Coupled Heuristic Prediction of Long Lead-Time  Accumulated Total Inflow of a Reservoir during Typhoons  Using Deterministic Recurrent and Fuzzy Inference-Based Neural Network

Huang, Chien‐Lin; Wei, Chih-Chiang

doi:10.3390/w7116516

Cited by 6 publications

(4 citation statements)

References 38 publications

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“…Shihmen Reservoir has an approximate volume of 203,150,000 m 3 and flow rate of 800,000 m 3 /day. The annual average rainfall is 2350 mm [17], of which 80% occurs between May and October, due to typhoon precipitation.…”

Section: Characteristics Of Reservoirsmentioning

confidence: 99%

Influencing Factors Analysis of Taiwan Eutrophicated Reservoirs

Putri

Lin

Hsieh

et al. 2020

Water

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Treatment cost and quality of domestic water are highly correlated with raw water quality in reservoirs. This study aims to identify the key factors that influence the trophic state levels and correlations among Carlson trophic state index (CTSI) levels, water quality parameters and weather factors in four major reservoirs in Taiwan from 2000 to 2017. Weather (e.g., air temperature, relative humidity, total precipitation, sunlight percentage and cloud cover) and water quality parameters (e.g., pH, chemical oxygen demand, suspended solids (SS), ammonia, total hardness, nitrate, nitrite and water temperature) were included in the principal component analysis and absolute principal component score models to evaluate the main governing factors of the trophic state levels (e.g., CTSI). SS were washed out by precipitation, thereby influencing the reservoir transparency tremendously and contributing over 50% to the CTSI level in eutrophicated reservoirs (e.g., the Shihmen and Chengchinghu Reservoirs). CTSI levels in the mesotrophic reservoir (e.g., Liyutan Reservoir) had strong correlation with chlorophyll-a and total phosphorus. Results show that rainfall/weather factors were the key driving factors that affected the CTSI levels in Taiwan eutrophicated reservoirs, indicating the need to consider basin management and the impacts of extreme precipitation in reservoir management and future policymaking.

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Section: Characteristics Of Reservoirsmentioning

confidence: 99%

Influencing Factors Analysis of Taiwan Eutrophicated Reservoirs

Putri

Lin

Hsieh

et al. 2020

Water

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“…It is also the third-largest reservoir in Taiwan. Typhoons bring the majority of the annual rainfall of 2350 mm to the Shihmen Reservoir watershed between May and October [24].…”

Section: Methodsmentioning

confidence: 99%

DEM- and GIS-Based Analysis of Soil Erosion Depth Using Machine Learning

Nguyen

Chen

2021

IJGI

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Soil erosion is a form of land degradation. It is the process of moving surface soil with the action of external forces such as wind or water. Tillage also causes soil erosion. As outlined by the United Nations Sustainable Development Goal (UN SDG) #15, it is a global challenge to “combat desertification, and halt and reverse land degradation and halt biodiversity loss.” In order to advance this goal, we studied and modeled the soil erosion depth of a typical watershed in Taiwan using 26 morphometric factors derived from a digital elevation model (DEM) and 10 environmental factors. Feature selection was performed using the Boruta algorithm to determine 15 factors with confirmed importance and one tentative factor. Then, machine learning models, including the random forest (RF) and gradient boosting machine (GBM), were used to create prediction models validated by erosion pin measurements. The results show that GBM, coupled with 15 important factors (confirmed), achieved the best result in the context of root mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE). Finally, we present the maps of soil erosion depth using the two machine learning models. The maps are useful for conservation planning and mitigating future soil erosion.

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“…In real-time discharge forecasting, particularly during typhoon attacks, the difficulties mostly encountered include high uncertainty and long lead time. Huang et al [16] couple a real-time recurrent learning neural network, an adaptive network-based fuzzy inference system, and some heuristic techniques to address this problem. Heuristic inputs are utilized to enhance the spatial and temporal precision.…”

Section: Contributorsmentioning

confidence: 99%

Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling

2017

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Each year, extreme floods, which appear to be occurring more frequently in recent years (owing to climate change), lead to enormous economic damage and human suffering around the world. It is therefore imperative to be able to accurately predict both the occurrence time and magnitude of peak discharge in advance of an impending flood event. The use of meta-heuristic techniques in rainfall-runoff modeling is a growing field of endeavor in water resources management. These techniques can be used to calibrate data-driven rainfall-runoff models to improve forecasting accuracies. This Special Issue of the journal Water is designed to fill the analytical void by including papers concerning advances in the contemporary use of meta-heuristic techniques in rainfall-runoff modeling. The information and analyses can contribute to the development and implementation of effective hydrological predictions, and thus, of appropriate precautionary measures.

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Coupled Heuristic Prediction of Long Lead-Time Accumulated Total Inflow of a Reservoir during Typhoons Using Deterministic Recurrent and Fuzzy Inference-Based Neural Network

Cited by 6 publications

References 38 publications

Influencing Factors Analysis of Taiwan Eutrophicated Reservoirs

Influencing Factors Analysis of Taiwan Eutrophicated Reservoirs

DEM- and GIS-Based Analysis of Soil Erosion Depth Using Machine Learning

Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling

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