A Hybrid Fuzzy Wavelet Neural Network Model with Self-Adapted Fuzzy <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2724395pt" id="M1" height="8.14084pt" version="1.1" viewBox="-0.0657574 -7.8684 7.1246 8.14084" width="7.1246pt"><g transform="matrix(.017,0,0,-0.017,0,0)"><path id="g113-100" d="M383 397C383 424 344 448 299 448C244 448 172 409 132 375C66 319 23 227 23 146C23 42 74 -12 146 -12C208 -12 298 30 359 103L343 124C315 95 248 48 192 48C145 48 111 85 111 163C111 228 129 294 151 330C171 363 201 401 241 401C275 401 302 384 325 356C332 347 339 344 348 348C373 360 383 381 383 397Z"/></g></svg>-</span>Means Clustering and Genetic Algorithm for Water Quality Prediction in Rivers

<div>FWNN performance of the estimation of water quality for COD.</div>

Complexity

fig5

Figure 5

Figure 5: A Hybrid Fuzzy Wavelet Neural Network Model with Self-Adapted Fuzzy <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2724395pt" id="M1" height="8.14084pt" version="1.1" viewBox="-0.0657574 -7.8684 7.1246 8.14084" width="7.1246pt"><g transform="matrix(.017,0,0,-0.017,0,0)"><path id="g113-100" d="M383 397C383 424 344 448 299 448C244 448 172 409 132 375C66 319 23 227 23 146C23 42 74 -12 146 -12C208 -12 298 30 359 103L343 124C315 95 248 48 192 48C145 48 111 85 111 163C111 228 129 294 151 330C171 363 201 401 241 401C275 401 302 384 325 356C332 347 339 344 348 348C373 360 383 381 383 397Z"/></g></svg>-</span>Means Clustering and Genetic Algorithm for Water Quality Prediction in Rivers 

Figure 5 | A Hybrid Fuzzy Wavelet Neural Network Model with Self-Adapted Fuzzy -Means Clustering and Genetic Algorithm for Water Quality Prediction in Rivers 