Prediction of Algal Bloom Using Genetic Programming

In this study, an attempt was made to mathematically model and predict algal blooms in Tolo Harbor (Hong Kong) using genetic programming (GP). Chlorophyll plays a vital role in blooms and was used in this model as a measure of algal bloom biomass, and eight other variables were used as input for its prediction. It has been observed that GP evolves multiple models with almost the same values of errors-of-measure. Previous studies on GP modeling have primarily focused on comparing GP results with actual values. In contrast, in this study, the main aim was to propose a systematic procedure for identifying the most appropriate GP model from a list of feasible models (with similar error-of-measure) using a physical understanding of the process aided by data interpretation. Evaluation of the GP-evolved equations shows that they correctly identify the ecologically significant variables. Analysis of the final GP-evolved mathematical model indicates that, of the eight variables assumed to affect algal blooms, the most significant effects are due to chlorophyll, total inorganic nitrogen and dissolved oxygen for a 1-week prediction. For longer lead predictions (biweekly), secchi-disc depth and temperature appear to be significant variables, in addition to chlorophyll

Modelling the elements of flash flood hydrograph using genetic programming

1031-1038A novel approach is proposed in this work on constructing the flash flood hydrograph by modelling the elements of the hydrograph namely the time to start of the initial flood (ti), the time to peak discharge (tp), the peak discharge (Qp) and the base time (tb) using Genetic Programming (GP). The proposed method is applied to the Kickapoo River catchment in Wisconsin, USA. It is demonstrated that even under limited data scenario, for a poorly gauged station, GP is able to model the elements of hydrograph with reasonably high accuracy thereby offering considerable lead time to predict the flash flood. The mathematical models developed by GP also offer some understanding of the influence of rainfall events and the stream discharge in producing the flash floods

Spatial and temporal epithelial ovarian cancer cell heterogeneity impacts Maraba virus oncolytic potential

Background: Epithelial ovarian cancer exhibits extensive interpatient and intratumoral heterogeneity, which can hinder successful treatment strategies. Herein, we investigated the efficacy of an emerging oncolytic, Maraba virus (MRBV), in an in vitro model of ovarian tumour heterogeneity. Methods: Four ovarian high-grade serous cancer (HGSC) cell lines were isolated and established from a single patient at four points during disease progression. Limiting-dilution subcloning generated seven additional subclone lines to assess intratumoral heterogeneity. MRBV entry and oncolytic efficacy were assessed among all 11 cell lines. Low-density receptor (LDLR) expression, conditioned media treatments and co-cultures were performed to determine factors impacting MRBV oncolysis. Results: Temporal and intratumoral heterogeneity identified two subpopulations of cells: one that was highly sensitive to MRBV, and another set which exhibited 1000-fold reduced susceptibility to MRBV-mediated oncolysis. We explored both intracellular and extracellular mechanisms influencing sensitivity to MRBV and identified that LDLR can partially mediate MRBV infection. LDLR expression, however, was not the singular determinant of sensitivity to MRBV among the HGSC cell lines and subclones. We verified that there were no apparent extracellular factors, such as type I interferon responses, contributing to MRBV resistance. However, direct cell-cell contact by co-culture of MRBV-resistant subclones with sensitive cells restored virus infection and oncolytic killing of mixed population. Conclusions: Our data is the first to demonstrate differential efficacy of an oncolytic virus in the context of both spatial and temporal heterogeneity of HGSC cells and to evaluate whether it will constitute a barrier to effective viral oncolytic therapy

Modeling studies on the behavior of single and double rubble mound breakwaters using genetic programming tool

Experimental investigation on wave transmission, reflection and dissipation characteristics of rubble mound breakwater models are time consuming and expensive. However, such studies are required for designing the rubble mound breakwaters for marine structures in an optimal condition. In order to overcome such problems many researchers used various soft computing techniques such as Artificial Neural Network (ANN), Adaptive Neuro Fuzzy Interference System (ANFIS), Genetic Programming (GP), Support Vector Machine (SVM) etc, in order to predict the design factors in the field of coastal engineering. The current work proposes Genetic Programming (GP) as a modeling tool to evolve mathematical models for the behavior of single and double breakwaters. Based on the detailed experimental data, GP models were performed to predict the reflected wave height (Hr), wave height on the breakwater (H5) and transmitted wave height (Ht) by considering with and without trigonometric effects of those breakwaters. The quality of predictability of the present model is measured by the statistical parameter, RMSE (Root Mean Square Error). Since the waves were more complex in nature, it is very essential in considering the trigonometric function’s effect in the modeling aspects. It is evident that, the GP model accurately described the non linear complex effects

Streamflow forecasting using least-squares support vector machines

This paper investigates the ability of a least-squares support vector machine (LSSVM) model to improve the accuracy of streamflow forecasting. Cross-validation and grid-search methods are used to automatically determine the LSSVM parameters in the forecasting process. To assess the effectiveness of this model, monthly streamflow records from two stations, Tg Tulang and Tg Rambutan of the Kinta River in Perak, Peninsular Malaysia, were used as case studies. The performance of the LSSVM model is compared with the conventional statistical autoregressive integrated moving average (ARIMA), the artificial neural network (ANN) and support vector machine (SVM) models using various statistical measures. The results of the comparison indicate that the LSSVM model is a useful tool and a promising new method for streamflow forecasting.Editor D. Koutsoyiannis; Associate editor L. SeeCitation Shabri, A. and Suhartono, 2012. Streamflow forecasting using least-squares support vector machines. Hydrological Sciences Journal, 57 (7), 1275-1293

Flow categorization model for improving forecasting

Nordic Hydrology36137-4

Flood stage forecasting with support vector machines

Journal of the American Water Resources Association381173-186JWRA

An Evolutionary Algorithm Approach to Modelling of Tsunami Wave Force

Estimation of design load for tsunami resistant structures is important. Though standard codes such as FEMA exist and are widely used, this study recommends that it is preferable to conduct model studies as per the need to estimate the expected force. The distribution of force can significantly change based on the type and shape of the structure as well any other internal or external arrangements made for its protection. In this study, the specific case of a building without stilt column and provided with barriers as external protection measures is considered. It is demonstrated that Genetic Programming (GP) is a potential modelling technique to model the data/information obtained from force sensors fitted at different levels. For each type of dwelling units, similar independent model studies are recommended to be carried out

An Evolutionary Algorithm Approach to Modelling of Tsunami Wave Force

Estimation of design load for tsunami resistant structures is important. Though standard codes such as FEMA exist and are widely used, this study recommends that it is preferable to conduct model studies as per the need to estimate the expected force. The distribution of force can significantly change based on the type and shape of the structure as well any other internal or external arrangements made for its protection. In this study, the specific case of a building without stilt column and provided with barriers as external protection measures is considered. It is demonstrated that Genetic Programming (GP) is a potential modelling technique to model the data/information obtained from force sensors fitted at different levels. For each type of dwelling units, similar independent model studies are recommended to be carried out

Ascertaining Time Series Predictability in Process Control: Case Study on Rainfall Prediction

Rainfall prediction is a challenging task due to its dependency on many natural phenomenon. Some authors used Hurst exponent as a predictability indicator to ensure predictability of the time series before prediction. In this paper, a detailed analysis has been done to ascertain whether a definite relation exists between a strong Hurst exponent and predictability. The one-lead monthly rainfall prediction has been done for 19 rain gauge station of the Yarra river basin in Victoria, Australia using Artificial Neural Network. The prediction error in terms of normalized Root Mean Squared Error has been compared with Hurst exponent. The study establishes the truth of the hypothesis for only 6 stations out of 19 stations, and thus recommends further investigation to prove the hypothesis. This concept is relevant for any time series which need to be used for real time process control