[HTML][HTML] A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting
Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has
further generated immense interest in researching aspects for further improvements to …
further generated immense interest in researching aspects for further improvements to …
Artificial intelligence based models for stream-flow forecasting: 2000–2015
Summary The use of Artificial Intelligence (AI) has increased since the middle of the 20th
century as seen in its application in a wide range of engineering and science problems. The …
century as seen in its application in a wide range of engineering and science problems. The …
[HTML][HTML] Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia
WM Ridwan, M Sapitang, A Aziz, KF Kushiar… - Ain Shams Engineering …, 2021 - Elsevier
Rainfall plays a main role in managing the water level in the reservoir. The unpredictable
amount of rainfall due to the climate change can cause either overflow or dry in the reservoir …
amount of rainfall due to the climate change can cause either overflow or dry in the reservoir …
Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm
Reservoirs and dams are vital human-built infrastructures that play essential roles in flood
control, hydroelectric power generation, water supply, navigation, and other functions. The …
control, hydroelectric power generation, water supply, navigation, and other functions. The …
Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility
The main objective of the present study was to produce a novel ensemble data mining
technique that involves an adaptive neuro-fuzzy inference system (ANFIS) optimized by …
technique that involves an adaptive neuro-fuzzy inference system (ANFIS) optimized by …
Support vector machine applications in the field of hydrology: a review
PC Deka - Applied soft computing, 2014 - Elsevier
In the recent few decades there has been very significant developments in the theoretical
understanding of Support vector machines (SVMs) as well as algorithmic strategies for …
understanding of Support vector machines (SVMs) as well as algorithmic strategies for …
Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support …
The spatial prediction of landslide susceptibility is an important prerequisite for the analysis
of landslide hazards and risks in any area. This research uses three data mining techniques …
of landslide hazards and risks in any area. This research uses three data mining techniques …
Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model
The present study proposes a new hybrid evolutionary Adaptive Neuro-Fuzzy Inference
Systems (ANFIS) approach for monthly streamflow forecasting. The proposed method is a …
Systems (ANFIS) approach for monthly streamflow forecasting. The proposed method is a …
Combining statistical machine learning models with ARIMA for water level forecasting: The case of the Red river
XH Nguyen - Advances in Water Resources, 2020 - Elsevier
Forecasting water level is an extremely important task as it allows to mitigate the effects of
floods, reduce and prevent disasters. Physically based models often give good results but …
floods, reduce and prevent disasters. Physically based models often give good results but …
Application of artificial intelligence (AI) techniques in water quality index prediction: a case study in tropical region, Malaysia
The management of river water quality is one the most significant environmental challenges.
Water quality index (WQI) describes several water quality variables at a certain aquatic …
Water quality index (WQI) describes several water quality variables at a certain aquatic …