a new machine learning model for predicting suspended sediment load

Predicting suspended sediment load is important for water resource management, water quality protection, erosion control, infrastructure planning, flood management, ecological conservation, pollution control, and environmental impact assessments. as a novel aspect of this study, the anfis-m5t model is introduced and used to predict suspended sediment loads. our study combines the adaptive neuro-fuzzy inference system (anfis) and the m5t model to create a hybrid model for predicting suspended sediment load (ssl). the lagged rainfall, discharge, and ssl values were used to predict ssl. the results showed that the introduced anfis-m5t model performs better than other models so that it had the lowest mean absolute error (mae: 525), the highest nash sutcliffe efficiency (0.98) and the lowest percent bias (4).the anfis model had the second lowest mae of 576, followed by mlp (586), rfn (682), and m5t (981). thus the anfis-m5t was a reliable tool for predicting ssl. by tackling the obstacles, assessing various methods, and showcasing the anfis-m5t model’s efficiency, our research contributes to the continuous improvement and advancement of sediment measurement techniques and tools.by carrying pollutants, nutrients, and heavy metals, suspended sediments can significantly impact water quality (salih et al., 2020). a high sediment load can cause problems for dams, bridges, and water treatment facilities. during flood events, sediment loads often increase dramatically. the sediment load can also be a good indicator of soil erosion in the watershed. suspended sediments can affect water clarity and quality, which is crucial for both aquatic life and human uses of water, such as drinking and recreation (gupta et al., 2021). predicting sediment loads can contribute to flood risk assessment and management strategies .accurately forecasting sediment load is crucial and significant for effective water resource management, as it aids in understanding and quantifying sediment transport in rivers, reservoirs, and other water bodies. the anfis-m5t model excels in predicting suspended load, which is essential for planning and optimizing reservoir operations. by leveraging these predictions, water resource managers can determine suitable sediment management practices, such as sediment flushing or periodic dredging, to ensure efficient and sustainable reservoir operations.the anfis-m5t model offers reliable predictions that help evaluate the potential impacts of projects on water quality, aquatic habitats, and downstream ecosystems. it enables water resource managers to identify areas with high sediment loads and implement targeted erosion control measures. additionally, the model contributes to environmental impact assessments by providing accurate predictions of suspended sediment load, supporting the evaluation of potential impacts on sensitive ecosystems and aiding in the development of appropriate mitigation strategies. our study uses the anfis-m5t model to predict suspended sediment load in a reservoir basin. the limitations of the methodologies present in such studies include restrictions in the accuracy of experimental models, as well as limitations in other machine learning methods like mt5, which are explained in the introduction of the paper. regarding the limitations of the combined approach introduced in this research, one can point to constraints in optimizing the model structure, training time, and some complexities during implementation. however, considering that advanced computer systems are available today, these limitations can be addressed.in summary, based on the above literature, considering the limitations mentioned about the m5t model, a new model that does not have these issues and limitations will be very helpful. in fact, the main goal of this study is to overcome the limitations of the m5t model and predict ssl data close to reality. the novelty of this study is the introduction of a new hybrid model for the mentioned purpose, which we called anfis-m5t.

a new machine learning model for predicting suspended sediment load

predicting suspended sediment load is important for water resource management, water quality protection, erosion control, infrastructure planning, flood management, ecological conservation, pollution control, and environmental impact assessments. as a novel aspect of this study, the anfis-m5t model is introduced and used to predict suspended sediment loads. our study combines the adaptive neuro-fuzzy inference system (anfis) and the m5t model to create a hybrid model for predicting suspended sediment load (ssl). the lagged rainfall, discharge, and ssl values were used to predict ssl. the results showed that the introduced anfis-m5t model performs better than other models so that it had the lowest mean absolute error (mae: 525), the highest nash sutcliffe efficiency (0.98) and the lowest percent bias (4).the anfis model had the second lowest mae of 576, followed by mlp (586), rfn (682), and m5t (981). thus the anfis-m5t was a reliable tool for predicting ssl. by tackling the obstacles, assessing various methods, and showcasing the anfis-m5t model’s efficiency, our research contributes to the continuous improvement and advancement of sediment measurement techniques and tools.by carrying pollutants, nutrients, and heavy metals, suspended sediments can significantly impact water quality (salih et al., 2020). a high sediment load can cause problems for dams, bridges, and water treatment facilities. during flood events, sediment loads often increase dramatically. the sediment load can also be a good indicator of soil erosion in the watershed. suspended sediments can affect water clarity and quality, which is crucial for both aquatic life and human uses of water, such as drinking and recreation (gupta et al., 2021). predicting sediment loads can contribute to flood risk assessment and management strategies .accurately forecasting sediment load is crucial and significant for effective water resource management, as it aids in understanding and quantifying sediment transport in rivers, reservoirs, and other water bodies. the anfis-m5t model excels in predicting suspended load, which is essential for planning and optimizing reservoir operations. by leveraging these predictions, water resource managers can determine suitable sediment management practices, such as sediment flushing or periodic dredging, to ensure efficient and sustainable reservoir operations.the anfis-m5t model offers reliable predictions that help evaluate the potential impacts of projects on water quality, aquatic habitats, and downstream ecosystems. it enables water resource managers to identify areas with high sediment loads and implement targeted erosion control measures. additionally, the model contributes to environmental impact assessments by providing accurate predictions of suspended sediment load, supporting the evaluation of potential impacts on sensitive ecosystems and aiding in the development of appropriate mitigation strategies. our study uses the anfis-m5t model to predict suspended sediment load in a reservoir basin. the limitations of the methodologies present in such studies include restrictions in the accuracy of experimental models, as well as limitations in other machine learning methods like mt5, which are explained in the introduction of the paper. regarding the limitations of the combined approach introduced in this research, one can point to constraints in optimizing the model structure, training time, and some complexities during implementation. however, considering that advanced computer systems are available today, these limitations can be addressed.in summary, based on the above literature, considering the limitations mentioned about the m5t model, a new model that does not have these issues and limitations will be very helpful. in fact, the main goal of this study is to overcome the limitations of the m5t model and predict ssl data close to reality. the novelty of this study is the introduction of a new hybrid model for the mentioned purpose, which we called anfis-m5t.