Streamflow and soil moisture forecasting with hybrid data intelligent machine learning approaches: case studies in the Australian Murray-Darling basin

For a drought-prone agricultural nation such as Australia, hydro-meteorological imbalances and increasing demand for water resources are immensely constraining terrestrial water reservoirs and regional-scale agricultural productivity. Two important components of the terrestrial water reservoir i.e., streamflow water level (SWL) and soil moisture (SM), are imperative both for agricultural and hydrological applications. Forecasted SWL and SM can enable prudent and sustainable decisionmaking for agriculture and water resources management. To feasibly emulate SWL and SM, machine learning data-intelligent models are a promising tool in today’s rapidly advancing data science era. Yet, the naturally chaotic characteristics of hydro-meteorological variables that can exhibit non-linearity and non-stationarity behaviors within the model dataset, is a key challenge for non-tuned machine learning models. Another important issue that could confound model accuracy or applicability is the selection of relevant features to emulate SWL and SM since the use of too fewer inputs can lead to insufficient information to construct an accurate model while the use of an excessive number and redundant model inputs could obscure the performance of the simulation algorithm. This research thesis focusses on the development of hybridized dataintelligent models in forecasting SWL and SM in the upper layer (surface to 0.2 m) and the lower layer (0.2–1.5 m depth) within the agricultural region of the Murray-Darling Basin, Australia. The SWL quantifies the availability of surface water resources, while, the upper layer SM (or the surface SM) is important for surface runoff, evaporation, and energy exchange at the Earth-Atmospheric interface. The lower layer (or the root zone) SM is essential for groundwater recharge purposes, plant uptake and transpiration. This research study is constructed upon four primary objectives designed for the forecasting of SWL and SM with subsequent robust evaluations by means of statistical metrics, in tandem with the diagnostic plots of observed and modeled datasets. The first objective establishes the importance of feature selection (or optimization) in the forecasting of monthly SWL at three study sites within the Murray-Darling Basin. Artificial neural network (ANN) model optimized with iterative input selection (IIS) algorithm named IIS-ANN is developed whereby the IIS algorithm achieves feature optimization. The IIS-ANN model outperforms the standalone models and a further hybridization is performed by integrating a nondecimated and advanced maximum overlap discrete wavelet transformation (MODWT) technique. The IIS selected inputs are transformed into wavelet subseries via MODWT to unveil the embedded features leading to IIS-W-ANN model. The IIS-W-ANN outperforms the comparative IIS-W-M5 Model Tree, IIS-based and standalone models. In the second objective, improved self-adaptive multi-resolution analysis (MRA) techniques, ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) are utilized to address the non-stationarity issues in forecasting monthly upper and lower layer soil moisture at seven sites. The SM time-series are decomposed using EEMD/CEEMDAN into respective intrinsic mode functions (IMFs) and residual components. Then the partial-auto correlation function based significant lags are utilized as inputs to the extreme learning machine (ELM) and random forest (RF) models. The hybrid EEMD-ELM yielded better results in comparison to the CEEMDAN-ELM, EEMD-RF, CEEMDAN-RF and the classical ELM and RF models. Since SM is contingent upon many influential meteorological, hydrological and atmospheric parameters, for the third objective sixty predictor inputs are collated in forecasting upper and lower layer soil moisture at four sites. An ANN-based ensemble committee of models (ANN-CoM) is developed integrating a two-phase feature optimization via Neighborhood Component Analysis based feature selection algorithm for regression (fsrnca) and a basic ELM. The ANN-CoM shows better predictive performance in comparison to the standalone second order Volterra, M5 Model Tree, RF, and ELM models. In the fourth objective, a new multivariate sequential EEMD based modelling is developed. The establishment of multivariate sequential EEMD is an advancement of the classical single input EEMD approach, achieving a further methodological improvement. This multivariate approach is developed to allow for the utilization of multiple inputs in forecasting SM. The multivariate sequential EEMD optimized with cross-correlation function and Boruta feature selection algorithm is integrated with the ELM model in emulating weekly SM at four sites. The resulting hybrid multivariate sequential EEMD-Boruta-ELM attained a better performance in comparison with the multivariate adaptive regression splines (MARS) counterpart (EEMD-Boruta-MARS) and standalone ELM and MARS models. The research study ascertains the applicability of feature selection algorithms integrated with appropriate MRA for improved hydrological forecasting. Forecasting at shorter and near-real-time horizons (i.e., weekly) would help reinforce scientific tenets in designing knowledge-based systems for precision agriculture and climate change adaptation policy formulations

New single VDCC Based Electronically Adjustable FDNR using Grounded Capacitances

A FDNR (Frequency Dependent Negative Resistance) simulator has been presented, which uses single Voltage Differencing Current Conveyor (VDCC) along with two grounded capacitances to realize the pure FDNR function. The proposed FDNR simulator places itself as one of the most compact circuit architecture present in the available literature. It is a resistor-less realization and finds no requirement of any active/passive component/parameter matching to realize the FDNR behaviour. The presented analysis shows that the proposed circuit remains always stable under the effect of parasitics irrespective of any values of circuit parameters, which is not witnessed in many previously reported FDNRs. The usability of realized synthetic FDNR has been checked through validation of developed higher-order CDR filters by using the proposed FDNR. To verify the designed circuits, the presented configurations have been simulated under the PSPICE simulation environment.  The experimental results are shown for the commercial ICs (AD844 and CA3080) based physical realization of described FDNR

Designing Deep-based Learning Flood Forecast Model with ConvLSTM Hybrid Algorithm

Efficient, robust, and accurate early flood warning is a pivotal decision support tool that can help save lives and protect the infrastructure in natural disasters. This research builds a hybrid deep learning (ConvLSTM) algorithm integrating the predictive merits of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network to design and evaluate a flood forecasting model to forecast the future occurrence of flood events. Derived from precipitation dataset, the work adopts a Flood Index (IF), in form of a mathematical representation, to capture the gradual depletion of water resources over time, employed in a flood monitoring system to determine the duration, severity, and intensity of any flood situation. The newly designed predictive model utilizes statistically significant lagged IF, improved by antecedent and real-time rainfall data to forecast the next daily IF value. The performance of the proposed ConvLSTM model is validated against 9 different rainfall datasets in flood prone regions in Fiji which faces flood-driven devastations almost annually. The results illustrate the superiority of ConvLSTM-based flood model over the benchmark methods, all of which were tested at the 1-day, 3-day, 7-day, and the 14-day forecast horizon. For instance, the Root Mean Squared Error (RMSE) for the study sites were 0.101, 0.150, 0.211 and 0.279 for the four forecasted periods, respectively, using ConvLSTM model. For the next best model, the RMSE values were 0.105, 0.154, 0.213 and 0.282 in that same order for the four forecast horizons. In terms of the difference in model performance for individual stations, the Legate-McCabe Efficiency Index (LME) were 0.939, 0.898, 0.832 and 0.726 for the four forecast horizons, respectively. The results demonstrated practical utility of ConvLSTM in accurately forecasting IF and its potential use in disaster management and risk mitigation in the current phase of extreme weather events

Deep Air Quality Forecasts: Suspended Particulate Matter Modeling With Convolutional Neural and Long Short-Term Memory Networks

Public health risks arising from airborne pollutants, e.g ., Total Suspended Particulate ( TSP ) matter, can significantly elevate ongoing and future healthcare costs. The chaotic behaviour of air pollutants posing major difficulties in tracking their three-dimensional movements over diverse temporal domains is a significant challenge in designing practical air quality systems. This research paper builds a deep learning hybrid CLSTM model where convolutional neural network ( CNN ) is amalgamed with the long short-term memory ( LSTM ) network to forecast hourly TSP . The CNN model entails a data processer including feature extractors that draw upon statistically significant antecedent lagged predictor variables, whereas the LSTM model encapsulates a new feature mapping scheme to predict the next hourly TSP value. The hybrid CLSTM model is comprehensively benchmarked and is seen to outperform an ensemble of five machine learning models. The efficacy of the CLSTM model is elucidated in model testing phase at study sites in Queensland, Australia. Using performance metrics, visual analysis of TSP simulations relative to observations, and detailed error analysis, this study ascertains the CLSTM model’s practical utility for air pollutant forecasting systems in health risk mitigation. This study captures a feasible opportunity to emulate air quality at relatively high temporal resolutions in global regions where air pollution is a considerable threat to public health

Development of Flood Monitoring Index for daily flood risk evaluation: case studies in Fiji

Both fluvial and pluvial floods are a common occurrence in Fiji with fluvial floods causing significant economic consequences for island nations. To investigate flood risk and provide a mitigation tool on daily basis, the Flood Index (IF) is developed based on the rationale that the onset and severity of an event is based on current and antecedent day’s precipitation. This mathematical methodology considers the notion that the impact of daily cumulative precipitation on a particular flood event arising from a previous day’s precipitation, decreasing gradually over time due to the interaction of hydrological factors (e.g., evaporation, percolation, seepage, surface run-off, drainage, etc.,). These are accounted for, mathematically, by a time-reduction weighted precipitation influencing the magnitude of IF. Considering the duration, severity and intensity of all identified events, the applicability of IF is tested at 9 study sites in Fiji using 30-year precipitation datasets (1990–2019) obtained from Fiji Meteorological Services. Newly developed IF is adopted at flood prone sites, with results demonstrating that flood events were common throughout the country, mostly notable between November to April (or the wet season). Upon examining the variations in daily IF, the flood properties were determined, showing that the most severe events generally started in January. Flood events with the highest severity were recorded in Lautoka [IaccF (flood severity) ≈149.14, ImaxF (peak danger) ≈3.39, DF (duration of flood) ≈151days, tonset (onset date) =23rdJanuary2012], followed by Savusavu (IaccF≈141.65,ImaxF≈1.75,DF≈195days,tonset=27thNovember1999) and Ba (IaccF≈131.57,ImaxF≈3.13,DF≈113days,tonset=9thJanuary2009). The results clearly illustrate the practicality of daily IF in determining the duration, severity, and intensity of flood situation, as well as its potential application to small island nations. The use of daily IF to quantify flood events can therefore enable a cost-effective and innovative solution to study historical floods in both developing and first world countries. Our methodology is particularly useful to governments, private organizations, non-governmental organizations and communities to help develop community-amicable policy and strategic plans to prepare for flood impacts and undertake the necessary risk mitigation measures

Change detection of a coastal woodland mangrove forest in Fiji by integration of remote sensing with spatial mapping

Mangroves play key ecological role in structuring the availability of coastal resources. The current study was focused on change detection in a large mangrove patch located in Votua area of the Ba province in Fiji. Globally, the mangrove population continues to decline with the changes in climatic conditions and anthropo-genic activities. Baseline information through wetland maps and time series change are essential references for the development of effective mangrove management plans. These maps reveal the status of the resource over a period of time and the impacts from anthropogenic activities. Remote sensing techniques were integrated with geographic information system tools for mapping and detecting temporal change over a period of 20 years. Remotely sensed imagery data from Landsat satellite was sourced from the year 1999 to 2018 for this investigation. The mapping analysis of temporal changes in mangrove forests was carried using the versatile ArcGIS and ENVI software. The pilot change detection analysis revealed a small but important change in the mangrove patch over these years. Landward creep of mangroves was also detected. The outcomes of this study serve as baseline and conservation information for the development and implementation of effective management plans for one of Fiji’s largest mangrove patches

Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition

Data-intelligent algorithms designed for forecasting significant height of coastal waves over the relatively short time period in coastal zones can generate crucial information about enhancing the renewable energy production. In this study, a machine learning model is designed and evaluated for forecasting significant wave height (Hs) within the eastern coastal zones of Australia. The extreme learning machine (ELM) model is coupled with an improved complete ensemble empirical mode decomposition method with adaptive noise (ICEEMDAN) to design the proposed ICEEMDAN-ELM model. This model incorporates the historical lagged series of Hs as the model's predictor to forecast future Hs. The ICEEMDAN algorithm demarcates the original Hs data from January-2000 to March-2018, recorded at 30-min intervals, into decomposed signals i.e., intrinsic mode functions (IMFs) and a residual component. After decomposition, the partial autocorrelation function is determined for each IMF and the residual sub-series to determine the statistically significant lagged input dataset. The ELM model is applied for forecasting of each IMF by incorporating the significant antecedent Hs sub-series as inputs. Finally, all the forecasted IMFs are summed up to obtain the final forecasted Hs. The results are benchmarked with those from an online sequential extreme learning machine (OSELM) and random forest (RF) integrated with ICEEMDAN, i.e., the ICEEMDAN-OSELM and ICEEMDAN-RF models. The proposed ICEEMDAN-ELM model is tested geographically at two coastal sites of the Queensland state, Australia. The testing performance of all the standalone (ELM, OSELM, RF) and integrated models (ICEEMDAN-ELM, ICEEMDAN-OSELM, ICEEMDAN-RF), according to robust statistical error metrics, is satisfactory; however, the hybrid ICEEMDAN-ELM model is found to be a beneficial Hs forecasting tool in accordance to high performance accuracy. The proposed ICEEMDAN-ELM model can be considered as a pertinent decision-support framework and is vital for designing of reliable ocean energy converters

Receiver DCB estimation and GPS vTEC study at a low latitude station in the South Pacific

The statistical estimation of receiver differential code bias (DCB) of the GSV4004B receiver at a low latitude station, Suva (lat. 18.15°S, long. 178.45°E, Geomag. Lat. 21.07°S), Fiji, and the subsequent behaviour of vTEC, are presented. By means of least squares linear regression fitting technique, the receiver DCB was determined using the GPS vTEC data recorded during the year 2010, CODE TEC and IRI-2012 model for 2010. To substantiate the results, minimization of the standard deviation (SD) method was also used for GPS vTEC data. The overall monthly DCB was estimated to be in the range of 62.6 TECU. The vTEC after removing the resultant monthly DCB was consistent with other low latitude observations. The GPS vTEC 2010 data after eliminating the resultant DCB were lower in comparison to Faraday rotation vTEC measurements at Suva during 1984 primarily due to higher solar activity during 1984 as compared to 2010. Seasonally, vTEC was maximum during summer and minimum during winter. The winter showed least vTEC variability whereas equinox showed the largest daytime variability. The geomagnetic disturbances effect showed that both vTEC and its variability were higher on magnetically disturbed days as compared to quiet days with maximum variability in the daytime. Two geomagnetic storms of moderate strengths with main phases in the local daytime showed long duration (∼52 h) increase in vTEC by 33–67% which can be accounted by changes in E×B drifts due to prompt penetration of storm-time auroral electric field in the daytime and disturbance dynamo electric field in the nighttime to low latitudes

Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation

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