclones (TCs) are hazardous weather elements with detrimental impacts on populations, wildlife, ecosystems, infrastructure, and the economy of developed as well as developing nations. Understanding the climatological behavior of TCs in relation to onsets, origin, and causal factors conductive to cyclogenesis can aid in the risk-management of cyclone vulnerability. This chapter studies the observed modulation of TC genesis in two study regions, namely the South Indian Ocean (SIO: 0–30° S, 30° E–130° E) and the South Pacific Ocean (SPO: 0–30° S, 130° E–130° W) was examined for the period 1980–2012. We define regional Madden–Julian Oscillation (MJO) indices based on the convective anomalies of large OLR variability centers, which exhibit a stronger modulation of the TC genesis than previously identified. Overall, an increase in the number of TC formations was recorded for the enhanced convective phase of the MJO compared to the dry phase. The modulation of TC genesis by MJO appeared to be pronounced with a ratio of 2:1 to the east of 70° E (for the SIO) and 7:1 to the west of 170° W (for the SPO). Stronger modulation in the latter region is attributable to (1) MJO-induced wind field impacts that were notably larger than the background mean flow, (2) TC genesis locations being consistent with MJO action centers, i.e., the TCs occur over the region of the MJO-induced low-level circulation with enhanced convection, and (3) TC genesis occurs in the South Pacific Convergence Zone (SPCZ), a region where MJO has a strong modulating effect. An analysis of large-scale dynamic and thermodynamic conditions demonstrated that low-level relative vorticity was strongly related to TC genesis modulation in both the SIO and SPO regions. However, the MJO appears to show little effect on TC genesis in the western SIO due to the existence of climatological conditions less conducive to TC formation throughout the cyclonic season. Finally, the chapter ascertains that TCs are generally produced further from the equatorial region in the southwest Indian zone where the MJO signal appears to be very weak

Spatio-temporal drought risk mapping approach and its application in the drought-prone region of south-east Queensland, Australia

Strategic management of water resources in drought-vulnerable regions can be greatly hampered by frequent, severe and long-lasting droughts. To enable better drought relief policy and amicable solutions and proactive actions for preparedness and mitigation of drought impacts, this study adopts a spatio-temporal methodology for the assessment of drought risk of drought-prone areas in south-east Queensland, Australia. In this study, the spatially representative depiction of the drought risk in a drought-prone region with multiple vulnerability, exposure and drought hazard indicators is considered in order to develop a geographic information systems-based drought risk mapping tool. Spatial indicators of drought are categorised into various subclasses, and the conditional joint probability of each indicator is the determined in accordance with the Bayes theorem. The fuzzy logic approach is then embraced as a new approach in this study to standardise the different drought factors on a range of 0–1 followed by an aggregation of drought vulnerability, exposure and hazard indices using the fuzzy GAMMA overlay operation in ArcGIS 10.5 to produce the optimal drought risk map for the case study region. The analysis of drought’s different phases shows varying vulnerability levels in different austral seasons (summer, autumn and spring of 2007) and annually (2007, 2009 and 2013) that is well represented by drought hazard index, i.e. rainfall departure. The application of the fuzzy set to incorporate and classify drought factors reveals its useful implications for handling of spatial drought-related data and the development of the drought risk index. The validation of the method performed with upper and lower layer soil moisture data reveals significant correlation with the drought risk index. The study has implications for drought risk mapping, particularly in utilising the ability of the fuzzy logic-based analytical technique integrated with GIS-based mapping tools for spatio-temporal drought risk studies. The approach in this paper can be considered as a practical mapping tool for drought studies, to better enable drought management, drought mitigation and relief-planning actions that need to be implemented by different decision-makers in water resources, agriculture and other socio-economic areas

Investigating drought duration-severity-intensity characteristics using the Standardized Precipitation-Evapotranspiration Index: case studies in drought-prone Southeast Queensland

Drought characterization is crucial for identifying impacts on irrigation, agriculture, hydrologic engineering, and water resources management. This case study demonstrates the scientific relevance of the standardized precipitation-evapotranspiration index (SPEI) as a robust drought metric that incorporates influence of supply-demand balance. Using long-term data, the SPEI was calculated at multiple timescales to identify historical water deficit periods in selected drought-prone case study regions in southeast Queensland, Australia. The drought duration (D; number of months with continuously negative SPEI representing below average water resources), severity (S; accumulated negative SPEI in a drought-identified period), intensity (I; minimum SPEI), and return periods were enumerated for iconic dry events over multiple (1-, 3-, 6-, 9-, 12-, and 24-month) timescales. The SPEI was evaluated with corresponding drought indicators (precipitation and soil moisture) and climatological Rainfall Anomaly Index to yield drought severity information from a meteorological perspective. The results showed disparities in duration, severity, and intensity (D–S–I) of different droughts among the case study regions; reaffirming the significance of SPEI for regional drought impact assessment. Accordingly, this case study advocates SPEI as a convenient metric for detecting drought onsets and terminations, including drought ranking and recurrence evaluations that are vital statistics in hydrologic engineering

Application of hybrid artificial neural network algorithm for the prediction of Standardized Precipitation Index

The application of wavelet transformation has become a popular area of interest in hydrological modeling as it enables the use of spectral and temporal information contained in input data. Drought modeling is one such area that is still far from complete, considering the stochastic nature of drought characteristics per every drought events. This study therefore aims to predict a drought index, i.e. the Standardized Precipitation Index (SPI), using artificial neural network (ANN) and a hybrid ANN with wavelet analysis (WA-ANN) using four main inputs: precipitation, potential evapotranspiration, Southern Oscillation Index, and Nino 4 index for Brisbane, Australia. For WA-ANN, the four inputs were decomposed into three detail and one approximation levels using Daubechies-4 (db4) orthogonal mother wavelet. The evaluation of prediction performance showed that WA-ANN outperformed ANN model with an increased accuracy by 49.89% based on Root Mean Squared Error values