Abstract:This paper presents the applicability of neural network (NN) modelling for forecasting and filtering problems. The multilayer feedforward (MLFF) network was first constructed to forecast the tidal-level variations at the mouth of the River Chao Phraya in Thailand. Unlike the well-known conventional harmonic analysis, the NN model uses a set of previous data for learning and then forecasting directly the time-series of tidal levels. It was found that lead time of 1 to 24 hourly tidal levels can be predicted successfully using only a short-time hourly learning data. The MLFF network was further used to establish a stage-discharge relationship for the tidal river. The results show a considerably better performance of the NN model over the conventional models. In addition, the stage-discharge relationship obtained by the NN model can indicate reasonably well the important behaviour of the tidal influences.
Five mainland SEA countries (Cambodia, Laos, Myanmar, Vietnam, and Thailand) are threatened by climate change. Here, the latest 18 Coupled Model Intercomparison Project Phase 6 (CMIP6) is employed to examine future climate change in this region under two SSP-RCP (shared socioeconomic pathway-representative concentration pathway) scenarios (SSP2-4.5 and SSP5-8.5). The bias-corrected multi-model ensemble (MME) projects a warming (wetting) over Cambodia, Laos, Myanmar, Vietnam, and Thailand by 1.88–3.89, 2.04–4.22, 1.88–4.09, 2.03–4.25, and 1.90–3.96 °C (8.76–20.47, 12.69–21.10, 9.54–21.10, 13.47–22.12, and 7.03–15.17%) in the 21st century with larger values found under SSP5-8.5 than SSP2-4.5. The MME model displays approximately triple the current rainfall during the boreal summer. Overall, there are robust increases in rainfall during the Southwest Monsoon (3.41–3.44, 8.44–9.53, and 10.89–17.59%) and the Northeast Monsoon (−2.58 to 0.78, −0.43 to 2.81, and 2.32 to 5.45%). The effectiveness of anticipated climate change mitigation and adaptation strategies under SSP2-4.5 results in slowing down the warming trends and decreasing precipitation trends after 2050. All these findings imply that member countries of mainland SEA need to prepare for appropriate adaptation measures in response to the changing climate.
Abstract:The present study aims to develop a hybrid multi-model using the soft computing approach. The model is a combination of a fuzzy logic, artificial neural network (ANN) and genetic algorithm (GA). While neural networks are low-level computational structures that perform well dealing with raw data, fuzzy logic deal with reasoning on a higher level by using linguistic information acquired from domain experts. However, fuzzy systems lack the ability to learn and cannot adjust themselves to a new environment. Moreover, experts occasionally make mistakes and thus some rules used in a system may be false. A network type structure of the present hybrid model is a multi-layer feed-forward network, the main part is a fuzzy system based on the first-order Sugeno fuzzy model with a fuzzification and a defuzzification processes. The consequent parameters are determined by least square method. The back-propagation is applied to adjust weights of network. Then, the antecedent parameters of the membership function are updated accordingly by the gradient descent method. The GA was applied to select the fuzzy rule. The hybrid multi-model was used to forecast the flood level at Chiang Mai (under the big flood 2005) and the Koriyama flood (2003) in Japan. The forecasting results are evaluated using standard global goodness of fit statistic, efficient index (EI), the root mean square error (RMSE) and the peak flood error. Moreover, the results are compared to the results of a neuro-genetic model (NGO) and ANFIS model using the same input and output variables. It was found that the hybrid multi-model can be used successfully with an efficiency index (EI) more than 0Ð95 (for Chiang Mai flood up to 12 h ahead forecasting) and more than 0Ð90 (for Koriyama flood up to 8 h ahead forecasting). In general, all of three models can predict the water level with satisfactory results. However, the hybrid model gave the best flood peak estimation among the three models. Therefore, the use of fuzzy rule base, which is selected by GA in the hybrid multi-model helps to improve the accuracy of flood peak.
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