3 research outputs found
Multi-influence factor prediction for water bloom based on multi-sensor system
This paper proposes a new multi-influence factors
prediction method for water bloom prediction
based on a remote monitor system and multi-sensor
data taking into account the integrated effect of
multiple influential factors along with the
periodicity and random effect of environmental
variables. Valid and accurate water-bloom
prediction can be obtained by combining various
multidimensional time series methods. Comparing
the proposed model based on multi-sensors data to
a traditional one-dimensional time series model
based on one-sensor data, it has been found that a
multidimensional model is a useful and accurate
model for establishing multiple influential factors
time series of water bloom. The optimum model can
be used not only to predict water bloom but also to
determine the period and random change rule of
multiple influential factors
Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management
The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management