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Accurate flood forecasts provide a critical time for authorities and the public to enact flood response measures and initiate evacuations. Long short-term memory (LSTM) is widely used in flood forecasting to ensure sufficient response time. The lag length of rainfall (LR), the number of hidden layers (NL), and the number of neurons (NN) are key parameters of the LSTM model. However, flood forecasting research seldom explores their optimization via the Genetic Algorithm (GA). This study introduces a novel LSTM-GA model, which integrates LSTM with GA to optimize the LR, NL, and NN for flash flood forecasting. The case study pertains to the water level forecasting of the Wu River in Taiwan. To assess the improvement brought by the proposed model, a standard LSTM model was utilized as a benchmark. This model accurately forecasted floods in the next 1 to 6 h, achieving a Nash–Sutcliffe efficiency coefficient (NSE) score ranging from 0.896 to 0.906. It also exhibited strong flood peak forecast performance. The integration of GA enhanced the LSTM’s forecasting accuracy, with NSE scores rising to between 0.917 and 0.931. Notably, a shorter forecast lead time augmented the degree of improvement. In the LSTM model, LR was set as the river’s concentration time, and NL represented the water storage function of the watershed. For short lead time forecasting, surface runoff was the dominant factor, leading to smaller optimized values for LR and NL. Conversely, long lead time forecasting needed to consider the impact of subsurface and groundwater runoff, resulting in larger optimized values for LR and NL. In conclusion, the parameters optimized through GA consider the watershed’s characteristics.

Oil is an important energy commodity. The difficulties of forecasting oil prices stem from the nonlinearity and non-stationarity of their dynamics. However, the oil prices are closely correlated with global financial markets and economic conditions, which provides us with sufficient information to predict them. Traditional models are linear and parametric, and are not very effective in predicting oil prices. To address these problems, this study developed a new strategy. Deep (or hierarchical) multiple kernel learning (DMKL) was used to predict the oil price time series. Traditional methods from statistics and machine learning usually involve shallow models; however, they are unable to fully represent complex, compositional, and hierarchical data features. This explains why traditional methods fail to track oil price dynamics. This study aimed to solve this problem by combining deep learning and multiple kernel machines using information from oil, gold, and currency markets. DMKL is good at exploiting multiple information sources. It can effectively identify the relevant information and simultaneously select an apposite data representation. The kernels of DMKL were embedded in a directed acyclic graph (DAG), which is a deep model and efficient at representing complex and compositional data features. This provided a solid foundation for extracting the key features of oil price dynamics. By using real data for empirical testing, our new system robustly outperformed traditional models and significantly reduced the forecasting errors.

This paper examines the spillovers of US credit supply to small open economies by focusing on three Asian economies, Korea, Singapore, and Taiwan, from 1999 to 2019. We employ a proxy vector autoregressive model (VAR) to identify credit supply shocks in the US and estimate dynamic responses in these Asian economies. Similar to Mian, Sufi and Verner (2017), we find that output in Asian economies increases in the short run but decreases in the long run in response to a positive credit supply shock. The estimated effects are comparable to the responses to domestic credit expansion.

With the progress of financial technology (FinTech), real-time information from FinTech is huge and complicated. For various fields of research, identifying intrinsic features of complex data is important, not limited to financial big data. Reviewing previous studies, there are no suitable methods to deal with complex financial data. General methods are traditionally developed from statistics and machine learning. They are usually in some shallow model forms, which cannot fully represent complex, compositional, and hierarchical financial data features. Due to above drawbacks, this study tries to address the problem by advanced deep learning (DL) methods. In DL more layers will increase the power for abstract data representation. Recently, DL has achieved state-of-the-art performance in a wide range of tasks including speech, image, and vision. DL is effective in learning increasingly more abstract representations in a layer-wise manner. That just meets the characteristic of financial data. This study applies newly developed DL networks, the deep canonically correlation analysis and deep canonically correlated autoencoders to perform FinTech data mining. To test the proposed model, this study employed financial statement data regarding many listed high technology companies in Taiwan stock markets. The computation of deep learning is leveraged by multiple graphics processing unit. Our systems and traditional methods are compared by the same data. Empirical results showed that our systems outperform traditional techniques from statistics and machine learning.

In this study, the relationship between electricity and growth of the economy is investigated by applying the newly-developed bootstrap autoregressive-distributed lag test with a Fourier function to examine both the causality and cointegration for China, India, and the United States (US). While it is not possible to detect a long-term cointegration relation among the economy's electricity and growth, the study findings demonstrate the contingency of the causality. The ensemble method in machine learning performs better than conventional methods as electricity is an independent indicator for forecast economics. Concerning the US, previous electricity consumption has a positive impact on the current nature of economic growth. In contrast, the consumption of electricity is negatively affected by the development of the economy. However, for China and India, positive and negative feedback can be observed, respectively. Due to the increased awareness of the environment's adverse effects, China should promote technologies that conserve energy and boost energy efficiency to achieve sustainable development in both environmental and economic terms. In India's context, broadening access to electricity has significance for residents in rural areas and enhances economic growth. It is recommended that policy-makers promote innovative technologies in the US, as the abundant natural and human resources can make valuable contributions to the society and development of the economy.

Since flooding in urban areas is rarely observed using sensors, most researchers use artificial intelligence (AI) models to predict flood hazards based on model simulation data. However, there is still a gap between simulation and real flooding phenomenon due to the limitation of the model. Few studies have reported on the AI model for flood inundation depth forecasting based on observed data. This study presents a novel method integrating long short-term memory (LSTM) with moving average (MA) for flood inundation depth forecasting based on observed data. A flood-prone intersection in Rende District, Tainan, Taiwan, was adopted as the study area. This investigation compared the forecasting performance of the backpropagation neural network (BPNN), recurrent neural network (RNN) and LSTM models. Accumulated rainfall (Ra) and the moving average (MA) method were applied to enhance the LSTM model performance. The model forecast accuracy was evaluated using root mean square error, coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE). Analytical results indicated that the LSTM had better forecasting ability than the RNN and BPNN, because LSTM had both long-term and short-term memory. Since Ra was an important factor in flooding, adding the Ra to the model input upgraded the LSTM forecasting accuracy for high inundation depths. Because MA reduced the noise of the data, processing the model output using the MA also elevated the forecasting accuracy for high inundation depths. For 3-step-ahead forecasting, the NSE of the model benchmark BPNN was 0.79. Using LSTM, Ra and MA, NSEs gradually increased to 0.83, 0.88 and 0.91, respectively.

In order to estimate water supply potential, the effects of shortages on water users, and the uncertainty of local headspring conditions during the planning stage of reservoir construction, the Shortage Index (SI) is often employed. However, the criterion used in the SI is difficult to adjust to satisfy local conditions and objectives. The SI also employs an ambiguous definition of value. Thus, this study adopted a water supply reliability index (WSRI) as an alternative to the SI for providing the criterion for water resources project planning. The value of the WSRI is easily understood, because it is defined according to the real water supply situation and it has a strong linear relationship with values of SI. For any given water supply system, the estimated results derived from this study could serve as an additional remark on different SI values to explain the relevant water supply considerations. In addition, for a new planning site, the estimated results of this study could provide another way for engineers to evaluate the maximum water supply capability. Consequently, an interesting avenue of investigation in future research would be the incorporation of the WSRI with the risk of deficit frequency in establishing an efficient and transparent bottom-up approach for water resources management, involving all the relevant stakeholders.

Accurate hourly real-time flood forecasting is necessary for early flood warning systems, especially during typhoon periods. Artificial intelligence methods have been increasingly used for real-time flood forecasting. This study developed a real-time flood forecasting model by using back-propagation networks (BPNs) with a self-organizing map (SOM) to create ensemble forecasts. Random weights and biases were set for the BPNs to learn the characteristics of a catchment system. An unsupervised SOM network with a classification function was then used to cluster representative BPN weights and biases; clusters of BPNs with high accuracy were selected to act as experts for the ensemble models to forecast flow rates. The model was applied to flood events in the Wu River Basin of Taiwan. Most observed values were within the forecasting intervals of the BPN clusters in the calibration and validation phases, indicating that the models had acceptable accuracy. For the large flood events of typhoons Saola in the calibration phase and Soulik in the validation phase, the mean average error of the ensemble mean model for the cluster A was 143.1 and 327.4 m3/s, respectively; these values were lower than those for the best individual model within the cluster (194.3 and 917.9 m3/s). The ensemble model thus outperformed the individual models and can accurately forecast flood values and intervals. Therefore, the model can be used to accurately forecast floods.

This study applied fuzzy time series (FTS) analysis to develop a real-time flood forecasting model to forecast typhoon flood discharges. Two crucial factors that influence the performance of FTS are the partition of intervals of the variable and the defuzzification method. This study examined the effects of various interval lengths and two defuzzification methods, the centroid and the midpoint methods, on the model performance. Criteria of model completeness and consistency principle were used to determine the effective interval length, and analytic results showed that the midpoint method outperforms the centroid method. Two structures of forecasting models were proposed to make multiple-hour-ahead flood forecasts. Validation results from typhoon flood events in the Wu River in Taiwan showed that the proposed FTS model, which is novel in hydrologic forecasting, can effectively forecast flood discharges.

This study proposed a hybrid neural network model that combines a self-organizing map (SOM) and back-propagation neural networks (BPNNs) to model the rainfall-runoff process in a physically interpretable manner and to accurately forecast typhoon floods. The SOM and a two-stage clustering scheme were applied to group hydrologic data into four clusters, each of which represented a meaningful hydrologic component of the rainfall-runoff process. BPNNs were constructed for each cluster to achieve high forecasting capability. The physical hybrid neural network model was used to forecast typhoon flood discharges in Wu River in Taiwan by using two types of rainfall data. The clustering results demonstrated that the rainfall-runoff process was favorably described by the sequence of derived clusters. The flood forecasting results indicated that the proposed hybrid neural network model has good forecasting capability, and the performance of the models using the two types of rainfall data is similar. In addition, the derived lagged inputs are hydrologically meaningful, and the number and activation function of the hidden nodes can be rationally interpreted. This study also developed a traditional, single BPNN model trained using the whole calibration data for comparison with the hybrid neural network model. The proposed physical hybrid neural network model outperformed the traditional neural network model in forecasting the peak discharges and low flows.