Hydrological Responses to the Future Climate Change in a Data Scarce Region, Northwest China: Application of Machine Learning Models

Zhu, Rui; Yang, Linshan; Liu, Tao; Wen, Xin; Zhang, Liming; Chang, Yabin

doi:10.3390/w11081588

Cited by 16 publications

(8 citation statements)

References 46 publications

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“…Nevertheless, for the long-term forecast (2060-2090), the average temperature for the RCP8.5 scenario is higher than for the RCP6.0 scenario. The temperature has increased in the near term by a median of 1.1 • C and 1.3 • C for RCP scenarios 6.0 and 8.5, respectively, and projected to increase in the long term by a median of 1.7 • C and 2.9 • C for the RCP scenarios 6.0 and 8.5, respectively [65]. This may be the reason for decreasing optimally suitable habitat in south Guangxi, Jiangsu and Shandong in 2070 under the scenario of RCP 8.5.…”

Section: Discussionmentioning

confidence: 99%

Climate Change May Imperil Tea Production in the Four Major Tea Producers According to Climate Prediction Models

Jayasinghe

Kumar

2020

Agronomy

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The threat of accelerating climate change on species distribution now and in the future is a topic of increasing research interest. However, little work has been undertaken to assess how shifting climates will affect the suitability of tea cultivation. Therefore, we used MaxEnt modelling to project the impact of current and future climatic scenarios on the potential distribution of tea across the four tea-producing countries of China, India, Kenya and Sri Lanka. Projections were made for the years 2050 and 2070 with three Representative Concentration Pathways (RCPs) using seven bioclimatic predictors under three global circulation models (GCMs). The current and future habitat suitability for tea predicted by the models produced a high accuracy rate, with high areas under the receiver operating characteristic curve (AUCs) for all tested RCPs under the three GCMs for the four countries. The mean true skill statistic (TSS) values for tea in Sri Lanka, Kenya, India and China were 0.80, 0.91, 0.91, and 0.74, respectively. The kappa values (k) of the current and future models for all four countries ranged from 0.40 to 0.75, which indicates that the overall performance of the model was good. The precipitation seasonality and annual precipitation were found to be the most influential variables in Sri Lanka and India, respectively, while annual mean temperature was the most effective contributor for determining the suitability of habitat for tea in Kenya and China. An important proviso is that some existing tea-growing areas will face reduced suitability for future tea cultivation suggesting that by 2050 there will be a drastic reduction in the optimal suitability by averages of 26.2%, 14%, and 4.7% in Kenya, Sri Lanka and China, respectively. The optimal suitability will be reduced by 15.1%, 28.6% and 2.6% in Kenya, Sri Lanka and China, respectively, by 2070. India displays an advantage in projected future climates as it gains optimal suitability areas of 15% by 2050 and 25% by 2070.

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Section: Discussionmentioning

confidence: 99%

Climate Change May Imperil Tea Production in the Four Major Tea Producers According to Climate Prediction Models

Jayasinghe

Kumar

2020

Agronomy

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“…Compared to other learning algorithms, such as back propagation (BP), ELM achieves swift learning and performs well in generation function processing [52][53][54][55]. Using ELM in various engineering science fields, such as feature selection [56], classification [57], and regression [51,58], has provided acceptable results.…”

Section: Introductionmentioning

confidence: 99%

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Bonakdari

Moradi

Ebtehaj

et al. 2020

Water

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Abutment scour is a complex three-dimensional phenomenon, which is one of the leading causes of marine structure damage. Structural integrity is potentially attainable through the precise estimation of local scour depth. Due to the high complexity of scouring hydrodynamics, existing regression-based relations cannot make accurate predictions. Therefore, this study presented a novel expansion of extreme learning machines (ELM) to predict abutment scour depth (ds) in clear water conditions. The model was built using the relative flow depth (h/L), excess abutment Froude number (Fe), abutment shape factor (Ks), and relative sediment size (d50/L). A wide range of experimental samples was collected from the literature, and data was utilized to develop the ELM model. The ELM model reliability was evaluated based on the estimation results and several statistical indices. According to the results, the sigmoid activation function (correlation coefficient, R = 0.97; root mean square error, RMSE = 0.162; mean absolute percentage error, MAPE = 7.69; and scatter index, SI = 0.088) performed the best compared with the hard limit, triangular bias, radial basis, and sine activation functions. Eleven input combinations were considered to investigate the impact of each dimensionless variable on the abutment scour depth. It was found that ds/L = f (Fe, h/L, d50/L, Ks) was the best ELM model, indicating that the dimensional analysis of the original data properly reflected the underlying physics of the problem. Also, the absence of one variable from this input combination resulted in a significant accuracy reduction. The results also demonstrated that the proposed ELM model significantly outperformed the regression-based equations derived from the literature. The ELM model presented a fundamental equation for abutment scours depth prediction. Based on the simulation results, it appeared the ELM model could be used effectively in practical engineering applications of predicting abutment scour depth. The estimated uncertainty of the developed ELM model was calculated and compared with the conventional and artificial intelligence-based models. The lowest uncertainty with a value of ±0.026 was found in the proposed model in comparison with ±0.50 as the best uncertainty of the other models.

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“…By applying modern mathematical models, some studies have proposed methods for accurately predicting climate change effects. [24][25][26][27] The optimization Maxent model presented in Li et al, 24 which is based on the method of maximum entropy, is a useful example of kind distribution. However, the range of appropriate variables within an environment, along with type parameters, is an essential factor that must be considered when applying this model.…”

Section: Related Workmentioning

confidence: 99%

“…By applying modern mathematical models, some studies have proposed methods for accurately predicting climate change effects 24‐27 . The optimization Maxent model presented in Li et al, 24 which is based on the method of maximum entropy, is a useful example of kind distribution.…”

Section: Related Workmentioning

confidence: 99%

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Wireless sensor networks and machine learning meet climate change prediction

Khoa

Minh

Son

et al. 2020

Int J Communication

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Summary Climate change is one of the main challenges faced by the development of every country. For countries producing agricultural commodities, the climate affects the quantity and quality of products. Many methods have been proposed to keep track of climate. One traditional method is the weather station model, which indicates the temperature, wind speed, and direction and extent of cloud cover. However, this method of predicting climate change has low accuracy due to geographical variation, for example, mountainous or forested areas. Recently, a combination of wireless sensor networks (WSN) and machine learning (ML) has been considered for prediction with the Internet of Things (IoT), for instance, through a wireless body area network. For climate change prediction, we design and develop a control system that uses node sensors to collect data in sandhills and beaches, with data management conducted via a web application with three components. The first component is designed to collect data from the node sensors. The second component is mainly used to control the system through a web application. The third component uses linear regression in ML to analyze the data to predict weight and volume. The complete system has been tried and tested in real time on a 10‐m2 area of a beach at Binh Thuan province, Vietnam, where sensor node data were wirelessly collected over a cloud using a web application. This enabled assessment of the current state of the land at a coastal sandy beach, as well as prediction of the risk level of desertification and natural disasters.

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Hydrological Responses to the Future Climate Change in a Data Scarce Region, Northwest China: Application of Machine Learning Models

Cited by 16 publications

References 46 publications

Climate Change May Imperil Tea Production in the Four Major Tea Producers According to Climate Prediction Models

Climate Change May Imperil Tea Production in the Four Major Tea Producers According to Climate Prediction Models

A Non-Tuned Machine Learning Technique for Abutment Scour Depth in Clear Water Condition

Wireless sensor networks and machine learning meet climate change prediction

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