Model Calibration and Validation for the Fuzzy-EGARCH-ANN Model

This work shown as the fuzzy-EGARCH-ANN (fuzzy-exponential generalized autoregressive conditional heteroscedastic-artificial neural network) model does not require continuous model calibration if the corresponding DE algorithm is used appropriately, but other models such as GARCH, EGARCH, and EGARCH-ANN need continuous model calibration and validation so they fit the data and reality very well up to the desired accuracy. Also, a robust analysis of volatility forecasting of the daily S&P 500 data collected from Yahoo Finance for the daily spanning period 1/3/2006 to 20/2/2020. To our knowledge, this is the first study that focuses on the daily S&P 500 data using high-frequency data and the fuzzy-EGARCH-ANN econometric model. Finally, the research finds that the best performing model in terms of one-step-ahead forecasts based on realized volatility computed from the underlying daily data series is the fuzzy-EGARCH-ANN (1,1,2,1) model with Student’s t-distribution.

Estimating Genetic Parameters for DSSAT CROPGRO-Cotton Model Calibration and Validation

DSSAT CROPGRO-Cotton Model (version 4.7.5) was generally used to forecast the effect of climate change on productivity. The objective of this study was to calibrate and validate this model in Tamil Nadu, India for simulation of development, growth and seed cotton yield of Suraj cotton cultivars under varied planting dates viz., 28th July, 11th August, 18th August, 25th August, 8th September and 15th September. The model was calibrated with data (phenology, biomass and yield components) collected during 2019. Calibration of CROPGRO-Cotton model with genetic coefficients of cultivar Suraj for seed cotton yield (kg ha-1). Simulation of days to flowering, days from planting to first pod and physiological maturity, LAI and seed cotton yield with normalized RMSE (NRMSE) values of less than 10% across all the various planting dates densities were considered excellent. Finally, we discovered that planting at the right time can mitigate many of the negative effects of fluctuating weather on cotton productivity. As a result conclude that DSSAT model will be used to make decision on cotton planting in changing climates.

Flood Risk Assessment in Urban Areas of Southern Taiwan

A flood risk assessment of urban areas in Kaohsiung city along the Dianbao River was performed based on flood hazards and social vulnerability. In terms of hazard analysis, a rainfall-runoff model (HEC-HMS) was adopted to simulate discharges in the watershed, and the simulated discharges were utilized as inputs for the inundation model (FLO-2D). Comparisons between the observed and simulated discharges at the Wulilin Bridge flow station during Typhoon Kongrey (2013) and Typhoon Megi (2016) were used for the HEC-HMS model calibration and validation, respectively. The observed water levels at the Changrun Bridge station during Typhoon Kongrey and Typhoon Megi were utilized for the FLO-2D model calibration and validation, respectively. The results indicated that the simulated discharges and water levels reasonably reproduced the observations. The validated model was then applied to predict the inundation depths and extents under 50-, 100-, and 200-year rainfall return periods to form hazard maps. For social vulnerability, the fuzzy Delphi method and the analytic hierarchy process were employed to select the main factors affecting social vulnerability and to yield the weight of each social vulnerability factor. Subsequently, a social vulnerability map was built. A risk map was developed that compiled both flood hazards and social vulnerability levels. Based on the risk map, flood mitigation strategies with structural and nonstructural measures were proposed for consideration by decision-makers.