Fuzzy clustering and distributed model for streamflow estimation in ungauged watersheds

Abstract

This paper proposes a regionalization method for streamflow prediction in ungauged watersheds in the 7461 km2 area above the Gharehsoo Hydrometry Station in the Ardabil Province, in the north of Iran. First, the Fuzzy c-means clustering method (FCM) was used to divide 46 gauged (19) and ungauged (27) watersheds into homogenous groups based on a variety of topographical and climatic factors. After identifying the homogenous watersheds, the Soil and Water Assessment Tool (SWAT) was calibrated and validated using data from the gauged watersheds in each group. The calibrated parameters were then tested in another gauged watershed that we considered as a pseudo ungauged watershed in each group. Values of R-Squared and Nash–Sutcliffe efficiency (NSE) were both ≥ 0.70 during the calibration and validation phases; and ≥ 0.80 and ≥ 0.74, respectively, during the testing in the pseudo ungauged watersheds. Based on these metrics, the validated regional models demonstrated a satisfactory result for predicting streamflow in the ungauged watersheds within each group. These models are important for managing stream quantity and quality in the intensive agriculture study area.

Application of a New Hybrid Machine Learning (Fuzzy-PSO) for Detection of Breast's Tumor

“…Figure 4 shows the shape of the membership functions of the fuzzy Sogno-particle swarm system after the parameter setting process. Similar cases, yet in other applications are provided in e.g., [45][46][47][48][49][50] Figure 5 shows the best and average implementation of the particle swarm algorithm to optimize the parameters of the Sogno fuzzy system. In general, in order to predict this important, other artificial intelligence methods can also be used such as those proposed in e.g., [51][52][53][54][55].…”

Application of a New Hybrid Machine Learning (Fuzzy-PSO) for Detection of Breast’s Tumor

“…Figure 4 shows the shape of the membership functions of the fuzzy Sogno-particle swarm system after the parameter setting process. Similar cases, yet in other applications are provided in e.g., [45][46][47][48][49][50] Figure 5 shows the best and average implementation of the particle swarm algorithm to optimize the parameters of the Sogno fuzzy system. In general, in order to predict this important, other artificial intelligence methods can also be used such as those proposed in e.g., [51][52][53][54][55].…”

Investigating the predictive contribution of attitude towards life and belief system on self-resilience and psychological toughness of cancer patients about the mediating role of emotion regulation

“…Therefore, emotion regulation can be effective on psychological toughness. By Increasing the data points the machine learning methods can be used, e.g., [33][34][35][36][37][38][39][40][41][42][43][44]. Therefore, the study on the perception to life and belief system on self-resilience and psychological toughness of cancer patients about the mediating role of emotion regulation can be better investigated.…”